Advisories

Concrete5 before 8.5.3 does not constrain the sort direction to a valid asc or desc value.

Liferay Portal, and Liferay DXP before fix pack before fix pack 6, does not restrict the size of a multipart/form-data POST action, which allows remote authenticated users to conduct denial-of-service attacks by uploading large files.

Handlebars before 4.4.5 allows Regular Expression Denial of Service (ReDoS) because of eager matching. The parser may be forced into an endless loop while processing crafted templates. This may allow attackers to exhaust system resources.

In Play Framework 2.6.0 through 2.8.2, stack consumption can occur because of unbounded recursion during parsing of crafted JSON documents.

In Play Framework, stack consumption can occur because of unbounded recursion during parsing of crafted JSON documents.

In Play Framework 2.6.0 through 2.8.2, data amplification can occur when an application accepts multipart/form-data JSON input.

The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.ConcatV2( values=[[1,2,3],[4,5,6]], axis = 0xb500005b) return y test() The axis argument is translated into concat_dim in the ConcatShapeHelper helper function. Then, a value for min_rank is computed based on concat_dim. This is then used to validate …

The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.ConcatV2( values=[[1,2,3],[4,5,6]], axis = 0xb500005b) return y test() The axis argument is translated into concat_dim in the ConcatShapeHelper helper function. Then, a value for min_rank is computed based on concat_dim. This is then used to validate …

The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.ConcatV2( values=[[1,2,3],[4,5,6]], axis = 0xb500005b) return y test() The axis argument is translated into concat_dim in the ConcatShapeHelper helper function. Then, a value for min_rank is computed based on concat_dim. This is then used to validate …

A flaw was found in Hibernate ORM in versions before 5.3.18, 5.4.18 and 5.5.0.Beta1. A SQL injection in the implementation of the JPA Criteria API can permit unsanitized literals when a literal is used in the SELECT or GROUP BY parts of the query. This flaw could allow an attacker to access unauthorized information or possibly conduct further attacks.

In Karaf, JMX authentication takes place using JAAS and authorization takes place using ACL files. By default, only an "admin" can actually invoke on an MBean. However there is a vulnerability there for someone who is not an admin, but has a "viewer" role. In the 'etc/jmx.acl.cfg', such as role can call get*. It's possible to authenticate as a viewer role + invokes on the MLet getMBeansFromURL method, which goes …

Impact The currently selected widget values were not correctly sanitized before passing it to the database, leading to an SQL injection possibility. Patches The issue has been patched in tablelookupwizard For more information If you have any questions or comments about this advisory: Open an issue in https://github.com/terminal42/contao-tablelookupwizard Email us at info@terminal42.ch

The implementation of UnravelIndex is vulnerable to a division by zero caused by an integer overflow bug: import tensorflow as tf tf.raw_ops.UnravelIndex(indices=-0x100000,dims=[0x100000,0x100000])

The implementation of UnravelIndex is vulnerable to a division by zero caused by an integer overflow bug: import tensorflow as tf tf.raw_ops.UnravelIndex(indices=-0x100000,dims=[0x100000,0x100000])

The implementation of UnravelIndex is vulnerable to a division by zero caused by an integer overflow bug: import tensorflow as tf tf.raw_ops.UnravelIndex(indices=-0x100000,dims=[0x100000,0x100000])

An issue was discovered in PlayJava in Play Framework The body parsing of HTTP requests eagerly parses a payload given a Content-Type header. A deep JSON structure sent to a valid POST endpoint (that may or may not expect JSON payloads) causes a StackOverflowError and Denial of Service.

An issue was discovered in PlayJava in Play Framework 2.6.0 through 2.8.2. The body parsing of HTTP requests eagerly parses a payload given a Content-Type header. A deep JSON structure sent to a valid POST endpoint (that may or may not expect JSON payloads) causes a StackOverflowError and Denial of Service.

The implementation of GetInitOp is vulnerable to a crash caused by dereferencing a null pointer: const auto& init_op_sig_it = meta_graph_def.signature_def().find(kSavedModelInitOpSignatureKey); if (init_op_sig_it != sig_def_map.end()) { *init_op_name = init_op_sig_it->second.outputs() .find(kSavedModelInitOpSignatureKey) ->second.name(); return Status::OK(); } Here, we have a nested map and we assume that if the first .find succeeds then so would be the search in the internal map. However, the maps are built based on the SavedModel protobuf format and …

The implementation of GetInitOp is vulnerable to a crash caused by dereferencing a null pointer: const auto& init_op_sig_it = meta_graph_def.signature_def().find(kSavedModelInitOpSignatureKey); if (init_op_sig_it != sig_def_map.end()) { *init_op_name = init_op_sig_it->second.outputs() .find(kSavedModelInitOpSignatureKey) ->second.name(); return Status::OK(); } Here, we have a nested map and we assume that if the first .find succeeds then so would be the search in the internal map. However, the maps are built based on the SavedModel protobuf format and …

The implementation of GetInitOp is vulnerable to a crash caused by dereferencing a null pointer: const auto& init_op_sig_it = meta_graph_def.signature_def().find(kSavedModelInitOpSignatureKey); if (init_op_sig_it != sig_def_map.end()) { *init_op_name = init_op_sig_it->second.outputs() .find(kSavedModelInitOpSignatureKey) ->second.name(); return Status::OK(); } Here, we have a nested map and we assume that if the first .find succeeds then so would be the search in the internal map. However, the maps are built based on the SavedModel protobuf format and …

If Apache TomEE is configured to use the embedded ActiveMQ broker, and the broker URI includes the useJMX=true parameter, a JMX port is opened on TCP port 1099, which does not include authentication. This affects Apache TomEE 8.0.0-M1 - 8.0.1, Apache TomEE 7.1.0 - 7.1.2, Apache TomEE 7.0.0-M1 - 7.0.7, Apache TomEE 1.0.0 - 1.7.5.

Web endpoint authentication check is broken in Apache Hadoop 3.0.0-alpha4, 3.0.0-beta1, and 3.0.0. Authenticated users may impersonate any user even if no proxy user is configured.

If a graph node is invalid, TensorFlow can leak memory in the implementation of ImmutableExecutorState::Initialize: Status s = params_.create_kernel(n->properties(), &item->kernel); if (!s.ok()) { item->kernel = nullptr; s = AttachDef(s, n); return s; } Here, we set item->kernel to nullptr but it is a simple OpKernel pointer so the memory that was previously allocated to it would leak.

If a graph node is invalid, TensorFlow can leak memory in the implementation of ImmutableExecutorState::Initialize: Status s = params_.create_kernel(n->properties(), &item->kernel); if (!s.ok()) { item->kernel = nullptr; s = AttachDef(s, n); return s; } Here, we set item->kernel to nullptr but it is a simple OpKernel pointer so the memory that was previously allocated to it would leak.

If a graph node is invalid, TensorFlow can leak memory in the implementation of ImmutableExecutorState::Initialize: Status s = params_.create_kernel(n->properties(), &item->kernel); if (!s.ok()) { item->kernel = nullptr; s = AttachDef(s, n); return s; } Here, we set item->kernel to nullptr but it is a simple OpKernel pointer so the memory that was previously allocated to it would leak.

The implementation of StringNGrams can be used to trigger a denial of service attack by causing an OOM condition after an integer overflow: import tensorflow as tf tf.raw_ops.StringNGrams( data=['123456'], data_splits=[0,1], separator='a'*15, ngram_widths=[], left_pad='', right_pad='', pad_width=-5, preserve_short_sequences=True) We are missing a validation on pad_witdh and that result in computing a negative value for ngram_width which is later used to allocate parts of the output.

The implementation of StringNGrams can be used to trigger a denial of service attack by causing an OOM condition after an integer overflow: import tensorflow as tf tf.raw_ops.StringNGrams( data=['123456'], data_splits=[0,1], separator='a'*15, ngram_widths=[], left_pad='', right_pad='', pad_width=-5, preserve_short_sequences=True) We are missing a validation on pad_witdh and that result in computing a negative value for ngram_width which is later used to allocate parts of the output.

The implementation of ThreadPoolHandle can be used to trigger a denial of service attack by allocating too much memory: import tensorflow as tf y = tf.raw_ops.ThreadPoolHandle(num_threads=0x60000000,display_name='tf') This is because the num_threads argument is only checked to not be negative, but there is no upper bound on its value.

The implementation of ThreadPoolHandle can be used to trigger a denial of service attack by allocating too much memory: import tensorflow as tf y = tf.raw_ops.ThreadPoolHandle(num_threads=0x60000000,display_name='tf') This is because the num_threads argument is only checked to not be negative, but there is no upper bound on its value.

The implementation of StringNGrams can be used to trigger a denial of service attack by causing an OOM condition after an integer overflow: import tensorflow as tf tf.raw_ops.StringNGrams( data=['123456'], data_splits=[0,1], separator='a'*15, ngram_widths=[], left_pad='', right_pad='', pad_width=-5, preserve_short_sequences=True) We are missing a validation on pad_witdh and that result in computing a negative value for ngram_width which is later used to allocate parts of the output.

The implementation of ThreadPoolHandle can be used to trigger a denial of service attack by allocating too much memory: import tensorflow as tf y = tf.raw_ops.ThreadPoolHandle(num_threads=0x60000000,display_name='tf') This is because the num_threads argument is only checked to not be negative, but there is no upper bound on its value.

The implementation of OpLevelCostEstimator::CalculateTensorSize is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements: int64_t OpLevelCostEstimator::CalculateTensorSize( const OpInfo::TensorProperties& tensor, bool* found_unknown_shapes) { int64_t count = CalculateTensorElementCount(tensor, found_unknown_shapes); int size = DataTypeSize(BaseType(tensor.dtype())); VLOG(2) << "Count: " << count << " DataTypeSize: " << size; return count * size; } Here, count and size can be large enough …

The implementation of OpLevelCostEstimator::CalculateOutputSize is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements: for (const auto& dim : output_shape.dim()) { output_size *= dim.size(); } Here, we can have a large enough number of dimensions in output_shape.dim() or just a small number of dimensions being large enough to cause an overflow in the multiplication.

The implementation of OpLevelCostEstimator::CalculateOutputSize is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements: for (const auto& dim : output_shape.dim()) { output_size *= dim.size(); } Here, we can have a large enough number of dimensions in output_shape.dim() or just a small number of dimensions being large enough to cause an overflow in the multiplication.

The implementation of OpLevelCostEstimator::CalculateOutputSize is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements: for (const auto& dim : output_shape.dim()) { output_size *= dim.size(); } Here, we can have a large enough number of dimensions in output_shape.dim() or just a small number of dimensions being large enough to cause an overflow in the multiplication.

The implementation of OpLevelCostEstimator::CalculateTensorSize is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements: int64_t OpLevelCostEstimator::CalculateTensorSize( const OpInfo::TensorProperties& tensor, bool* found_unknown_shapes) { int64_t count = CalculateTensorElementCount(tensor, found_unknown_shapes); int size = DataTypeSize(BaseType(tensor.dtype())); VLOG(2) << "Count: " << count << " DataTypeSize: " << size; return count * size; } Here, count and size can be large enough …

The implementation of OpLevelCostEstimator::CalculateTensorSize is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements: int64_t OpLevelCostEstimator::CalculateTensorSize( const OpInfo::TensorProperties& tensor, bool* found_unknown_shapes) { int64_t count = CalculateTensorElementCount(tensor, found_unknown_shapes); int size = DataTypeSize(BaseType(tensor.dtype())); VLOG(2) << "Count: " << count << " DataTypeSize: " << size; return count * size; } Here, count and size can be large enough …

The HttpClient from Reactor Netty,, may be used incorrectly, leading to a credentials leak during a redirect to a different domain. In order for this to happen, the HttpClient must have been explicitly configured to follow redirects.

Apache Solr versions 6.6.0 to 6.6.6, 7.0.0 to 7.7.3 and 8.0.0 to 8.6.2 prevents some features considered dangerous (which could be used for remote code execution) to be configured in a ConfigSet that's uploaded via API without authentication/authorization. The checks in place to prevent such features can be circumvented by using a combination of UPLOAD/CREATE actions.

An issue exsits in Gitea through 1.15.7, which could let a malicious user gain privileges due to client side cookies not being deleted and the session remains valid on the server side for reuse.

The package handlebars before 4.7.7 are vulnerable to Prototype Pollution when selecting certain compiling options to compile templates coming from an untrusted source.

In Eclipse Hono the AMQP protocol adapter does not verify the size of AMQP messages received from devices. In particular, a device may send messages that are bigger than the max-message-size that the protocol adapter has indicated during link establishment. While the AMQP protocol explicitly disallows a peer to send such messages, a hand crafted AMQP client could exploit this behavior in order to send a message of unlimited size …

PostgreSQL JDBC Driver (aka PgJDBC) before 42.2.13 allows XXE.

A flaw was found in Hibernate ORM Beta1. A SQL injection in the implementation of the JPA Criteria API can permit unsanitized literals when a literal is used in the SELECT or GROUP BY parts of the query. This flaw could allow an attacker to access unauthorized information or possibly conduct further attacks.

strong-nginx-controller is vulnerable to Command Injection. It allows execution of arbitrary command as part of the '_nginxCmd()' function.

Netflix Titus uses Java Bean Validation (JSR ) custom constraint validators. When building custom constraint violation error messages, different types of interpolation are supported, including Java EL expressions. If an attacker can inject arbitrary data in the error message template being passed to ConstraintValidatorContext.buildConstraintViolationWithTemplate() argument, they will be able to run arbitrary Java code.

The gnuplot package for Node.js allows code execution via shell metacharacters in Gnuplot commands.

we got reports for 2 injection attacks against the DeltaSpike windowhandler.js. This is only active if a developer selected the ClientSideWindowStrategy which is not the default.

XWiki is a generic wiki platform offering runtime services for applications built on top of it. When using default XWiki configuration, it's possible for an attacker to upload an SVG containing a script executed when executing the download action on the file. This problem has been patched so that the default configuration does not allow to display the SVG files in the browser. Users are advised to update or to …

Apache Atlas before 2.1.0 contain a XSS vulnerability. While saving search or rendering elements values are not sanitized correctly and because of that it triggers the XSS vulnerability.

RosarioSIS through 6.8-beta allows modules/Custom/NotifyParents.php XSS because of the href attributes for AddStudents.php and User.php.

The HTMLSanitizer class in html-sanitizer.ts in all released versions of the Aurelia framework repository is vulnerable to XSS. The sanitizer only attempts to filter SCRIPT elements, which makes it feasible for remote attackers to conduct XSS attacks via (for example) JavaScript code in an attribute of various other elements. An attacker might also exploit a bug in how the SCRIPT string is processed by splitting and nesting them for example.

