CVE-2022-21736: Undefined behavior in `SparseTensorSliceDataset`
(updated )
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.
References
- github.com/advisories/GHSA-pfjj-m3jj-9jc9
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-60.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-115.yaml
- github.com/tensorflow/tensorflow
- github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc
- github.com/tensorflow/tensorflow/commit/965b97e4a9650495cda5a8c210ef6684b4b9eceb
- github.com/tensorflow/tensorflow/security/advisories/GHSA-pfjj-m3jj-9jc9
- nvd.nist.gov/vuln/detail/CVE-2022-21736
Code Behaviors & Features
Detect and mitigate CVE-2022-21736 with GitLab Dependency Scanning
Secure your software supply chain by verifying that all open source dependencies used in your projects contain no disclosed vulnerabilities. Dependency Scanning using SBOM also detects vulnerabilities from proprietary advisories that this site does not include. Learn more about Dependency Scanning using SBOM →