GHSA-fq6p-6334-8gr4 BIT-tensorflow-2022-23585 CVE-2022-23585 PYSEC-2022-149 PYSEC-2022-94 PYSEC-2026-3174
Memory leak in decoding PNG images
- Severity
- 4.3 / 10
- Medium · CVSS 3.1
- Exploit chance (EPSS)
- 1.00%
- Chance of exploitation in the next 30 days. Higher than 62.83% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-401 Missing Release of Memory after Effective Lifetime
- Fix
- Available
- 3 packages have a fixed version
Description
Impact
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.
Patches
We have patched the issue in GitHub commit ab51e5b813573dc9f51efa335aebcf2994125ee9.
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 3.1 4.3
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- Low
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- None
- Integrity
- None
- Availability
- Low
Weakness
CWE-401: Missing Release of Memory after Effective Lifetime
The product does not sufficiently track and release allocated memory after it has been used, making the memory unavailable for reallocation and reuse.
How to prevent it
- Implementation: Choose a language or tool that provides automatic memory management, or makes manual memory management less error-prone. For example, glibc in Linux provides protection against free of invalid pointers. When using Xcode to target OS X or iOS, enable automatic reference counting (ARC) [REF-391]. To help correctly and consistently manage memory when programming in C++, consider using a smart pointer class such as std::auto_ptr (defined by ISO/IEC ISO/IEC 14882:2003), std::shared_ptr and std::unique_ptr (specified by an upcoming revision of the C++ standard, informally referred to as C++ 1x), or equivalent solutions such as Boost.
- Architecture and Design: Use an abstraction library to abstract away risky APIs. Not a complete solution.
- Architecture and Design, Build and Compilation: Consider using the Boehm-Demers-Weiser garbage collector (bdwgc), which can help avoid leaks.
References
Advisories
Packages
Other links
- github.com/tensorflow/tensorflow/security/advisories/GHSA-fq6p-6334-8gr4
- github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-94.…
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-149…
- 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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