Froala Editor before 3.2.3 allows XSS.

XWiki is a generic wiki platform offering runtime services for applications built on top of it. When using default XWiki configuration, it's possible for an attacker to upload an SVG containing a script executed when executing the download action on the file. This problem has been patched so that the default configuration doesn't allow to display the SVG files in the browser. Users are advised to update or to disallow …

The hyperlinks functionality in atlaskit/editor-core in allows remote attackers to inject arbitrary HTML or JavaScript via a Cross-Site Scripting (XSS) vulnerability in link targets.

Cross-site Scripting (XSS) - Stored in Packagist ptrofimov/beanstalk_console prior to 1.7.14.

Persistent Cross-site scripting vulnerability on Fork CMS version 5.8.2 allows remote attackers to inject arbitrary Javascript code via the "navigation_title" parameter and the "title" parameter in /private/en/pages/add.

Contao before 4.5.7 has XSS in the system log.

In Eclipse Vert.x 3.4.x up to 3.9.4, 4.0.0.milestone1, 4.0.0.milestone2, 4.0.0.milestone3, 4.0.0.milestone4, 4.0.0.milestone5, 4.0.0.Beta1, 4.0.0.Beta2, and 4.0.0.Beta3, StaticHandler doesn't correctly processes back slashes on Windows Operating systems, allowing, escape the webroot folder to the current working directory.

A directory traversal issue in the Utils/Unzip module in Microweber through 1.1.20 allows an authenticated attacker to gain remote code execution via the backup restore feature. To exploit the vulnerability, an attacker must have the credentials of an administrative user, upload a maliciously constructed ZIP file with file paths including relative paths (i.e., ../../), move this file into the backup directory, and execute a restore on this file.

Reported in SOLR-14515 (private) and fixed in SOLR-14561 (public), released in Solr version 8.6.0. The Replication handler allows commands backup, restore and deleteBackup. Each of these take a location parameter, which was not validated, i.e you could read/write to any location the solr user can access.

Reactor Netty HttpServer, is exposed to a URISyntaxException that causes the connection to be closed prematurely instead of producing a response.

Handlebars before 3.0.8 and 4.x before 4.5.3 is vulnerable to Arbitrary Code Execution. The lookup helper fails to properly validate templates, allowing attackers to submit templates that execute arbitrary JavaScript. This can be used to run arbitrary code on a server processing Handlebars templates or in a victim's browser (effectively serving as XSS).

When TLS is enabled with ssl-endpoint-identification-enabled set to true, Apache Geode fails to perform hostname verification of the entries in the certificate SAN during the SSL handshake. This could compromise intra-cluster communication using a man-in-the-middle attack.

Graylog lacks SSL Certificate Validation for LDAP servers. It allows use of an external user/group database stored in LDAP. The connection configuration allows the usage of unencrypted, SSL- or TLS-secured connections. Unfortunately, the Graylog client code (in all versions that support LDAP) does not implement proper certificate validation (regardless of whether the "Allow self-signed certificates" option is used). Therefore, any attacker with the ability to intercept network traffic between a …

In Apache Spark 2.4.5 and earlier, a standalone resource manager's master may be configured to require authentication (spark.authenticate) via a shared secret. When enabled, however, a specially-crafted RPC to the master can succeed in starting an application's resources on the Spark cluster, even without the shared key. This can be leveraged to execute shell commands on the host machine. This does not affect Spark clusters using other resource managers (YARN, …

In Apache Spark 2.4.5 and earlier, a standalone resource manager's master may be configured to require authentication (spark.authenticate) via a shared secret. When enabled, however, a specially-crafted RPC to the master can succeed in starting an application's resources on the Spark cluster, even without the shared key. This can be leveraged to execute shell commands on the host machine. This does not affect Spark clusters using other resource managers (YARN, …

An Authentication Bypass vulnerability exists in Gitea before 1.5.0, which could let a malicious user gain privileges. If captured, the TOTP code for the 2FA can be submitted correctly more than once.

This issue has been marked as a false positive.

An issue was discovered in Walmart Labs Concord CORS Access-Control-Allow-Origin headers have a potentially unsafe dependency on Origin headers, and are not configurable. This allows remote attackers to discover host information, nodes, API metadata, and references to usernames via api/v1/apikey.

Apache CXF has the ability to integrate with JMX by registering an InstrumentationManager extension with the CXF bus. If the ‘createMBServerConnectorFactory‘ property of the default InstrumentationManagerImpl is not disabled, then it is vulnerable to a man-in-the-middle (MITM) style attack. An attacker on the same host can connect to the registry and rebind the entry to another server, thus acting as a proxy to the original. They are then able to …

Express-handlebars is a Handlebars view engine for Express. Express-handlebars mixes pure template data with engine configuration options through the Express render API. More specifically, the layout parameter may trigger file disclosure vulnerabilities in downstream applications. This potential vulnerability is somewhat restricted in that only files with existing extentions (i.e. file.extension) can be included, files that lack an extension will have .handlebars appended to them. For complete details refer to the …

The estimator for the cost of some convolution operations can be made to execute a division by 0: import tensorflow as tf @tf.function def test(): y=tf.raw_ops.AvgPoolGrad( orig_input_shape=[1,1,1,1], grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]], ksize=[1,1,1,1], strides=[1,1,1,0], padding='VALID', data_format='NCHW') return y test() The function fails to check that the stride argument is stricly positive: int64_t GetOutputSize(const int64_t input, const int64_t filter, const int64_t stride, const Padding& padding) { // Logic for calculating output shape is from GetWindowedOutputSizeVerbose() …

The estimator for the cost of some convolution operations can be made to execute a division by 0: import tensorflow as tf @tf.function def test(): y=tf.raw_ops.AvgPoolGrad( orig_input_shape=[1,1,1,1], grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]], ksize=[1,1,1,1], strides=[1,1,1,0], padding='VALID', data_format='NCHW') return y test() The function fails to check that the stride argument is stricly positive: int64_t GetOutputSize(const int64_t input, const int64_t filter, const int64_t stride, const Padding& padding) { // Logic for calculating output shape is from GetWindowedOutputSizeVerbose() …

The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0: import tensorflow as tf import numpy as np tf.raw_ops.FractionalMaxPool( value=tf.constant(value=[[[[1, 4, 2, 3]]]], dtype=tf.int64), pooling_ratio=[1.0, 1.44, 1.73, 1.0], pseudo_random=False, overlapping=False, deterministic=False, seed=0, seed2=0, name=None)

The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0: import tensorflow as tf import numpy as np tf.raw_ops.FractionalMaxPool( value=tf.constant(value=[[[[1, 4, 2, 3]]]], dtype=tf.int64), pooling_ratio=[1.0, 1.44, 1.73, 1.0], pseudo_random=False, overlapping=False, deterministic=False, seed=0, seed2=0, name=None)

The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0: import tensorflow as tf import numpy as np tf.raw_ops.FractionalMaxPool( value=tf.constant(value=[[[[1, 4, 2, 3]]]], dtype=tf.int64), pooling_ratio=[1.0, 1.44, 1.73, 1.0], pseudo_random=False, overlapping=False, deterministic=False, seed=0, seed2=0, name=None)

The estimator for the cost of some convolution operations can be made to execute a division by 0: import tensorflow as tf @tf.function def test(): y=tf.raw_ops.AvgPoolGrad( orig_input_shape=[1,1,1,1], grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]], ksize=[1,1,1,1], strides=[1,1,1,0], padding='VALID', data_format='NCHW') return y test() The function fails to check that the stride argument is stricly positive: int64_t GetOutputSize(const int64_t input, const int64_t filter, const int64_t stride, const Padding& padding) { // Logic for calculating output shape is from GetWindowedOutputSizeVerbose() …

A deserialization flaw is present in Taoensso Nippy In some circumstances, it is possible for an attacker to create a malicious payload that, when deserialized, will allow arbitrary code to be executed. This occurs because there is automatic use of the Java Serializable interface.

This vulnerability can affect all Dubbo users stay on or lower. An attacker can send RPC requests with unrecognized service name or method name along with some malicious parameter payloads. When the malicious parameter is deserialized, it will execute some malicious code. More details can be found below.

Jodd performs Deserialization of Untrusted JSON Data when setClassMetadataName is set.

Attackers can use public NetTest web service of Apache OpenMeetings 4.0.0-5.0.0 to organize denial of service attack.

It's possible to know if a user has or not an account in a wiki related to an email address, and which username(s) is actually tied to that email by forging a request to the Forgot username page.

A Remote Code Execution (RCE) vulnerability exists in ThinkPHP 3.x.x via value[_filename] in index.php, which could let a malicious user obtain server control privileges.

The implementation of MapStage is vulnerable a CHECK-fail if the key tensor is not a scalar: import tensorflow as tf import numpy as np tf.raw_ops.MapStage( key = tf.constant(value=[4], shape= (1,2), dtype=tf.int64), indices = np.array([[6]]), values = np.array([-60]), dtypes = [tf.int64], capacity=0, memory_limit=0, container='', shared_name='', name=None )

The implementation of MapStage is vulnerable a CHECK-fail if the key tensor is not a scalar: import tensorflow as tf import numpy as np tf.raw_ops.MapStage( key = tf.constant(value=[4], shape= (1,2), dtype=tf.int64), indices = np.array([[6]]), values = np.array([-60]), dtypes = [tf.int64], capacity=0, memory_limit=0, container='', shared_name='', name=None )

The implementation of MapStage is vulnerable a CHECK-fail if the key tensor is not a scalar: import tensorflow as tf import numpy as np tf.raw_ops.MapStage( key = tf.constant(value=[4], shape= (1,2), dtype=tf.int64), indices = np.array([[6]]), values = np.array([-60]), dtypes = [tf.int64], capacity=0, memory_limit=0, container='', shared_name='', name=None )

A malicious user can cause a denial of service by altering a SavedModel such that any binary op would trigger CHECK failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the dtype no longer matches the dtype expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved: …

A malicious user can cause a denial of service by altering a SavedModel such that any binary op would trigger CHECK failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the dtype no longer matches the dtype expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved: …

A malicious user can cause a denial of service by altering a SavedModel such that any binary op would trigger CHECK failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the dtype no longer matches the dtype expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved: …

A malicious user can cause a denial of service by altering a SavedModel such that TensorByteSize would trigger CHECK failures. int64_t TensorByteSize(const TensorProto& t) { // num_elements returns -1 if shape is not fully defined. int64_t num_elems = TensorShape(t.tensor_shape()).num_elements(); return num_elems < 0 ? -1 : num_elems * DataTypeSize(t.dtype()); } TensorShape constructor throws a CHECK-fail if shape is partial or has a number of elements that would overflow the size …

A malicious user can cause a denial of service by altering a SavedModel such that TensorByteSize would trigger CHECK failures. int64_t TensorByteSize(const TensorProto& t) { // num_elements returns -1 if shape is not fully defined. int64_t num_elems = TensorShape(t.tensor_shape()).num_elements(); return num_elems < 0 ? -1 : num_elems * DataTypeSize(t.dtype()); } TensorShape constructor throws a CHECK-fail if shape is partial or has a number of elements that would overflow the size …

A malicious user can cause a denial of service by altering a SavedModel such that TensorByteSize would trigger CHECK failures. int64_t TensorByteSize(const TensorProto& t) { // num_elements returns -1 if shape is not fully defined. int64_t num_elems = TensorShape(t.tensor_shape()).num_elements(); return num_elems < 0 ? -1 : num_elems * DataTypeSize(t.dtype()); } TensorShape constructor throws a CHECK-fail if shape is partial or has a number of elements that would overflow the size …

The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a SavedModel such that SafeToRemoveIdentity would trigger CHECK failures.

The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a SavedModel such that SafeToRemoveIdentity would trigger CHECK failures.

The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a SavedModel such that SafeToRemoveIdentity would trigger CHECK failures.

Crafter CMS Crafter Studio 3.0.1 is affected by: XML External Entity (XXE). An unauthenticated attacker is able to create a site with specially crafted XML that allows the retrieval of OS files out-of-band.

Impact What kind of vulnerability is it? Who is impacted? Under certain circumstances a valid user object would have been created with invalid provider metadata. This vulnerability affects everyone running an instance of GoTrue as a service. We advise you to update especially if you are using the provider metadata from the user object to secure other resources. Patches Has the problem been patched? What versions should users upgrade to? …

A malicious user can cause a use after free behavior when decoding PNG images: if (/* … error conditions … */) { png::CommonFreeDecode(&decode); OP_REQUIRES(context, false, errors::InvalidArgument("PNG size too large for int: ", decode.width, " by ", decode.height)); } After png::CommonFreeDecode(&decode) gets called, the values of decode.width and decode.height are in an unspecified state.

A malicious user can cause a use after free behavior when decoding PNG images: if (/* … error conditions … */) { png::CommonFreeDecode(&decode); OP_REQUIRES(context, false, errors::InvalidArgument("PNG size too large for int: ", decode.width, " by ", decode.height)); } After png::CommonFreeDecode(&decode) gets called, the values of decode.width and decode.height are in an unspecified state.

A malicious user can cause a use after free behavior when decoding PNG images: if (/* … error conditions … */) { png::CommonFreeDecode(&decode); OP_REQUIRES(context, false, errors::InvalidArgument("PNG size too large for int: ", decode.width, " by ", decode.height)); } After png::CommonFreeDecode(&decode) gets called, the values of decode.width and decode.height are in an unspecified state.

XWiki Platform is a generic wiki platform offering runtime services for applications built on top of it. In affected versions there is no protection against URL redirection to untrusted sites, in particular some well known parameters (xredirect) can be used to perform url redirections. This problem has been patched in XWiki 12.10.7 and XWiki 13.3RC1. Users are advised to update. There are no known workarounds for this issue.

The implementation of AssignOp can result in copying unitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized.

The implementation of AssignOp can result in copying unitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized.

The implementation of AssignOp can result in copying unitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized.

The implementation of SparseTensorSliceDataset has an undefined behavior: under certain condition it can be made to dereference a nullptr value: import tensorflow as tf import numpy as np tf.raw_ops.SparseTensorSliceDataset( indices=[[]], values=[], dense_shape=[1,1]) The 3 input arguments represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation.

The implementation of SparseTensorSliceDataset has an undefined behavior: under certain condition it can be made to dereference a nullptr value: import tensorflow as tf import numpy as np tf.raw_ops.SparseTensorSliceDataset( indices=[[]], values=[], dense_shape=[1,1]) The 3 input arguments represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation.

