GHSA-q85f-69q7-55h2 BIT-tensorflow-2022-23573 CVE-2022-23573 PYSEC-2022-137 PYSEC-2022-82 PYSEC-2026-3229
Uninitialized variable access in Tensorflow
- Severity
- 0.0 / 10
- High · CVSS 4.0
- Exploit chance (EPSS)
- 0.76%
- Chance of exploitation in the next 30 days. Higher than 55.34% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-908 Use of Uninitialized Resource
- Fix
- Available
- 3 packages have a fixed version
Description
Impact
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.
Patches
We have patched the issue in GitHub commit ef1d027be116f25e25bb94a60da491c2cf55bd0b.
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.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| tensorflow | < 2.5.3 |
2.5.3 |
| tensorflow | >= 2.6.0, < 2.6.3 |
2.6.3 |
| tensorflow | >= 2.7.0, < 2.7.1 |
2.7.1 |
| tensorflow | < 2.5.3>= 2.6.0, < 2.6.3>= 2.7.0, < 2.7.1 |
2.5.32.6.32.7.1 |
| tensorflow-cpu | < 2.5.3 |
2.5.3 |
| tensorflow-cpu | >= 2.6.0, < 2.6.3 |
2.6.3 |
| tensorflow-cpu | >= 2.7.0, < 2.7.1 |
2.7.1 |
| tensorflow-cpu | < 2.5.3>= 2.6.0, < 2.6.3 |
2.5.32.6.3 |
| tensorflow-gpu | < 2.5.3 |
2.5.3 |
| tensorflow-gpu | >= 2.6.0, < 2.6.3 |
2.6.3 |
| tensorflow-gpu | >= 2.7.0, < 2.7.1 |
2.7.1 |
| tensorflow-gpu | < 2.5.3>= 2.6.0, < 2.6.3 |
2.5.32.6.3 |
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
tensorflow PyPI → 2.5.3
- pip
pip install --upgrade "tensorflow>=2.5.3"- poetry
poetry add "tensorflow>=2.5.3"- uv
uv add "tensorflow>=2.5.3"
tensorflow-cpu PyPI → 2.5.3
- pip
pip install --upgrade "tensorflow-cpu>=2.5.3"- poetry
poetry add "tensorflow-cpu>=2.5.3"- uv
uv add "tensorflow-cpu>=2.5.3"
tensorflow-gpu PyPI → 2.5.3
- pip
pip install --upgrade "tensorflow-gpu>=2.5.3"- poetry
poetry add "tensorflow-gpu>=2.5.3"- uv
uv add "tensorflow-gpu>=2.5.3"
Severity breakdown
CVSS 4.0 0.0
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N
- Attack vector
- Network
- Attack complexity
- Low
- Attack requirements
- None
- Privileges required
- Low
- User interaction
- None
- Confidentiality (vulnerable system)
- Low
- Integrity (vulnerable system)
- Low
- Availability (vulnerable system)
- High
- Confidentiality (subsequent systems)
- None
- Integrity (subsequent systems)
- None
- Availability (subsequent systems)
- None
CVSS 3.1 7.6
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- Low
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- Low
- Integrity
- Low
- Availability
- High
Weakness
CWE-908: Use of Uninitialized Resource
The product uses or accesses a resource that has not been initialized.
How to prevent it
- Implementation: Explicitly initialize the resource before use. If this is performed through an API function or standard procedure, follow all required steps.
- Implementation: Pay close attention to complex conditionals that affect initialization, since some branches might not perform the initialization.
- Implementation: Avoid race conditions (CWE-362) during initialization routines.
- Build and Compilation: Run or compile the product with settings that generate warnings about uninitialized variables or data.
References
Advisories
Packages
Other links
- github.com/tensorflow/tensorflow/security/advisories/GHSA-q85f-69q7-55h2
- github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-82.…
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-137…
- github.com/tensorflow/tensorflow
- github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/…
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| GHSA-gjh7-xx4r-x345 TensorFlow has segfault in array_ops.upper_bound | High | Also affects tensorflow |
| GHSA-fxgc-95xx-grvq TensorFlow Denial of Service vulnerability | Medium | Also affects tensorflow |
| GHSA-93vr-9q9m-pj8p TensorFlow vulnerable to Out-of-Bounds Read in DynamicStitch | High | Also affects tensorflow |
| GHSA-qjqc-vqcf-5qvj TensorFlow vulnerable to seg fault in `tf.raw_ops.Print` | High | Also affects tensorflow |
| GHSA-7jvm-xxmr-v5cw TensorFlow vulnerable to integer overflow in EditDistance | High | Also affects tensorflow |
| GHSA-64jg-wjww-7c5w TensorFlow has Null Pointer Error in TensorArrayConcatV2 | High | Also affects tensorflow |
| GHSA-6hg6-5c2q-7rcr TensorFlow has Heap-buffer-overflow in AvgPoolGrad | High | Also affects tensorflow |
| GHSA-558h-mq8x-7q9g TensorFlow has Null Pointer Error in SparseSparseMaximum | High | Also affects tensorflow |
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