PYSEC-2022-131
See a problem?- Import Source
- https://github.com/pypa/advisory-database/blob/main/vulns/tensorflow-gpu/PYSEC-2022-131.yaml
- JSON Data
- https://api.osv.dev/v1/vulns/PYSEC-2022-131
- Aliases
- Published
- 2022-02-03T12:15:00Z
- Modified
- 2023-12-06T00:46:58.158074Z
- Summary
- [none]
- Details
-
Tensorflow is an Open Source Machine Learning Framework. The implementations of
Sparse*Cwise*ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) orCHECK-fails when building newTensorShapeobjects (so, assert failures based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a largeTensorShapewith user-provided dimensions. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. - References
-
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrx2-r989-2c43
- https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md
- https://github.com/tensorflow/tensorflow/commit/e952a89b7026b98fe8cbe626514a93ed68b7c510
- https://github.com/tensorflow/tensorflow/commit/1b54cadd19391b60b6fcccd8d076426f7221d5e8
- https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc
Affected packages
PyPI / tensorflow-gpu
Package
- Name
- tensorflow-gpu
- View open source insights on deps.dev
- Purl
- pkg:pypi/tensorflow-gpu
Affected ranges
- Type
- GIT
- Repo
- https://github.com/tensorflow/tensorflow
- Events
-
Introduced0 Unknown introduced commit / All previous commits are affectedFixedFixed
- Type
- ECOSYSTEM
- Events
-
Introduced0Unknown introduced version / All previous versions are affectedFixed2.5.3Introduced2.6.0Fixed2.6.3
Affected versions
0.*
0.12.0
0.12.1
1.*
1.0.0
1.0.1
1.1.0
1.2.0
1.2.1
1.3.0
1.4.0
1.4.1
1.5.0
1.5.1
1.6.0
1.7.0
1.7.1
1.8.0
1.9.0
1.10.0
1.10.1
1.11.0
1.12.0
1.12.2
1.12.3
1.13.1
1.13.2
1.14.0
1.15.0
1.15.2
1.15.3
1.15.4
1.15.5
2.*
2.0.0
2.0.1
2.0.2
2.0.3
2.0.4
2.1.0
2.1.1
2.1.2
2.1.3
2.1.4
2.2.0
2.2.1
2.2.2
2.2.3
2.3.0
2.3.1
2.3.2
2.3.3
2.3.4
2.4.0
2.4.1
2.4.2
2.4.3
2.4.4
2.5.0
2.5.1
2.5.2
2.6.0
2.6.1
2.6.2