The implementation of SparseTensorSliceDataset has an undefined behavior: under certain condition it can be made to dereference a nullptr value: import tensorflow as tf import numpy as np tf.raw_ops.SparseTensorSliceDataset( indices=[[]], values=[], dense_shape=[1,1]) The 3 input arguments represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation.

A specially crafted sequence of HTTP/2 requests sent to Apache Tomcat 10.0.0-M1 to 10.0.0-M5, 9.0.0.M1 to 9.0.35 and 8.5.0 to 8.5.55 could trigger high CPU usage for several seconds. If a sufficient number of such requests were made on concurrent HTTP/2 connections, the server could become unresponsive.

A specially crafted sequence of HTTP/2 requests sent to Apache Tomcat 10.0.0-M1 to 10.0.0-M5, 9.0.0.M1 to 9.0.35 and 8.5.0 to 8.5.55 could trigger high CPU usage for several seconds. If a sufficient number of such requests were made on concurrent HTTP/2 connections, the server could become unresponsive.

The GraphDef format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a GraphDef containing a fragment such as the following can be consumed when loading a SavedModel: library { function { signature { name: "SomeOp" description: "Self recursive op" } node_def { name: "1" op: "SomeOp" } node_def { name: "2" op: "SomeOp" } } } This would result in a …

The GraphDef format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a GraphDef containing a fragment such as the following can be consumed when loading a SavedModel: library { function { signature { name: "SomeOp" description: "Self recursive op" } node_def { name: "1" op: "SomeOp" } node_def { name: "2" op: "SomeOp" } } } This would result in a …

The GraphDef format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a GraphDef containing a fragment such as the following can be consumed when loading a SavedModel: library { function { signature { name: "SomeOp" description: "Self recursive op" } node_def { name: "1" op: "SomeOp" } node_def { name: "2" op: "SomeOp" } } } This would result in a …

In the jsrsasign package through 10.1.13 for Node.js, some invalid RSA PKCS#1 v1.5 signatures are mistakenly recognized to be valid. NOTE: there is no known practical attack.

Server-Side Request Forgery (SSRF) in GitHub repository chocobozzz/peertube prior to f33e515991a32885622b217bf2ed1d1b0d9d6832

In ArangoDB, versions v3.7.0 through v3.9.0-alpha.1 have a feature which allows downloading a Foxx service from a publicly available URL. This feature does not enforce proper filtering of requests performed internally, which can be abused by a highly-privileged attacker to perform blind SSRF and send internal requests to localhost.

Apache Batik is vulnerable to server-side request forgery, caused by improper input validation by the "xlink:href" attributes. By using a specially-crafted argument, an attacker could exploit this vulnerability to cause the underlying server to make arbitrary GET requests.

This advisory has been moved to batik-svgrasterizer.

The simplifyBroadcast function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. size_t maxRank = 0; for (auto shape : llvm::enumerate(shapes)) { auto found_shape = analysis.dimensionsForShapeTensor(shape.value()); if (!found_shape) return {}; shapes_found.push_back(found_shape); maxRank = std::max(maxRank, found_shape->size()); } SmallVector<const ShapeComponentAnalysis::SymbolicDimension> joined_dimensions(maxRank); If all shapes are scalar, then maxRank is 0, so we build an empty SmallVector.

The simplifyBroadcast function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. size_t maxRank = 0; for (auto shape : llvm::enumerate(shapes)) { auto found_shape = analysis.dimensionsForShapeTensor(shape.value()); if (!found_shape) return {}; shapes_found.push_back(found_shape); maxRank = std::max(maxRank, found_shape->size()); } SmallVector<const ShapeComponentAnalysis::SymbolicDimension> joined_dimensions(maxRank); If all shapes are scalar, then maxRank is 0, so we build an empty SmallVector.

The simplifyBroadcast function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. size_t maxRank = 0; for (auto shape : llvm::enumerate(shapes)) { auto found_shape = analysis.dimensionsForShapeTensor(shape.value()); if (!found_shape) return {}; shapes_found.push_back(found_shape); maxRank = std::max(maxRank, found_shape->size()); } SmallVector<const ShapeComponentAnalysis::SymbolicDimension> joined_dimensions(maxRank); If all shapes are scalar, then maxRank is 0, so we build an empty SmallVector.

If Apache TomEE - - - - is configured to use the embedded ActiveMQ broker, and the broker config is misconfigured, a JMX port is opened on TCP port, which does not include authentication. CVE-2020-11969 previously addressed the creation of the JMX management interface, however the incomplete fix does not cover this edge case.

An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors.

An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors.

An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors.

When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a CHECK assertion is invalidated based on user controlled arguments, if the tensors have an invalid dtype and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes.

When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a CHECK assertion is invalidated based on user controlled arguments, if the tensors have an invalid dtype and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes.

When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a CHECK assertion is invalidated based on user controlled arguments, if the tensors have an invalid dtype and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes.

When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a CHECK assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes.

When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a CHECK assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes.

When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a CHECK assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes.

XWiki Platform is a generic wiki platform offering runtime services for applications built on top of it. In affected versions any user with SCRIPT right can save a document with the right of the current user which allow accessing API requiring programming right if the current user has programming right. This has been patched in XWiki 13.0. Users are advised to update to resolve this issue. The only known workaround …

Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect. If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have …

Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect. If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have …

An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.

An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.

An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.

TensorFlow is vulnerable to a heap OOB write in Grappler: Status SetUnknownShape(const NodeDef* node, int output_port) { shape_inference::ShapeHandle shape = GetUnknownOutputShape(node, output_port); InferenceContext* ctx = GetContext(node); if (ctx == nullptr) { return errors::InvalidArgument("Missing context"); } ctx->set_output(output_port, shape); return Status::OK(); } The set_output function writes to an array at the specified index: void set_output(int idx, ShapeHandle shape) { outputs_.at(idx) = shape; } Hence, this gives a malicious user a write primitive.

TensorFlow is vulnerable to a heap OOB write in Grappler: Status SetUnknownShape(const NodeDef* node, int output_port) { shape_inference::ShapeHandle shape = GetUnknownOutputShape(node, output_port); InferenceContext* ctx = GetContext(node); if (ctx == nullptr) { return errors::InvalidArgument("Missing context"); } ctx->set_output(output_port, shape); return Status::OK(); } The set_output function writes to an array at the specified index: void set_output(int idx, ShapeHandle shape) { outputs_.at(idx) = shape; } Hence, this gives a malicious user a write primitive.

TensorFlow is vulnerable to a heap OOB write in Grappler: Status SetUnknownShape(const NodeDef* node, int output_port) { shape_inference::ShapeHandle shape = GetUnknownOutputShape(node, output_port); InferenceContext* ctx = GetContext(node); if (ctx == nullptr) { return errors::InvalidArgument("Missing context"); } ctx->set_output(output_port, shape); return Status::OK(); } The set_output function writes to an array at the specified index: void set_output(int idx, ShapeHandle shape) { outputs_.at(idx) = shape; } Hence, this gives a malicious user a write primitive.

The implementation of FractionalAvgPoolGrad does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.FractionalAvgPoolGrad( orig_input_tensor_shape=[2,2,2,2], out_backprop=[[[[1,2], [3, 4], [5, 6]], [[7, 8], [9,10], [11,12]]]], row_pooling_sequence=[-10,1,2,3], col_pooling_sequence=[1,2,3,4], overlapping=True) return y test()

TensorFlow's type inference can cause a heap OOB read as the bounds checking is done in a DCHECK (which is a no-op during production): if (node_t.type_id() != TFT_UNSET) { int ix = input_idx[i]; DCHECK(ix < node_t.args_size()) << "input " << i << " should have an output " << ix << " but instead only has " << node_t.args_size() << " outputs: " << node_t.DebugString(); input_types.emplace_back(node_t.args(ix)); // … } An …

The implementation of FractionalAvgPoolGrad does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.FractionalAvgPoolGrad( orig_input_tensor_shape=[2,2,2,2], out_backprop=[[[[1,2], [3, 4], [5, 6]], [[7, 8], [9,10], [11,12]]]], row_pooling_sequence=[-10,1,2,3], col_pooling_sequence=[1,2,3,4], overlapping=True) return y test()

The implementation of Dequantize does not fully validate the value of axis and can result in heap OOB accesses: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.Dequantize( input=tf.constant([1,1],dtype=tf.qint32), min_range=[1.0], max_range=[10.0], mode='MIN_COMBINED', narrow_range=False, axis=2**31-1, dtype=tf.bfloat16) return y test() The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound …

TensorFlow's type inference can cause a heap OOB read as the bounds checking is done in a DCHECK (which is a no-op during production): if (node_t.type_id() != TFT_UNSET) { int ix = input_idx[i]; DCHECK(ix < node_t.args_size()) << "input " << i << " should have an output " << ix << " but instead only has " << node_t.args_size() << " outputs: " << node_t.DebugString(); input_types.emplace_back(node_t.args(ix)); // … } An …

The implementation of shape inference for ReverseSequence does not fully validate the value of batch_dim and can result in a heap OOB read: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.ReverseSequence( input = ['aaa','bbb'], seq_lengths = [1,1,1], seq_dim = -10, batch_dim = -10 ) return y test() There is a check to make sure the value of batch_dim does not go over the rank of the input, but …

The implementation of shape inference for ReverseSequence does not fully validate the value of batch_dim and can result in a heap OOB read: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.ReverseSequence( input = ['aaa','bbb'], seq_lengths = [1,1,1], seq_dim = -10, batch_dim = -10 ) return y test() There is a check to make sure the value of batch_dim does not go over the rank of the input, but …

The implementation of Dequantize does not fully validate the value of axis and can result in heap OOB accesses: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.Dequantize( input=tf.constant([1,1],dtype=tf.qint32), min_range=[1.0], max_range=[10.0], mode='MIN_COMBINED', narrow_range=False, axis=2**31-1, dtype=tf.bfloat16) return y test() The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound …

The implementation of Dequantize does not fully validate the value of axis and can result in heap OOB accesses: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.Dequantize( input=tf.constant([1,1],dtype=tf.qint32), min_range=[1.0], max_range=[10.0], mode='MIN_COMBINED', narrow_range=False, axis=2**31-1, dtype=tf.bfloat16) return y test() The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound …

TensorFlow's type inference can cause a heap OOB read as the bounds checking is done in a DCHECK (which is a no-op during production): if (node_t.type_id() != TFT_UNSET) { int ix = input_idx[i]; DCHECK(ix < node_t.args_size()) << "input " << i << " should have an output " << ix << " but instead only has " << node_t.args_size() << " outputs: " << node_t.DebugString(); input_types.emplace_back(node_t.args(ix)); // … } An …

The implementation of FractionalAvgPoolGrad does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.FractionalAvgPoolGrad( orig_input_tensor_shape=[2,2,2,2], out_backprop=[[[[1,2], [3, 4], [5, 6]], [[7, 8], [9,10], [11,12]]]], row_pooling_sequence=[-10,1,2,3], col_pooling_sequence=[1,2,3,4], overlapping=True) return y test()

The implementation of shape inference for ReverseSequence does not fully validate the value of batch_dim and can result in a heap OOB read: import tensorflow as tf @tf.function def test(): y = tf.raw_ops.ReverseSequence( input = ['aaa','bbb'], seq_lengths = [1,1,1], seq_dim = -10, batch_dim = -10 ) return y test() There is a check to make sure the value of batch_dim does not go over the rank of the input, but …

There is a typo in TensorFlow's SpecializeType which results in heap OOB read/write: for (int i = 0; i < op_def.output_arg_size(); i++) { // … for (int j = 0; j < t->args_size(); j++) { auto* arg = t->mutable_args(i); // … } } Due to a typo, arg is initialized to the ith mutable argument in a loop where the loop index is j. Hence it is possible to assign …

There is a typo in TensorFlow's SpecializeType which results in heap OOB read/write: for (int i = 0; i < op_def.output_arg_size(); i++) { // … for (int j = 0; j < t->args_size(); j++) { auto* arg = t->mutable_args(i); // … } } Due to a typo, arg is initialized to the ith mutable argument in a loop where the loop index is j. Hence it is possible to assign …

There is a typo in TensorFlow's SpecializeType which results in heap OOB read/write: for (int i = 0; i < op_def.output_arg_size(); i++) { // … for (int j = 0; j < t->args_size(); j++) { auto* arg = t->mutable_args(i); // … } } Due to a typo, arg is initialized to the ith mutable argument in a loop where the loop index is j. Hence it is possible to assign …

A flaw was found in the pipe lookup plugin of ansible. Arbitrary commands can be run, when the pipe lookup plugin uses subprocess.Popen() with shell=True, by overwriting ansible facts and the variable is not escaped by quote plugin. An attacker could take advantage and run arbitrary commands by overwriting the ansible facts.

When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a DCHECK: const auto* attr = attrs.Find(arg->s()); DCHECK(attr != nullptr); if (attr->value_case() == AttrValue::kList) { // … } However, DCHECK is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the …

When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a DCHECK: const auto* attr = attrs.Find(arg->s()); DCHECK(attr != nullptr); if (attr->value_case() == AttrValue::kList) { // … } However, DCHECK is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the …

When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a DCHECK: const auto* attr = attrs.Find(arg->s()); DCHECK(attr != nullptr); if (attr->value_case() == AttrValue::kList) { // … } However, DCHECK is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the …

When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference: string allowed_gpus = flr->config_proto()->gpu_options().visible_device_list(); In the default scenario, all devices are allowed, so flr->config_proto is nullptr.

When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference: string allowed_gpus = flr->config_proto()->gpu_options().visible_device_list(); In the default scenario, all devices are allowed, so flr->config_proto is nullptr.

When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference: string allowed_gpus = flr->config_proto()->gpu_options().visible_device_list(); In the default scenario, all devices are allowed, so flr->config_proto is nullptr.

