CVE-2020-15197: Denial of Service in Tensorflow
(updated )
The SparseCountSparseOutput implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the indices tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L185
However, malicious users can pass in tensors of different rank, resulting in a CHECK assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor.
References
- github.com/advisories/GHSA-qc53-44cj-vfvx
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-277.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-312.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-120.yaml
- github.com/tensorflow/tensorflow
- github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02
- github.com/tensorflow/tensorflow/releases/tag/v2.3.1
- github.com/tensorflow/tensorflow/security/advisories/GHSA-qc53-44cj-vfvx
- nvd.nist.gov/vuln/detail/CVE-2020-15197
Detect and mitigate CVE-2020-15197 with GitLab Dependency Scanning
Secure your software supply chain by verifying that all open source dependencies used in your projects contain no disclosed vulnerabilities. Learn more about Dependency Scanning →