The implementation of QuantizedMaxPool has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer. import tensorflow as tf tf.raw_ops.QuantizedMaxPool( input = tf.constant([[[[4]]]], dtype=tf.quint8), min_input = [], max_input = [1], ksize = [1, 1, 1, 1], strides = [1, 1, 1, 1], padding = "SAME", name=None )

The implementation of QuantizedMaxPool has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer. import tensorflow as tf tf.raw_ops.QuantizedMaxPool( input = tf.constant([[[[4]]]], dtype=tf.quint8), min_input = [], max_input = [1], ksize = [1, 1, 1, 1], strides = [1, 1, 1, 1], padding = "SAME", name=None )

The implementation of QuantizedMaxPool has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer. import tensorflow as tf tf.raw_ops.QuantizedMaxPool( input = tf.constant([[[[4]]]], dtype=tf.quint8), min_input = [], max_input = [1], ksize = [1, 1, 1, 1], strides = [1, 1, 1, 1], padding = "SAME", name=None )

Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a SavedModel file (fixing the first one would trigger the same dereference in the second place):

Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a SavedModel file (fixing the first one would trigger the same dereference in the second place):

Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a SavedModel file (fixing the first one would trigger the same dereference in the second place):

Impact The code for boosted trees in TensorFlow is still missing validation. This allows malicious users to read and write outside of bounds of heap allocated data as well as trigger denial of service (via dereferencing nullptrs or via CHECK-failures). This follows after CVE-2021-41208 where these APIs were still vulnerable to multiple security issues. Note: Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no …

Impact The code for boosted trees in TensorFlow is still missing validation. This allows malicious users to read and write outside of bounds of heap allocated data as well as trigger denial of service (via dereferencing nullptrs or via CHECK-failures). This follows after CVE-2021-41208 where these APIs were still vulnerable to multiple security issues. Note: Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no …

Impact The code for boosted trees in TensorFlow is still missing validation. This allows malicious users to read and write outside of bounds of heap allocated data as well as trigger denial of service (via dereferencing nullptrs or via CHECK-failures). This follows after CVE-2021-41208 where these APIs were still vulnerable to multiple security issues. Note: Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no …

A malicious user can cause a denial of service by altering a SavedModel such that assertions in function.cc would be falsified and crash the Python interpreter.

A malicious user can cause a denial of service by altering a SavedModel such that assertions in function.cc would be falsified and crash the Python interpreter.

A malicious user can cause a denial of service by altering a SavedModel such that assertions in function.cc would be falsified and crash the Python interpreter.

XWiki Platform is a generic wiki platform offering runtime services for applications built on top of it. In affected versions any user with edit right can copy the content of a page it does not have access to by using it as template of a new page. This issue has been patched in XWiki 13.2CR1 and 12.10.6. Users are advised to update. There are no known workarounds for this issue.

XWiki Platform is a generic wiki platform offering runtime services for applications built on top of it. In affected versions any user with SCRIPT right can read any file located in the XWiki WAR (for example xwiki.cfg and xwiki.properties) through XWiki#invokeServletAndReturnAsString as $xwiki.invokeServletAndReturnAsString("/WEB-INF/xwiki.cfg"). This issue has been patched in XWiki versions 12.10.9, 13.4.3 and 13.7-rc-1. Users are advised to update. The only workaround is to limit SCRIPT right.

A flaw was found in infinispan 10 REST API, where authorization permissions are not checked while performing some server management operations. When authz is enabled, any user with authentication can perform operations like shutting down the server without the ADMIN role.

Apache ActiveMQ uses LocateRegistry.createRegistry() to create the JMX RMI registry and binds the server to the "jmxrmi" entry. It is possible to connect to the registry without authentication and call the rebind method to rebind jmxrmi to something else. If an attacker creates another server to proxy the original, and bound that, he effectively becomes a man in the middle and is able to intercept the credentials when an user …

When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling png::CommonInitDecode(…, &decode), the decode value contains allocated buffers which can only be freed by calling png::CommonFreeDecode(&decode). However, several error case in the function implementation invoke the OP_REQUIRES macro which immediately terminates the execution of the function, without allowing for the memory free to occur.

When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling png::CommonInitDecode(…, &decode), the decode value contains allocated buffers which can only be freed by calling png::CommonFreeDecode(&decode). However, several error case in the function implementation invoke the OP_REQUIRES macro which immediately terminates the execution of the function, without allowing for the memory free to occur.

When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling png::CommonInitDecode(…, &decode), the decode value contains allocated buffers which can only be freed by calling png::CommonFreeDecode(&decode). However, several error case in the function implementation invoke the OP_REQUIRES macro which immediately terminates the execution of the function, without allowing for the memory free to occur.

The implementation of AddManySparseToTensorsMap is vulnerable to an integer overflow which results in a CHECK-fail when building new TensorShape objects (so, an assert failure based denial of service): import tensorflow as tf import numpy as np tf.raw_ops.AddManySparseToTensorsMap( sparse_indices=[(0,0),(0,1),(0,2),(4,3),(5,0),(5,1)], sparse_values=[1,1,1,1,1,1], sparse_shape=[232,232], container='', shared_name='', name=None) We are missing some validation on the shapes of the input tensors as well as directly constructing a large TensorShape with user-provided dimensions. The latter is an …

The implementations of SparseCwise ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or CHECK-fails when building new TensorShape objects (so, assert failures based denial of service): import tensorflow as tf import numpy as np tf.raw_ops.SparseDenseCwiseDiv( sp_indices=np.array([[9]]), sp_values=np.array([5]), sp_shape=np.array([92233720368., 92233720368]), dense=np.array([4])) We are missing some validation on the shapes of the input tensors as well as directly constructing a …

The implementation of AddManySparseToTensorsMap is vulnerable to an integer overflow which results in a CHECK-fail when building new TensorShape objects (so, an assert failure based denial of service): import tensorflow as tf import numpy as np tf.raw_ops.AddManySparseToTensorsMap( sparse_indices=[(0,0),(0,1),(0,2),(4,3),(5,0),(5,1)], sparse_values=[1,1,1,1,1,1], sparse_shape=[232,232], container='', shared_name='', name=None) We are missing some validation on the shapes of the input tensors as well as directly constructing a large TensorShape with user-provided dimensions. The latter is an …

The implementation of AddManySparseToTensorsMap is vulnerable to an integer overflow which results in a CHECK-fail when building new TensorShape objects (so, an assert failure based denial of service): import tensorflow as tf import numpy as np tf.raw_ops.AddManySparseToTensorsMap( sparse_indices=[(0,0),(0,1),(0,2),(4,3),(5,0),(5,1)], sparse_values=[1,1,1,1,1,1], sparse_shape=[232,232], container='', shared_name='', name=None) We are missing some validation on the shapes of the input tensors as well as directly constructing a large TensorShape with user-provided dimensions. The latter is an …

The implementations of SparseCwise ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or CHECK-fails when building new TensorShape objects (so, assert failures based denial of service): import tensorflow as tf import numpy as np tf.raw_ops.SparseDenseCwiseDiv( sp_indices=np.array([[9]]), sp_values=np.array([5]), sp_shape=np.array([92233720368., 92233720368]), dense=np.array([4])) We are missing some validation on the shapes of the input tensors as well as directly constructing a …

The implementations of SparseCwise ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or CHECK-fails when building new TensorShape objects (so, assert failures based denial of service): import tensorflow as tf import numpy as np tf.raw_ops.SparseDenseCwiseDiv( sp_indices=np.array([[9]]), sp_values=np.array([5]), sp_shape=np.array([92233720368., 92233720368]), dense=np.array([4])) We are missing some validation on the shapes of the input tensors as well as directly constructing a …

Impact The Grappler component of TensorFlow is vulnerable to a denial of service via CHECK-failure in constant folding for ; // … } The output_prop tensor has a shape that is controlled by user input and this can result in triggering one of the CHECKs in the PartialTensorShape constructor. This is an instance of TFSA-2021-198 . ### Patches We have patched the issue in GitHub commit be7b286d40bc68cb0b56f702186cc4837d508058 fix will be …

Impact The Grappler component of TensorFlow is vulnerable to a denial of service via CHECK-failure in constant folding for ; // … } The output_prop tensor has a shape that is controlled by user input and this can result in triggering one of the CHECKs in the PartialTensorShape constructor. This is an instance of TFSA-2021-198 . ### Patches We have patched the issue in GitHub commit be7b286d40bc68cb0b56f702186cc4837d508058 fix will be …

Impact The Grappler component of TensorFlow is vulnerable to a denial of service via CHECK-failure in constant folding for ; // … } The output_prop tensor has a shape that is controlled by user input and this can result in triggering one of the CHECKs in the PartialTensorShape constructor. This is an instance of TFSA-2021-198 . ### Patches We have patched the issue in GitHub commit be7b286d40bc68cb0b56f702186cc4837d508058 fix will be …

The implementation of SparseCountSparseOutput can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation: import tensorflow as tf import numpy as np tf.raw_ops.SparseCountSparseOutput( indices=[[1,1]], values=[2], dense_shape=[2 ** 31, 2 ** 32], weights=[1], binary_output=True, minlength=-1, maxlength=-1, name=None)

The implementation of SparseCountSparseOutput can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation: import tensorflow as tf import numpy as np tf.raw_ops.SparseCountSparseOutput( indices=[[1,1]], values=[2], dense_shape=[2 ** 31, 2 ** 32], weights=[1], binary_output=True, minlength=-1, maxlength=-1, name=None)

The implementation of SparseCountSparseOutput can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation: import tensorflow as tf import numpy as np tf.raw_ops.SparseCountSparseOutput( indices=[[1,1]], values=[2], dense_shape=[2 ** 31, 2 ** 32], weights=[1], binary_output=True, minlength=-1, maxlength=-1, name=None)

An attacker can craft a TFLite model that would cause an integer overflow in TfLiteIntArrayCreate: TfLiteIntArray* TfLiteIntArrayCreate(int size) { int alloc_size = TfLiteIntArrayGetSizeInBytes(size); // … TfLiteIntArray* ret = (TfLiteIntArray*)malloc(alloc_size); // … } The TfLiteIntArrayGetSizeInBytes returns an int instead of a size_t: int TfLiteIntArrayGetSizeInBytes(int size) { static TfLiteIntArray dummy; int computed_size = sizeof(dummy) + sizeof(dummy.data[0]) * size;

An attacker can craft a TFLite model that would cause an integer overflow in TfLiteIntArrayCreate: TfLiteIntArray* TfLiteIntArrayCreate(int size) { int alloc_size = TfLiteIntArrayGetSizeInBytes(size); // … TfLiteIntArray* ret = (TfLiteIntArray*)malloc(alloc_size); // … } The TfLiteIntArrayGetSizeInBytes returns an int instead of a size_t: int TfLiteIntArrayGetSizeInBytes(int size) { static TfLiteIntArray dummy; int computed_size = sizeof(dummy) + sizeof(dummy.data[0]) * size;

An attacker can craft a TFLite model that would cause an integer overflow in TfLiteIntArrayCreate: TfLiteIntArray* TfLiteIntArrayCreate(int size) { int alloc_size = TfLiteIntArrayGetSizeInBytes(size); // … TfLiteIntArray* ret = (TfLiteIntArray*)malloc(alloc_size); // … } The TfLiteIntArrayGetSizeInBytes returns an int instead of a size_t: int TfLiteIntArrayGetSizeInBytes(int size) { static TfLiteIntArray dummy; int computed_size = sizeof(dummy) + sizeof(dummy.data[0]) * size;

An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations: int embedding_size = 1; int lookup_size = 1; for (int i = 0; i < lookup_rank - 1; i++, k++) { const int dim = dense_shape->data.i32[i]; lookup_size *= dim; output_shape->data[k] = dim; } for (int i = 1; i < embedding_rank; i++, k++) { const int dim = SizeOfDimension(value, i); embedding_size *= dim; …

An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations: int embedding_size = 1; int lookup_size = 1; for (int i = 0; i < lookup_rank - 1; i++, k++) { const int dim = dense_shape->data.i32[i]; lookup_size *= dim; output_shape->data[k] = dim; } for (int i = 1; i < embedding_rank; i++, k++) { const int dim = SizeOfDimension(value, i); embedding_size *= dim; …

An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations: int embedding_size = 1; int lookup_size = 1; for (int i = 0; i < lookup_rank - 1; i++, k++) { const int dim = dense_shape->data.i32[i]; lookup_size *= dim; output_shape->data[k] = dim; } for (int i = 1; i < embedding_rank; i++, k++) { const int dim = SizeOfDimension(value, i); embedding_size *= dim; …

Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior.

Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior.

Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior.

The implementation of shape inference for Dequantize is vulnerable to an integer overflow weakness: import tensorflow as tf input = tf.constant([1,1],dtype=tf.qint32) @tf.function def test(): y = tf.raw_ops.Dequantize( input=input, min_range=[1.0], max_range=[10.0], mode='MIN_COMBINED', narrow_range=False, axis=2**31-1, dtype=tf.bfloat16) return y test() The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not …

The implementation of Range suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations.

The implementation of Range suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations.

The implementation of shape inference for Dequantize is vulnerable to an integer overflow weakness: import tensorflow as tf input = tf.constant([1,1],dtype=tf.qint32) @tf.function def test(): y = tf.raw_ops.Dequantize( input=input, min_range=[1.0], max_range=[10.0], mode='MIN_COMBINED', narrow_range=False, axis=2**31-1, dtype=tf.bfloat16) return y test() The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not …

The implementation of Range suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations.

The implementation of shape inference for Dequantize is vulnerable to an integer overflow weakness: import tensorflow as tf input = tf.constant([1,1],dtype=tf.qint32) @tf.function def test(): y = tf.raw_ops.Dequantize( input=input, min_range=[1.0], max_range=[10.0], mode='MIN_COMBINED', narrow_range=False, axis=2**31-1, dtype=tf.bfloat16) return y test() The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not …

If NiFi Registry to uses an authentication mechanism other than PKI, when the user clicks Log Out, NiFi Registry invalidates the authentication token on the client side but not on the server side. This permits the user's client-side token to be used for up to hours after logging out to make API requests to NiFi Registry.

In multiple places, TensorFlow uses tempfile.mktemp to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in mktemp and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a …

In multiple places, TensorFlow uses tempfile.mktemp to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in mktemp and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a …

In multiple places, TensorFlow uses tempfile.mktemp to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in mktemp and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a …

A flaw was found in Ansible Engine when a file is moved using atomic_move primitive as the file mode cannot be specified. This sets the destination files world-readable if the destination file does not exist and if the file exists, the file could be changed to have less restrictive permissions before the move. This could lead to the disclosure of sensitive data. All versions in 2.7.x, 2.8.x and 2.9.x branches …

Apache Accumulo versions 1.5.0 through 1.10.0 and version 2.0.0 do not properly check the return value of some policy enforcement functions before permitting an authenticated user to perform certain administrative operations. Specifically, the return values of the 'canFlush' and 'canPerformSystemActions' security functions are not checked in some instances, therefore allowing an authenticated user with insufficient permissions to perform the following actions: flushing a table, shutting down Accumulo or an individual …

A flaw was found in all versions of Keycloak before 10.0.0, where the NodeJS adapter did not support the verify-token-audience. This flaw results in some users having access to sensitive information outside of their permissions.

Versions of Apache DolphinScheduler prior to 1.3.2 allowed an ordinary user under any tenant to override another users password through the API interface.

A Incorrect Default Permissions vulnerability in the packaging of tomcat on SUSE Enterprise Storage 5, SUSE Linux Enterprise Server 12-SP2-BCL, SUSE Linux Enterprise Server 12-SP2-LTSS, SUSE Linux Enterprise Server 12-SP3-BCL, SUSE Linux Enterprise Server 12-SP3-LTSS, SUSE Linux Enterprise Server 12-SP4, SUSE Linux Enterprise Server 12-SP5, SUSE Linux Enterprise Server 15-LTSS, SUSE Linux Enterprise Server for SAP 12-SP2, SUSE Linux Enterprise Server for SAP 12-SP3, SUSE Linux Enterprise Server for SAP …

A Incorrect Default Permissions vulnerability in the packaging of tomcat on SUSE Enterprise Storage 5, SUSE Linux Enterprise Server 12-SP2-BCL, SUSE Linux Enterprise Server 12-SP2-LTSS, SUSE Linux Enterprise Server 12-SP3-BCL, SUSE Linux Enterprise Server 12-SP3-LTSS, SUSE Linux Enterprise Server 12-SP4, SUSE Linux Enterprise Server 12-SP5, SUSE Linux Enterprise Server 15-LTSS, SUSE Linux Enterprise Server for SAP 12-SP2, SUSE Linux Enterprise Server for SAP 12-SP3, SUSE Linux Enterprise Server for SAP …

OPA is an open source, general-purpose policy engine. Under certain conditions, pretty-printing an abstract syntax tree (AST) that contains synthetic nodes could change the logic of some statements by reordering array literals. Example of policies impacted are those that parse and compare web paths. All of these three conditions have to be met to create an adverse effect: An AST of Rego had to be created programmatically such that it …

In Apache Hadoop 3.2.0 to 3.2.1, 3.0.0-alpha1 to 3.1.3, and 2.0.0-alpha to 2.10.0, WebHDFS client might send SPNEGO authorization header to remote URL without proper verification.

In Apache Solr, the cluster can be partitioned into multiple collections and only a subset of nodes actually host any given collection. However, if a node receives a request for a collection it does not host, it proxies the request to a relevant node and serves the request. Solr bypasses all authorization settings for such requests. This affects all Solr versions prior to 7.7 that use the default authorization mechanism …

A flaw was found in keycloak before version 13.0.0. In some scenarios a user still has access to a resource after changing the role mappings in Keycloak and after expiration of the previous access token.

The implementation of tf.sparse.split does not fully validate the input arguments.

The implementation of tf.sparse.split does not fully validate the input arguments.

The implementation of tf.sparse.split does not fully validate the input arguments.

An issue exsits in Gitea, which could let a malicious user gain privileges due to client side cookies not being deleted and the session remains valid on the server side for reuse.

In applications using Spring Cloud Task RELEASE, may be vulnerable to SQL injection when exercising certain lookup queries in the TaskExplorer.

An issue was discovered in ming-soft MCMS v5.0, where a malicious user can exploit SQL injection without logging in through /mcms/view.do.

Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection') in de.tum.in.ase:artemis-java-test-sandbox.

A regression has been introduced in the commit preventing JMX re-bind. By passing an empty environment map to RMIConnectorServer, instead of the map that contains the authentication credentials, it leaves ActiveMQ open to the following attack: https://docs.oracle.com/javase/8/docs/technotes/guides/management/agent.html "A remote client could create a javax.management.loading.MLet MBean and use it to create new MBeans from arbitrary URLs, at least if there is no security manager. In other words, a rogue remote client …

XWiki Platform is a generic wiki platform offering runtime services for applications built on top of it. In affected versions it's possible for an unprivileged user to perform a remote code execution by injecting a groovy script in her own profile and by calling the Reset password feature since the feature is performing a save of the user profile with programming rights in the impacted versions of XWiki. The issue …

dot-object is vulnerable to Prototype Pollution. The set function could be tricked into adding or modifying properties of Object.prototype using a proto payload.

undefsafe is vulnerable to Prototype Pollution. The 'a' function could be tricked into adding or modifying properties of Object.prototype using a proto payload.

An instance of a cross-site scripting vulnerability was identified to be present in the web based administration console on the message.jsp page of Apache ActiveMQ versions 5.15.12 through 5.16.0.

A flaw was found in Keycloak before version 12.0.0, where it is possible to add unsafe schemes for the redirect_uri parameter. This flaw allows an attacker to perform a Cross-site scripting attack.

XWiki Platform is a generic wiki platform offering runtime services for applications built on top of it.This template is only used in the following conditions: The wiki must be open to registration for anyone. The wiki must be closed to view for Guest users or more specifically the XWiki.Registration page must be forbidden in View for guest user. A way to obtain the second condition is when administrators checked the …

In Apache ActiveMQ Artemis 2.5.0 to 2.13.0, a specially crafted MQTT packet which has an XSS payload as client-id or topic name can exploit this vulnerability. The XSS payload is being injected into the admin console's browser. The XSS payload is triggered in the diagram plugin; queue node and the info section.

server/handler/HistogramQueryHandler.scala in Twitter TwitterServer (aka twitter-server), in some configurations, allows XSS via the /histograms endpoint.

A flaw was found in Keycloak's data filter, in version 10.0.1, where it allowed the processing of data URLs in some circumstances. This flaw allows an attacker to conduct cross-site scripting or further attacks.

A Stored Cross-site Scripting (XSS) vulnerability was found in microweber.

Crafter CMS Crafter Studio 3.0.1 is affected by: Cross Site Scripting (XSS), which allows remote attackers to steal users’ cookies.

XWiki Platform is a generic wiki platform offering runtime services for applications built on top of it. it is possible to for the HTML export process to contain reference elements containing filesystem syntax like "../", "./". or "/" in general. The referenced elements are not properly escaped. This issue has been resolved . This issue can be worked around by limiting or disabling document export.

In Crafter CMS Crafter Studio 3.0.1 a directory traversal vulnerability exists which allows unauthenticated attackers to overwrite files from the operating system which can lead to RCE.

A vulnerability was found in keycloak, where path traversal using URL-encoded path segments in the request is possible because the resources endpoint applies a transformation of the url path to the file path. Only few specific folder hierarchies can be exposed by this flaw

Apache Flink 1.5.1 introduced a REST handler that allows you to write an uploaded file to an arbitrary location on the local file system, through a maliciously modified HTTP HEADER. The files can be written to any location accessible by Flink 1.5.1. All users should upgrade to Flink 1.11.3 or 1.12.0 if their Flink instance(s) are exposed. The issue was fixed in commit a5264a6f41524afe8ceadf1d8ddc8c80f323ebc4 from apache/flink:master.

Crafter CMS Crafter Studio 3.0.1 has a directory traversal vulnerability which allows unauthenticated attackers to view files from the operating system.

XWiki Platform before 12.8 mishandles escaping in the property displayer.

superjson is a program to allow JavaScript expressions to be serialized to a superset of JSON. superjson allows input to run arbitrary code on any server using superjson input without prior authentication or knowledge. The only requirement is that the server implements at least one endpoint which uses superjson during request processing. This has been patched in superjson Users are advised to update. There are no known workarounds for this …

superjson is a program to allow JavaScript expressions to be serialized to a superset of JSON. superjson allows input to run arbitrary code on any server using superjson input without prior authentication or knowledge. The only requirement is that the server implements at least one endpoint which uses superjson during request processing. This has been patched in superjson Users are advised to update. There are no known workarounds for this …

Improper Control of Dynamically-Managed Code Resources vulnerability in Crafter Studio of Crafter CMS allows authenticated developers to execute OS commands via FreeMarker template exposed objects. This issue affects: Crafter Software Crafter CMS 3.0 versions prior to 3.0.27; 3.1 versions prior to 3.1.7.

Improper Control of Dynamically-Managed Code Resources vulnerability in Crafter Studio of Crafter CMS allows authenticated developers to execute OS commands via Groovy scripting. This issue affects: Crafter Software Crafter CMS 3.0 versions prior to 3.0.27; 3.1 versions prior to 3.1.7.

A flaw was found in Keycloak in versions before 10.0.0, where it does not perform the TLS hostname verification while sending emails using the SMTP server. This flaw allows an attacker to perform a man-in-the-middle (MITM) attack.

Apache Shiro, when using Apache Shiro with Spring, a specially crafted HTTP request may cause an authentication bypass.

An Authentication Bypass vulnerability exists in Gitea, which could let a malicious user gain privileges. If captured, the TOTP code for the 2FA can be submitted correctly more than once.

Apache Shiro before 1.7.1, when using Apache Shiro with Spring, a specially crafted HTTP request may cause an authentication bypass.

A flaw was found in the reset credential flow in all Keycloak versions before 8.0.0. This flaw allows an attacker to gain unauthorized access to the application.

Netty in WSO2 transport-http before v6.3.1 is vulnerable to HTTP Response Splitting due to HTTP Header validation being disabled.

The implementation of SparseCountSparseOutput is vulnerable to a heap overflow: import tensorflow as tf import numpy as np tf.raw_ops.SparseCountSparseOutput( indices=[[-1,-1]], values=[2], dense_shape=[1, 1], weights=[1], binary_output=True, minlength=-1, maxlength=-1, name=None)

The implementation of SparseCountSparseOutput is vulnerable to a heap overflow: import tensorflow as tf import numpy as np tf.raw_ops.SparseCountSparseOutput( indices=[[-1,-1]], values=[2], dense_shape=[1, 1], weights=[1], binary_output=True, minlength=-1, maxlength=-1, name=None)

The implementation of SparseCountSparseOutput is vulnerable to a heap overflow: import tensorflow as tf import numpy as np tf.raw_ops.SparseCountSparseOutput( indices=[[-1,-1]], values=[2], dense_shape=[1, 1], weights=[1], binary_output=True, minlength=-1, maxlength=-1, name=None)

Generation of Error Message Containing Sensitive Information in Packagist microweber/microweber prior to 1.2.11.

A flaw was found in Keycloak 7.0.1. A logged in user can do an account email enumeration attack.

This advisory impacts flink-runtime.

This advisory impacts flink-runtime.

This advisory has been marked as a False Positive and has been removed.

If an HTTP/2 client connecting to Apache Tomcat 10.0.0-M1 to 10.0.0-M7, 9.0.0.M1 to 9.0.37 or 8.5.0 to 8.5.57 exceeded the agreed maximum number of concurrent streams for a connection (in violation of the HTTP/2 protocol), it was possible that a subsequent request made on that connection could contain HTTP headers - including HTTP/2 pseudo headers - from a previous request rather than the intended headers. This could lead to users …

If an HTTP/2 client connecting to Apache Tomcat 10.0.0-M1 to 10.0.0-M7, 9.0.0.M1 to 9.0.37 or 8.5.0 to 8.5.57 exceeded the agreed maximum number of concurrent streams for a connection (in violation of the HTTP/2 protocol), it was possible that a subsequent request made on that connection could contain HTTP headers - including HTTP/2 pseudo headers - from a previous request rather than the intended headers. This could lead to users …

Exposure of Sensitive Information to an Unauthorized Actor in NPM follow-redirects prior to 1.14.8.

XWiki Platform is a generic wiki platform offering runtime services for applications built on top of it. In affected versions it's possible to guess if a user has an account on the wiki by using the "Forgot your password" form, even if the wiki is closed to guest users. This problem has been patched on XWiki 12.10.9, 13.4.1 and 13.6RC1. Users are advised yo update. There are no known workarounds …

An incomplete fix was found for the fix of the flaw CVE-2020-1733 ansible insecure temporary directory when running become_user from become directive. The provided fix is insufficient to prevent the race condition on systems using ACLs and FUSE filesystems. Ansible Engine 2.7.18, 2.8.12, and 2.9.9 as well as previous versions are affected and Ansible Tower 3.4.5, 3.5.6 and 3.6.4 as well as previous versions are affected.

An attacker can craft a TFLite model that would trigger a division by zero in BiasAndClamp implementation: inline void BiasAndClamp(float clamp_min, float clamp_max, int bias_size, const float* bias_data, int array_size, float* array_data) { // … TFLITE_DCHECK_EQ((array_size % bias_size), 0); // … } There is no check that the bias_size is non zero.

An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is stricly positive.

An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is stricly positive.

An attacker can craft a TFLite model that would trigger a division by zero in BiasAndClamp implementation: inline void BiasAndClamp(float clamp_min, float clamp_max, int bias_size, const float* bias_data, int array_size, float* array_data) { // … TFLITE_DCHECK_EQ((array_size % bias_size), 0); // … } There is no check that the bias_size is non zero.

An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is stricly positive.

An attacker can craft a TFLite model that would trigger a division by zero in BiasAndClamp implementation: inline void BiasAndClamp(float clamp_min, float clamp_max, int bias_size, const float* bias_data, int array_size, float* array_data) { // … TFLITE_DCHECK_EQ((array_size % bias_size), 0); // … } There is no check that the bias_size is non zero.

When handler-router component is enabled in servicecomb-java-chassis, authenticated user may inject some data and cause arbitrary code execution. The problem happens in versions between ~ and fixed in Apache ServiceComb-Java-Chassis.

A deserialization vulnerability existed in dubbo and its earlier versions, which could lead to malicious code execution. Most Dubbo users use Hessian2 as the default serialization/deserialization protool, during Hessian2 deserializing the HashMap object, some functions in the classes stored in HasMap will be executed after a series of program calls, however, those special functions may cause remote command execution.

A Java Serialization vulnerability was found in Apache Tapestry Apache Tapestry 4 will attempt to deserialize the "sp" parameter even before invoking the page's validate method, leading to deserialization without authentication. Apache Tapestry 4 reached end of life and no update to address this issue will be released. Apache Tapestry 5 versions are not vulnerable to this issue. Users of Apache Tapestry 4 should upgrade to the latest Apache Tapestry …

Jenkins defines custom XStream converters that have not been updated to apply the protections for the vulnerability CVE-2021-43859 and allow unconstrained resource usage.

This advisory has been marked as a false positive.

An issue was discovered in Play Framework Carefully crafted JSON payloads sent as a form field lead to Data Amplification. This affects users migrating from a Play that used the Play Java API to serialize classes with protected or private fields to JSON.

A Cross-Site Request Forgery (CSRF) vulnerability was found in microweber.

Under certain scenarios, TensorFlow can fail to specialize a type during shape inference: void InferenceContext::PreInputInit( const OpDef& op_def, const std::vector<const Tensor*>& input_tensors, const std::vector<ShapeHandle>& input_tensors_as_shapes) { const auto ret = full_type::SpecializeType(attrs_, op_def); DCHECK(ret.status().ok()) << "while instantiating types: " << ret.status(); ret_types_ = ret.ValueOrDie(); // … } However, DCHECK is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the ValueOrDie …

Under certain scenarios, TensorFlow can fail to specialize a type during shape inference: void InferenceContext::PreInputInit( const OpDef& op_def, const std::vector<const Tensor*>& input_tensors, const std::vector<ShapeHandle>& input_tensors_as_shapes) { const auto ret = full_type::SpecializeType(attrs_, op_def); DCHECK(ret.status().ok()) << "while instantiating types: " << ret.status(); ret_types_ = ret.ValueOrDie(); // … } However, DCHECK is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the ValueOrDie …

Under certain scenarios, TensorFlow can fail to specialize a type during shape inference: void InferenceContext::PreInputInit( const OpDef& op_def, const std::vector<const Tensor*>& input_tensors, const std::vector<ShapeHandle>& input_tensors_as_shapes) { const auto ret = full_type::SpecializeType(attrs_, op_def); DCHECK(ret.status().ok()) << "while instantiating types: " << ret.status(); ret_types_ = ret.ValueOrDie(); // … } However, DCHECK is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the ValueOrDie …

A GraphDef from a TensorFlow SavedModel can be maliciously altered to cause a TensorFlow process to crash due to encountering a StatusOr value that is an error and forcibly extracting the value from it: if (op_reg_data->type_ctor != nullptr) { VLOG(3) << "AddNode: found type constructor for " << node_def.name(); const auto ctor_type = full_type::SpecializeType(AttrSlice(node_def), op_reg_data->op_def); const FullTypeDef ctor_typedef = ctor_type.ValueOrDie(); if (ctor_typedef.type_id() != TFT_UNSET) { *(node_def.mutable_experimental_type()) = ctor_typedef; } } …

A GraphDef from a TensorFlow SavedModel can be maliciously altered to cause a TensorFlow process to crash due to encountering a StatusOr value that is an error and forcibly extracting the value from it: if (op_reg_data->type_ctor != nullptr) { VLOG(3) << "AddNode: found type constructor for " << node_def.name(); const auto ctor_type = full_type::SpecializeType(AttrSlice(node_def), op_reg_data->op_def); const FullTypeDef ctor_typedef = ctor_type.ValueOrDie(); if (ctor_typedef.type_id() != TFT_UNSET) { *(node_def.mutable_experimental_type()) = ctor_typedef; } } …

A GraphDef from a TensorFlow SavedModel can be maliciously altered to cause a TensorFlow process to crash due to encountering a StatusOr value that is an error and forcibly extracting the value from it: if (op_reg_data->type_ctor != nullptr) { VLOG(3) << "AddNode: found type constructor for " << node_def.name(); const auto ctor_type = full_type::SpecializeType(AttrSlice(node_def), op_reg_data->op_def); const FullTypeDef ctor_typedef = ctor_type.ValueOrDie(); if (ctor_typedef.type_id() != TFT_UNSET) { *(node_def.mutable_experimental_type()) = ctor_typedef; } } …

There exists a race condition between the deletion of the temporary file and the creation of the temporary directory in webkit subproject of HTML/Java API A similar vulnerability has recently been disclosed in other Java projects and the fix in HTML/Java API follows theirs.

A vulnerability was found in all versions of Keycloak Gatekeeper, where on using lower case HTTP headers (via cURL) an attacker can bypass our Gatekeeper. Lower case headers are also accepted by some webservers (e.g. Jetty). This means there is no protection when we put a Gatekeeper in front of a Jetty server and use lowercase headers.

A vulnerability was found in all versions of keycloak, where on using lower case HTTP headers (via cURL) we can bypass our Gatekeeper. Lower case headers are also accepted by some webservers (e.g. Jetty). This means there is no protection when we put a Gatekeeper in front of a Jetty server and use lowercase headers.

The implementation of *Bincount operations allows malicious users to cause denial of service by passing in arguments which would trigger a CHECK-fail: import tensorflow as tf tf.raw_ops.DenseBincount( input=[[0], [1], [2]], size=[1], weights=[3,2,1], binary_output=False) There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in CHECK failures later when the output tensors get allocated.

The implementation of *Bincount operations allows malicious users to cause denial of service by passing in arguments which would trigger a CHECK-fail: import tensorflow as tf tf.raw_ops.DenseBincount( input=[[0], [1], [2]], size=[1], weights=[3,2,1], binary_output=False) There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in CHECK failures later when the output tensors get allocated.

The implementation of *Bincount operations allows malicious users to cause denial of service by passing in arguments which would trigger a CHECK-fail: import tensorflow as tf tf.raw_ops.DenseBincount( input=[[0], [1], [2]], size=[1], weights=[3,2,1], binary_output=False) There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in CHECK failures later when the output tensors get allocated.

A vulnerability was found in Keycloak before 11.0.1 where DoS attack is possible by sending twenty requests simultaneously to the specified keycloak server, all with a Content-Length header value that exceeds the actual byte count of the request body.

An attacker can trigger denial of service via assertion failure by altering a SavedModel on disk such that AttrDefs of some operation are duplicated.

An attacker can trigger denial of service via assertion failure by altering a SavedModel on disk such that AttrDefs of some operation are duplicated.

An attacker can trigger denial of service via assertion failure by altering a SavedModel on disk such that AttrDefs of some operation are duplicated.

Multiple operations in TensorFlow can be used to trigger a denial of service via CHECK-fails (i.e., assertion failures). This is similar to TFSA-2021-198 (CVE-2021-41197) and has similar fixes.

Multiple operations in TensorFlow can be used to trigger a denial of service via CHECK-fails (i.e., assertion failures). This is similar to TFSA-2021-198 (CVE-2021-41197) and has similar fixes.

Multiple operations in TensorFlow can be used to trigger a denial of service via CHECK-fails (i.e., assertion failures). This is similar to TFSA-2021-198 (CVE-2021-41197) and has similar fixes.

A malicious user can cause a denial of service by altering a SavedModel such that Grappler optimizer would attempt to build a tensor using a reference dtype. This would result in a crash due to a CHECK-fail in the Tensor constructor as reference types are not allowed.

A malicious user can cause a denial of service by altering a SavedModel such that Grappler optimizer would attempt to build a tensor using a reference dtype. This would result in a crash due to a CHECK-fail in the Tensor constructor as reference types are not allowed.

A malicious user can cause a denial of service by altering a SavedModel such that Grappler optimizer would attempt to build a tensor using a reference dtype. This would result in a crash due to a CHECK-fail in the Tensor constructor as reference types are not allowed.

Gitea is affected by URL Redirection to Untrusted Site ('Open Redirect') via internal URLs.

Server Side Request Forgery (SSRF) vulneraility exists in Gitea using the OpenID URL.

Fix of CVE-2021-40525 do not prepend delimiters upon valid directory validations. Affected implementations include: - maildir mailbox store - Sieve file repository This enables a user to access other users data stores (limited to user names being prefixed by the value of the username being used).

Impact Systems that rely on digest equivalence for image attestations may be vulnerable to type confusion.

An h2c direct connection to Apache Tomcat 10.0.0-M1 to 10.0.0-M6, 9.0.0.M5 to 9.0.36 and 8.5.1 to 8.5.56 did not release the HTTP/1.1 processor after the upgrade to HTTP/2. If a sufficient number of such requests were made, an OutOfMemoryException could occur leading to a denial of service.

The payload length in a WebSocket frame was not correctly validated in Apache Tomcat 10.0.0-M1 to 10.0.0-M6, 9.0.0.M1 to 9.0.36, 8.5.0 to 8.5.56 and 7.0.27 to 7.0.104. Invalid payload lengths could trigger an infinite loop. Multiple requests with invalid payload lengths could lead to a denial of service.

The payload length in a WebSocket frame was not correctly validated in Apache Tomcat 10.0.0-M1 to 10.0.0-M6, 9.0.0.M1 to 9.0.36, 8.5.0 to 8.5.56 and 7.0.27 to 7.0.104. Invalid payload lengths could trigger an infinite loop. Multiple requests with invalid payload lengths could lead to a denial of service.

NATS nats-server has Incorrect Access Control. Any authenticated user can obtain the privileges of the System account by misusing the "dynamically provisioned sandbox accounts" feature.

Kubernetes API server in all versions allow an attacker who is able to create a ClusterIP service and set the spec.externalIPs field, to intercept traffic to that IP address. Additionally, an attacker who is able to patch the status (which is considered a privileged operation and should not typically be granted to users) of a LoadBalancer service can set the status.loadBalancer.ingress.ip to similar effect.

NATS nats-server before 2.7.2 has Incorrect Access Control. Any authenticated user can obtain the privileges of the System account by misusing the "dynamically provisioned sandbox accounts" feature.

Studio elFinder allows XSS via an SVG document.

Cross Site Scripting (XSS) vulnerability exists in Gitea via the repository settings inside the external wiki/issue tracker URL field.

Cross-site Scripting (XSS) - Reflected in Packagist pimcore/pimcore

Cross-site Scripting (XSS) - Stored in Packagist pimcore/pimcore

Cross Site Request Forgery (CSRF) vulnerability exists in Gitea via API routes.This can be dangerous especially with state altering POST requests.

Jopl allows remote attackers to execute system commands through malicious code in user search results.

Gitea which could let a remote malisious user execute arbitrary code.

Business Logic Errors in GitHub repository publify/publify

In Apache Traffic Control Traffic Ops, an unprivileged user who can reach Traffic Ops over HTTPS can send a specially-crafted POST request to /user/login/oauth to scan a port of a server that Traffic Ops can reach.

Cross-site Scripting (XSS) - Stored in Packagist remdex/livehelperchat prior to 3.93v.

Fix of CVE-2021-40525 do not prepend delimiters upon valid directory validations. Affected implementations include the maildir mailbox store and the Sieve file repository. This enables a user to access other users data stores (limited to user names being prefixed by the value of the username being used).

Argo CD before 2.1.9 and 2.2.x before 2.2.4 allows directory traversal related to Helm charts because of an error in helmTemplate in repository.go. For example, an attacker may be able to discover credentials stored in a YAML file.

Argo CD before 2.1.9 and 2.2.x before 2.2.4 allows directory traversal related to Helm charts because of an error in helmTemplate in repository.go. For example, an attacker may be able to discover credentials stored in a YAML file.

Frourio is a full stack framework, for TypeScript. Frourio users who uses frourio version prior to v0.26.0 and integration with class-validator through validators/ folder are subject to a input validation vulnerability. Validators do not work properly for request bodies and queries in specific situations and some input is not validated at all. Users are advised to update frourio to v0.26.0 or later and to install class-transformer and reflect-metadata.

Frourio-express is a minimal full stack framework, for TypeScript. Frourio-express users who uses frourio-express version prior to v0.26.0 and integration with class-validator through validators/ folder are subject to a input validation vulnerability. Validators do not work properly for request bodies and queries in specific situations and some input is not validated at all. Users are advised to update frourio to v0.26.0 or later and to install class-transformer and reflect-metadata.

fleet is an open source device management, built on osquery. expose a limited ability to spoof SAML authentication with missing audience verification. This impacts deployments using SAML SSO in two specific cases: A malicious or compromised Service Provider (SP) could reuse the SAML response to log into Fleet as a user – only if the user has an account with the same email in Fleet, and the user signs into …

Cookie and Authorization headers are leaked when following cross-origin redirects in twited.web.client.RedirectAgent and twisted.web.client.BrowserLikeRedirectAgent.

During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user: const auto num_dims = Value(shape_dim); std::vector<DimensionHandle> dims; dims.reserve(num_dims);

During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user: const auto num_dims = Value(shape_dim); std::vector<DimensionHandle> dims; dims.reserve(num_dims);

During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user: const auto num_dims = Value(shape_dim); std::vector<DimensionHandle> dims; dims.reserve(num_dims);

The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a SavedModel such that IsSimplifiableReshape would trigger CHECK failures.

The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a SavedModel such that IsSimplifiableReshape would trigger CHECK failures.

The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a SavedModel such that IsSimplifiableReshape would trigger CHECK failures.

Unrestricted Upload of File with Dangerous Type in Packagist jsdecena/laracom prior to v2.0.9.

In Apache ActiveMQ Artemis, an attacker could partially disrupt availability (DoS) through uncontrolled resource consumption of memory.

In Apache Traffic Control Traffic Ops, an unprivileged user who can reach Traffic Ops over HTTPS can send a specially-crafted POST request to /user/login/oauth to scan a port of a server that Traffic Ops can reach.

CGI.escape_html in Ruby has an integer overflow and resultant buffer overflow via a long string on platforms (such as Windows) where size_t and long have different numbers of bytes.

Cross-site Scripting (XSS) - DOM in NPM karma

Cross-site Scripting (XSS) - Reflected in Packagist ptrofimov/beanstalk_console

In Apache Gobblin, the Hadoop token is written to a temp file that is visible to all local users on Unix-like systems. This affects Users should update to which addresses this issue.

Apache Gobblin trusts all certificates used for LDAP connections in Gobblin-as-a-Service. This affects Users should update to which addresses this issue.

A Cross-Site Request Forgery vulnerability exists in Filebrowser < 2.18.0 that allows attackers to create a backdoor user with admin privilege and get access to the filesystem via a malicious HTML webpage that is sent to the victim. An admin can run commands using the FileBrowser and hence it leads to RCE.

A Cross-Site Request Forgery vulnerability exists in Filebrowser that allows attackers to create a backdoor user with admin privilege and get access to the filesystem via a malicious HTML webpage that is sent to the victim. An admin can run commands using the FileBrowser and hence it leads to RCE.

A Cross-Site Request Forgery vulnerability exists in Filebrowser that allows attackers to create a backdoor user with admin privilege and get access to the filesystem via a malicious HTML webpage that is sent to the victim. An admin can run commands using the FileBrowser and hence it leads to RCE.

Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect. If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues …

An issue was discovered in MultiPartParser in Django 2.2 before 2.2.27, 3.2 before 3.2.12, and 4.0 before 4.0.2. Passing certain inputs to multipart forms could result in an infinite loop when parsing files.

This affects the package @strikeentco/set It allows an attacker to cause a denial of service and may lead to remote code execution. Note: This vulnerability derives from an incomplete fix in https://security.snyk.io/vuln/SNYK-JS-STRIKEENTCOSET-1038821

This affects the package putil-merge The merge() function does not check the values passed into the argument. An attacker can supply a malicious value by adjusting the value to include the constructor property. Note: This vulnerability derives from an incomplete fix in https://security.snyk.io/vuln/SNYK-JS-PUTILMERGE-1317077

The package object-path-set is vulnerable to Prototype Pollution via the setPath method, as it allows an attacker to merge object prototypes into it. Note: This vulnerability derives from an incomplete fix in https://security.snyk.io/vuln/SNYK-JS-OBJECTPATHSET-607908

Twig is an open source template language for PHP.Patched versions now disallow calling non Closure in the sort filter as is the case for some other filters. Users are advised to upgrade.

In OpenZeppelin <=v4.4.0, initializer functions that are invoked separate from contract creation (the most prominent example being minimal proxies) may be reentered if they make an untrusted non-view external call. Once an initializer has finished running it can never be re-executed. However, an exception put in place to support multiple inheritance made reentrancy possible, breaking the expectation that there is a single execution.

In OpenZeppelin <=v4.4.0, initializer functions that are invoked separate from contract creation (the most prominent example being minimal proxies) may be reentered if they make an untrusted non-view external call. Once an initializer has finished running it can never be re-executed. However, an exception put in place to support multiple inheritance made reentrancy possible, breaking the expectation that there is a single execution.

The {% debug %} template tag in Django 2.2 before 2.2.27, 3.2 before 3.2.12, and 4.0 before 4.0.2 does not properly encode the current context. This may lead to XSS.

Business Logic Errors in GitHub repository silverstripe/silverstripe-framework

Business Logic Errors in GitHub repository silverstripe/silverstripe-framework

As mitigations to a report from 2019 and CVE-2020-8555, Kubernetes attempts to prevent proxied connections from accessing link-local or localhost networks when making user-driven connections to Services, Pods, Nodes, or StorageClass service providers. As part of this mitigation Kubernetes does a DNS name resolution check and validates that response IPs are not in the link-local (169.254.0.0/16) or localhost (127.0.0.0/8) range. Kubernetes then performs a second DNS resolution without validation for …

pgjdbc is the offical PostgreSQL JDBC Driver. A security hole was found in the jdbc driver for postgresql database while doing security research. The system using the postgresql library will be attacked when attacker control the jdbc url or properties. pgjdbc instantiates plugin instances based on class names provided via authenticationPluginClassName, sslhostnameverifier, socketFactory, sslfactory, sslpasswordcallback connection properties. However, the driver does not verify if the class implements the expected interface …

iText v7.1.17 was discovered to contain a stack-based buffer overflow via the component ByteBuffer.append, which allows attackers to cause a Denial of Service (DoS) via a crafted PDF file.

iText v7.1.17 was discovered to contain an out-of-bounds exception via the component ARCFOUREncryption.encryptARCFOUR, which allows attackers to cause a Denial of Service (DoS) via a crafted PDF file.

Apache Superset up to and including 1.3.2 allowed for registered database connections password leak for authenticated users. This information could be accessed in a non-trivial way. Users should upgrade to Apache Superset 1.4.0 or higher.

Reflected Cross-site scripting (XSS) vulnerability in RosarioSIS 8.2.1 allows attackers to inject arbitrary HTML via the search_term parameter in the modules/Scheduling/Courses.php script.

Path Traversal in NPM w-zip

iText v7.1.17 was discovered to contain an out-of-memory error via the component readStreamBytesRaw, which allows attackers to cause a Denial of Service (DoS) via a crafted PDF file.

Element Desktop is a Matrix client for desktop platforms with Element Web at its core. Element Desktop before 1.9.7 is vulnerable to a remote program execution bug with user interaction. The exploit is non-trivial and requires clicking on a malicious link, followed by another button click. To the best of our knowledge, the vulnerability has never been exploited in the wild. If you are using Element Desktop < 1.9.7, we …

Treq's request methods (treq.get, treq.post, HTTPClient.request, HTTPClient.get, etc.) accept cookies as a dictionary, for example: treq.get('https://example.com/', cookies={'session': '1234'}) Such cookies are not bound to a single domain, and are therefore sent to every domain ("supercookies"). This can potentially cause sensitive information to leak upon an HTTP redirect to a different domain., e.g. should https://example.com redirect to http://cloudstorageprovider.com the latter will receive the cookie session.

Authenticated remote code execution in MotionEye and MotioneEyeOS allows a remote attacker to upload a configuration backup file containing a malicious python pickle file which will execute arbitrary code on the server.

calibreweb prior to version 0.6.16 contains a Server-Side Request Forgery (SSRF) vulnerability.

Impact The set method is vulnerable to prototype pollution with specially crafted inputs. // insert the following into poc.js and run node poc,js (after installing the package) let parser = require("min-dash"); parser.set({}, [["proto"], "polluted"], "success"); console.log(polluted); Patches min-dash>=3.8.1 fix the issue. Workarounds No workarounds exist for the issue. References Closed via https://github.com/bpmn-io/min-dash/pull/21. Credits Credits to Cristian-Alexandru STAICU who found the vulnerability and to Idan Digmi from the Snyk Security Team …

SharpZipLib (or #ziplib) is a Zip, GZip, Tar and BZip2 library. A check was added if the destination file is under a destination directory. However, it is not enforced that _baseDirectory ends with slash. If the _baseDirectory is not slash terminated like /home/user/dir it is possible to create a file with a name thats begins as the destination directory one level up from the directory, i.e. /home/user/dir.sh. Because of the …

SharpZipLib (or #ziplib) is a Zip, GZip, Tar and BZip2 library. A check was added if the destination file is under destination directory. However, it is not enforced that destDir ends with slash. If the destDir is not slash terminated like /home/user/dir it is possible to create a file with a name thats begins with the destination directory, i.e. /home/user/dir.sh. Because of the file name and destination directory constraints, the …

User enumeration in database authentication in Flask-AppBuilder < 3.4.4. Allows for a non authenticated user to enumerate existing accounts by timing the response time from the server when you are logging in.

Junrar is an open source java RAR archive library. The impact depends solely on how the application uses the library, and whether files can be provided by malignant users. The problem is patched There are no known workarounds and users are advised to upgrade as soon as possible.

Apache Superset up to and including allowed for registered database connections password leak for authenticated users. This information could be accessed in a non-trivial way. Users should upgrade to Apache Superset or higher.

In Apache Pulsar it is possible to access data from BookKeeper that does not belong to the topics accessible by the authenticated user. The Admin API get-message-by-id requires the user to input a topic and a ledger id. The ledger id is a pointer to the data, and it is supposed to be a valid it for the topic. Authorisation controls are performed against the topic name and there is …

SharpZipLib (or #ziplib) is a Zip, GZip, Tar and BZip2 library. A TAR file entry ../evil.txt may be extracted in the parent directory of destFolder. This leads to arbitrary file write that may lead to code execution.

gh-ost is a triggerless online schema migration solution for MySQL. It is subject to an arbitrary file read vulnerability. The attacker must have access to the target host or trick an administrator into executing a malicious gh-ost command on a host running gh-ost, plus network access from host running gh-ost to the attack's malicious MySQL server. The -database parameter does not properly sanitize user input which can lead to arbitrary …

Cross-site Scripting (XSS) - Stored in Packagist remdex/livehelperchat prior to 3.93v.

The Logs plugin before 3.0.4 for Craft CMS allows remote attackers to read arbitrary files via input to actionStream in Controller.php.

An improper input validation vulnerability in go-attestation allows local users to provide a maliciously-formed Quote over no/some PCRs, causing AKPublic.Verify to succeed despite the inconsistency. Subsequent use of the same set of PCR values in Eventlog.Verify lacks the authentication performed by quote verification, meaning a local attacker could couple this vulnerability with a maliciously-crafted TCG log in Eventlog.Verify to spoof events in the TCG log, hence defeating remotely-attested measured-boot.

In Apache Pulsar it is possible to access data from BookKeeper that does not belong to the topics accessible by the authenticated user. The Admin API get-message-by-id requires the user to input a topic and a ledger id. The ledger id is a pointer to the data, and it is supposed to be a valid it for the topic. Authorisation controls are performed against the topic name and there is …

In Apache Pulsar it is possible to access data from BookKeeper that does not belong to the topics accessible by the authenticated user. The Admin API get-message-by-id requires the user to input a topic and a ledger id. The ledger id is a pointer to the data, and it is supposed to be a valid it for the topic. Authorisation controls are performed against the topic name and there is …

An improper input validation vulnerability in go-attestation before 0.3.3 allows local users to provide a maliciously-formed Quote over no/some PCRs, causing AKPublic.Verify to succeed despite the inconsistency. Subsequent use of the same set of PCR values in Eventlog.Verify lacks the authentication performed by quote verification, meaning a local attacker could couple this vulnerability with a maliciously-crafted TCG log in Eventlog.Verify to spoof events in the TCG log, hence defeating remotely-attested …

Impact Directory Traversal Vulnerabilities found in several functions of apoc plugins in Neo4j Graph database. The attacker can retrieve and download files from outside the configured directory on the affected server. Under some circumstances, the attacker can also create files.

XStream is an open source java library to serialize objects to XML and back again. may allow a remote attacker to allocate % CPU time on the target system depending on CPU type or parallel execution of such a payload resulting in a denial of service only by manipulating the processed input stream. XStream monitors and accumulates the time it takes to add elements to collections and throws an exception …

Plone is vulnerable to reflected cross site scripting and open redirect when an attacker can get a compromised version of the image_view_fullscreen page in a cache, for example in Varnish. The technique is known as cache poisoning. Any later visitor can get redirected when clicking on a link on this page. Usually only anonymous users are affected, but this depends on your cache settings.

Symfony is a PHP framework for web and console applications and a set of reusable PHP components. The Symfony form component provides a CSRF protection mechanism by using a random token injected in the form and using the session to store and control the token submitted by the user. When using the FrameworkBundle, this protection can be enabled or disabled with the configuration. If the configuration is not specified, by …

Symfony is a PHP framework for web and console applications and a set of reusable PHP components. The Symfony form component provides a CSRF protection mechanism by using a random token injected in the form and using the session to store and control the token submitted by the user. When using the FrameworkBundle, this protection can be enabled or disabled with the configuration. If the configuration is not specified, by …

Symfony is a PHP framework for web and console applications and a set of reusable PHP components. The Symfony form component provides a CSRF protection mechanism by using a random token injected in the form and using the session to store and control the token submitted by the user. When using the FrameworkBundle, this protection can be enabled or disabled with the configuration. If the configuration is not specified, by …

calibreweb prior to version 0.6.16 contains an Incorrect Authorization vulnerability.

Business Logic Errors in Packagist dolibarr/dolibarr

The query API in Casdo has a SQL injection vulnerability related to the field and value parameters, as demonstrated by api/get-organizations.

Cross-site Scripting (XSS) - Stored in Packagist remdex/livehelperchat prior to 3.93v.

calibreweb prior to version 0.6.16 contains a cross-site scripting vulnerability.

Next. one must use next start or a custom server and the built-in i18n support. Deployments on Vercel, along with similar environments where invalid requests are filtered before reaching Next.js, are not affected. A patch has been released, next@12.0.9, that mitigates this issue. As a workaround, one may ensure /${locale}/_next/ is blocked from reaching the Next.js instance until it becomes feasible to upgrade.

laminas-form is a package for validating and displaying simple and complex forms. When rendering validation error messages via the formElementErrors() view helper shipped with laminas-form, many messages will contain the submitted value. However, in laminas-form, the value was not being escaped for HTML contexts, which could potentially lead to a reflected cross-site scripting attack. contain a patch to mitigate the vulnerability. A workaround is available. One may manually place code …

The package zip-local is vulnerable to Arbitrary File Write via Archive Extraction (Zip Slip) which can lead to an extraction of a crafted file outside the intended extraction directory.

The HTTP response will disclose the user password.

Missing authentication on ShenYu Admin when they register by HTTP.

User can access /plugin api without authentication.

From version 0.2.14 to 0.2.16 for Solana rBPF, function "relocate" in the file src/elf.rs has an integer overflow bug because the sym.st_value is read directly from ELF file without checking. If the sym.st_value is rather large, an integer overflow is triggered while calculating the variable "addr" via addr = (sym.st_value + refd_pa) as u64

The package bmo is vulnerable to Prototype Pollution due to missing sanitization in set function.

The package keyget from is vulnerable to Prototype Pollution via the methods set, push, and at which could allow an attacker to cause a denial of service and may lead to remote code execution.

Cross-site Scripting (XSS) - Stored in Packagist remdex/livehelperchat prior to 3.93v.

Cross-site Scripting (XSS) - Stored in Packagist remdex/livehelperchat prior to 3.93v.

Cross-site Scripting (XSS) - Stored in Packagist bytefury/crater prior to 6.0.2.

Cross-site Scripting (XSS) - Stored in Packagist remdex/livehelperchat prior to 3.93v.

Cross-site Scripting (XSS) - Reflected in Packagist microweber/microweber prior to 1.2.11.

Cross-site Scripting (XSS) - Stored in Packagist microweber/microweber prior to 1.2.11.

Products.ATContentTypes are the core content types for Plone - Versions of Plone that are dependent on Products.ATContentTypes is vulnerable to reflected cross site scripting and open redirect when an attacker can get a compromised version of the image_view_fullscreen page in a cache, for example in Varnish. The technique is known as cache poisoning. Any later visitor can get redirected when clicking on a link on this page. Usually only anonymous …

Cross-site Scripting (XSS) - Stored in Packagist remdex/livehelperchat prior to 3.93v.

Apache Karaf obr:* commands and run goal on the karaf-maven-plugin have partial path traversal which allows to break out of expected folder. The risk is low as obr:* commands are not very used and the entry is set by user. This has been fixed in revision: https://gitbox.apache.org/repos/asf?p=karaf.git;h=36a2bc4 https://gitbox.apache.org/repos/asf?p=karaf.git;h=52b70cf Mitigation: Apache Karaf users should upgrade to 4.2.15 or 4.3.6 or later as soon as possible, or use correct path. JIRA Tickets: …

Groovy Code Injection & SpEL Injection which lead to Remote Code Execution. This issue affected Apache ShenYu 2.4.0 and 2.4.1.

This advisory has been marked as False Positive and moved to org.apache.karaf.management/org.apache.karaf.management.server.

Apache Karaf allows monitoring of applications and the Java runtime by using the Java Management Extensions (JMX). JMX is a Java RMI based technology that relies on Java serialized objects for client server communication. Whereas the default JMX implementation is hardened against unauthenticated deserialization attacks, the implementation used by Apache Karaf is not protected against this kind of attack. The impact of Java deserialization vulnerabilities strongly depends on the classes …

Bingrep v0.8.5 was discovered to contain a memory allocation failure which can cause a Denial of Service (DoS).

Plone is vulnerable to reflected cross site scripting and open redirect when an attacker can get a compromised version of the image_view_fullscreen page in a cache, for example in Varnish.

Plone is vulnerable to reflected cross site scripting and open redirect when an attacker can get a compromised version of the image_view_fullscreen page in a cache, for example in Varnish. The technique is known as cache poisoning. Any later visitor can get redirected when clicking on a link on this page. Usually only anonymous users are affected, but this depends on your cache settings.

There is a carry propagation bug in the MIPS32 and MIPS64 squaring procedure. Many EC algorithms are affected, including some of the TLS 1.3 default curves. Impact was not analyzed in detail, because the pre-requisites for attack are considered unlikely and include reusing private keys. Analysis suggests that attacks against RSA and DSA as a result of this defect would be very difficult to perform and are not believed likely. …

There's a vulnerability within the Apache Xerces Java (XercesJ) XML parser when handling specially crafted XML document payloads. This causes, the XercesJ XML parser to wait in an infinite loop, which may sometimes consume system resources for prolonged duration. This vulnerability is present within XercesJ version 2.12.1 and the previous versions.

File upload vulnerability in mingSoft MCMS through 5.2.5, allows remote attackers to execute arbitrary code via a crafted jspx webshell to net.mingsoft.basic.action.web.FileAction#upload.

The fix for bug CVE-2020-9484 introduced a time of check, time of use vulnerability into Apache Tomcat that allowed a local attacker to perform actions with the privileges of the user that the Tomcat process is using. This issue is only exploitable when Tomcat is configured to persist sessions using the FileStore.

The fix for bug CVE-2020-9484 introduced a time of check, time of use vulnerability into Apache Tomcat that allowed a local attacker to perform actions with the privileges of the user that the Tomcat process is using. This issue is only exploitable when Tomcat is configured to persist sessions using the FileStore.

What kind of vulnerability is it? Server-Side Request Forgery ( SSRF ) Who is impacted? Any user deploying Jupyter Server or Notebook with jupyter-proxy-server extension enabled. A lack of input validation allowed authenticated clients to proxy requests to other hosts, bypassing the allowed_hosts check. Because authentication is required, which already grants permissions to make the same requests via kernel or terminal execution, this is considered low to moderate severity.

Nullptr dereference when a null char is present in a proto symbol. The symbol is parsed incorrectly, leading to an unchecked call into the proto file's name during generation of the resulting error message. Since the symbol is incorrectly parsed, the file is nullptr. We recommend upgrading to version 3.15.0 or greater.

Nullptr dereference when a null char is present in a proto symbol. The symbol is parsed incorrectly, leading to an unchecked call into the proto file's name during generation of the resulting error message. Since the symbol is incorrectly parsed, the file is nullptr. We recommend upgrading to version 3.15.0 or greater.

Nullptr dereference when a null char is present in a proto symbol. The symbol is parsed incorrectly, leading to an unchecked call into the proto file's name during generation of the resulting error message. Since the symbol is incorrectly parsed, the file is nullptr. We recommend upgrading to version 3.15.0 or greater.

Nullptr dereference when a null char is present in a proto symbol. The symbol is parsed incorrectly, leading to an unchecked call into the proto file's name during generation of the resulting error message. Since the symbol is incorrectly parsed, the file is nullptr. We recommend upgrading to version 3.15.0 or greater.

Nullptr dereference when a null char is present in a proto symbol. The symbol is parsed incorrectly, leading to an unchecked call into the proto file's name during generation of the resulting error message. Since the symbol is incorrectly parsed, the file is nullptr. We recommend upgrading to version 3.15.0 or greater.

Nullptr dereference when a null char is present in a proto symbol. The symbol is parsed incorrectly, leading to an unchecked call into the proto file's name during generation of the resulting error message. Since the symbol is incorrectly parsed, the file is nullptr. We recommend upgrading to or greater.

Improper Access Control in GitHub repository crater-invoice/crater prior to 6.0.2.

https://gitee.com/mingSoft/MCMS MCMS <=5.2.5 is affected by: SQL Injection. The impact is: obtain sensitive information (remote). The component is: net.mingsoft.mdiy.action.web.DictAction#list. The attack vector is: 0 or sleep(3). ¶¶ MCMS has a sql injection vulnerability through which attacker can get sensitive information from the database.

model/criteria/criteria.go in Navidrome is vulnerable to SQL injection attacks when processing crafted Smart Playlists. An authenticated user could abuse this to extract arbitrary data from the database, including the user table (which contains sensitive information such as the users' encrypted passwords).

https://gitee.com/mingSoft/MCMS MCMS <=5.2.5 is affected by: SQL Injection. The impact is: obtain sensitive information (remote). The component is: net.mingsoft.mdiy.action.FormDataAction#queryData. The attack vector is: 0 or sleep(3). ¶¶ MCMS has a sql injection vulnerability through which attacker can get sensitive information from the database.

Improper Neutralization of Special Elements Used in a Template Engine in Packagist mustache/mustache

Cross-site Scripting (XSS) - Stored in Packagist getgrav/grav prior to 1.7.28.

Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') in github.com/projectdiscovery/interactsh.

A stored Cross-site Scripting (XSS) vulnrability was found in pimcore.

This affects all versions of package convert-svg-core; all versions of package convert-svg-to-png; all versions of package convert-svg-to-jpeg. Using a specially crafted SVG file, an attacker could read arbitrary files from the file system and then show the file content as a converted PNG file.

This affects all versions of package convert-svg-core; all versions of package convert-svg-to-png; all versions of package convert-svg-to-jpeg. Using a specially crafted SVG file, an attacker could read arbitrary files from the file system and then show the file content as a converted PNG file.

PrestaShop is an Open Source e-commerce platform. Starting with version 1.7.0.0 and ending with version 1.7.8.3, an attacker is able to inject twig code inside the back office when using the legacy layout. The problem is fixed in version 1.7.8.3. There are no known workarounds.

graphql-go is a GraphQL server with a focus on ease of use. there exists a DoS vulnerability that is possible due to a bug in the library that would allow an attacker with specifically designed queries to cause stack overflow panics. Any user with access to the GraphQL handler can send these queries and cause stack overflows. This in turn could potentially compromise the ability of the server to serve …

Cross-Site Request Forgery (CSRF) in Packagist yetiforce/yetiforce-crm prior to 6.3.0.

SPIP is affected by a remote command execution vulnerability. To exploit the vulnerability, an attacker must craft a malicious picture with a double extension, upload it and then click on it to execute it.

Nullptr dereference when a null char is present in a proto symbol. The symbol is parsed incorrectly, leading to an unchecked call into the proto file's name during generation of the resulting error message. Since the symbol is incorrectly parsed, the file is nullptr.

Improper Privilege Management in Conda loguru prior to 0.5.3.

In JeecgBoot, there is a SQL injection vulnerability that can operate the database with root privileges.

In JeecgBoot, there is a SQL injection vulnerability that can operate the database with root privileges.

A SQL injection vulnerability was found in showdoc.

SPIP is affected by a Cross Site Scripting (XSS) vulnerability. To exploit the vulnerability, a visitor must browse to a malicious SVG file. The vulnerability allows an authenticated attacker to inject malicious code running on the client side into web pages visited by other users (stored XSS).

SPIP is affected by a Cross Site Scripting (XSS) vulnerability in ecrire/public/interfaces.php, adding the function safehtml to the vulnerable fields. An editor is able to modify his personal information. If the editor has an article written and available, when a user goes to the public site and wants to read the author's information, the malicious code will be executed. The Who are you and Website Name fields are vulnerable.

A stored Cross-site Scripting (XSS) vulnerability was found in pimcore.

Apache Karaf obr:* commands and run goal on the karaf-maven-plugin have partial path traversal which allows to break out of expected folder. The risk is low as obr:* commands are not very used and the entry is set by user.

This advisory has been marked as False Positive and moved to org.apache.karaf.management/org.apache.karaf.management.server.

Exposure of Sensitive Information to an Unauthorized Actor in NPM simple-get.

Apache Karaf allows monitoring of applications and the Java runtime by using the Java Management Extensions (JMX). JMX is a Java RMI based technology that relies on Java serialized objects for client server communication. Whereas the default JMX implementation is hardened against unauthenticated deserialization attacks, the implementation used by Apache Karaf is not protected against this kind of attack. The impact of Java deserialization vulnerabilities strongly depends on the classes …

SPIP is affected by a Cross Site Request Forgery (CSRF) vulnerability in ecrire/public/aiguiller.php, ecrire/public/balises.php, ecrire/balise/formulaire_.php. To exploit the vulnerability, a visitor must visit a malicious website which redirects to the SPIP website. It is also possible to combine XSS vulnerabilities in SPIP to exploit it. The vulnerability allows an authenticated attacker to execute malicious code without the knowledge of the user on the website (CSRF).

livehelperchat is vulnerable to Cross-Site Request Forgery (CSRF)

livehelperchat is vulnerable to Cross-Site Request Forgery (CSRF)

There is an Assertion ''JERRY_CONTEXT (jmem_heap_allocated_size) == 0'' failed at /jerry-core/jmem/jmem-heap.c in Jerryscript

There is an Assertion ''ecma_is_value_boolean (base_value)'' failed at /jerry-core/ecma/operations/ecma-get-put-value.c in Jerryscript

Jerryscript v3.0.0 was discovered to contain a stack overflow via ecma_find_named_property in ecma-helpers.c.

A flaw was found in Keycloak which allows an attacker with any existing user account to create new default user accounts via the administrative REST API even when new user registration is disabled.

The calendar:manageentries capability allowed managers to access or modify any calendar event, but should have been restricted from accessing user level events.

A flaw was found in Keycloak in versions from 12.0.0 and before 15.1.1 which allows an attacker with any existing user account to create new default user accounts via the administrative REST API even when new user registration is disabled.

There is an Assertion ''ecma_object_is_typedarray (obj_p)'' failed at /jerry-core/ecma/operations/ecma-typedarray-object.c in Jerryscript

An SQL injection risk was identified in the h5p activity web service responsible for fetching user attempt data.

Shenyu is vulnerable to Groovy Code Injection & SpEL Injection which can lead to Remote Code Execution.

Insufficient capability checks could lead to users accessing their grade report for courses where they does not have the required gradereport/user:view capability.

The delete badge alignment functionality does not include the necessary token check to prevent a CSRF risk.

Authentication Bypass by Primary Weakness exists in adodb/adodb.

The Easy Forms for Mailchimp WordPress plugin does not sanitise and escape the field_name and field_type parameters before outputting them back in attributes, leading to Reflected Cross-Site Scripting issues

CodeIgniter4 is the branch of CodeIgniter, a PHP full-stack web framework. A cross-site scripting (XSS) vulnerability was found in API\ResponseTrait in Codeigniter4 Attackers can do XSS attacks if a potential victim is using API\ResponseTrait. contains a patch for this vulnerability. There are two potential workarounds available. Users may avoid using API\ResponseTrait or ResourceController Users may also disable Auto Route and use defined routes only.

Cross-site Scripting (XSS) - Stored in GitHub repository vanessa219/vdit

MCMS v5.2.4 was discovered to contain an arbitrary file upload vulnerability via the component /ms/template/writeFileContent.do.

MCMS v5.2.4 was discovered to have an arbitrary file upload vulnerability in the New Template module, which allows attackers to execute arbitrary code via a crafted ZIP file.

A remote code execution (RCE) vulnerability in the Template Management function of MCMS v5.2.4 allows attackers to execute arbitrary code via a crafted payload.

Cross-site Scripting (XSS) - Stored in GitHub repository star7th/showdoc

An attacker can inject malicious code into aspects of the setup script, which can allow XSS or HTML injection.

An issue was discovered in phpMyAdm. A valid user who is already authenticated to phpMyAdmin can manipulate their account to bypass two-factor authentication for future login instances.

Between September 26, 2021 and October 8, 2021, Radically Open Security conducted a penetration test of OnionShare 2.4, funded by the Open Technology Fund's Red Team lab. This is an issue from that penetration test. Vulnerability ID: OTF-005 Vulnerability type: Improper Input Sanitization Threat level: Low

HDF5 v1.13.1-1 was discovered to contain a heap-use-after free via the component H5AC_unpin_entry.

forge is vulnerable to URL Redirection to Untrusted Site

Unrestricted Upload of File with Dangerous Type in GitHub repository crater-invoice/crater

crater is vulnerable to Unrestricted Upload of File with Dangerous Type

Impact When handling form responses from the client (ModalFormResponsePacket), the Minecraft Windows client may send weird JSON that json_decode() can't understand. A workaround for this is implemented in InGamePacketHandler::stupid_json_decode(). An InvalidArgumentException is thrown by this function when it fails to fix an error found in the JSON, which is not caught by the caller. This leads to a server crash. Patches 56fe71d939c38fe14e18a31a673a9331bcc0e4ca Workarounds A plugin may handle DataPacketReceiveEvent, capture ModalFormResponsePacket …

The password reset component deployed within Umbraco uses the hostname supplied within the request host header when building a password reset URL.See the AppCheck advisory for further information and associated caveats.

Within the Umbraco CMS, a configuration element named "UmbracoApplicationUrl" (or just "ApplicationUrl") is used whenever application code needs to build a URL pointing back to the site. For example, when a user resets their password and the application builds a password reset URL or when the administrator invites users to the site. For Umbraco versions less than, if the Application URL is not specifically configured, the attacker can manipulate this …

UltraJSON (aka ujson) 1.34 through 5.1.0 has a stack-based buffer overflow in Buffer_AppendIndentUnchecked (called from encode).

The package @isomorphic-git/cors-proxy is vulnerable to Server-side Request Forgery (SSRF) due to missing sanitization and validation of the redirection action in middleware.js.

There is an Assertion 'ecma_is_value_undefined (value) || ecma_is_value_null (value) || ecma_is_value_boolean (value) || ecma_is_value_number (value) || ecma_is_value_string (value) || ecma_is_value_bigint (value) || ecma_is_value_symbol (value) || ecma_is_value_object (value)' failed at jerry-core/ecma/base/ecma-helpers-value.c in Jerryscripts

Between September 26, 2021 and October 8, 2021, Radically Open Security conducted a penetration test of OnionShare 2.4, funded by the Open Technology Fund's Red Team lab. This is an issue from that penetration test. Vulnerability ID: OTF-013 Vulnerability type: Improper Hardening Threat level: Low

Jerryscript was discovered to contain a stack overflow via ecma_lcache_lookup in /jerry-core/ecma/base/ecma-lcache.c.

Jerryscript was discovered to contain a heap-buffer-overflow via ecma_utf8_string_to_number_by_radix in /jerry-core/ecma/base/ecma-helpers-conversion.c.

Jerryscript was discovered to contain a stack overflow via vm_loop.lto_priv.304 in /jerry-core/vm/vm.c.

Between September 26, 2021 and October 8, 2021, Radically Open Security conducted a penetration test of OnionShare 2.4, funded by the Open Technology Fund's Red Team lab. This is an issue from that penetration test. Vulnerability ID: OTF-014 Vulnerability type: Out-of-bounds Read Threat level: Elevated