CVE-2022-23578: Tensorflow Memory Leak Vulnerability

MODERATE
Advisory Identifier: GHSA-8r7c-3cm2-3h8f
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CVSS 4.3
CWE-401
Published
Advisory Summary

CVE-2022-23578 is a MODERATE (CVSS 4.3) memory leak vulnerability (CWE-401: Missing Release of Memory after Effective Lifetime) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves memory leak in tensorflow. Remediation is available by upgrading affected packages to version 2.5.3, 2.6.3, 2.7.1.

CVE-2022-23578 at a Glance

CVE Identifier CVE-2022-23578
GHSA Identifier GHSA-8r7c-3cm2-3h8f
Severity MODERATE
CVSS v3.1 Base Score 4.3
CWE Classification CWE-401: Missing Release of Memory after Effective Lifetime
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 4.3

The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L).

Attack Vector Network (AV:N)
Attack Complexity Low (AC:L)
Privileges Required Low (PR:L)
User Interaction None (UI:N)
Scope Unchanged (S:U)
Confidentiality None (C:N)
Integrity None (I:N)
Availability Low (A:L)

How to Fix CVE-2022-23578

To remediate this vulnerability, upgrade all affected Tensorflow dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.

tensorflow fixed: 2.5.3 
pip install tensorflow>=2.5.3
tensorflow fixed: 2.6.3 
pip install tensorflow>=2.6.3
tensorflow fixed: 2.7.1 
pip install tensorflow>=2.7.1
tensorflow-cpu fixed: 2.5.3 
pip install tensorflow-cpu>=2.5.3
tensorflow-cpu fixed: 2.6.3 
pip install tensorflow-cpu>=2.6.3
tensorflow-cpu fixed: 2.7.1 
pip install tensorflow-cpu>=2.7.1
tensorflow-gpu fixed: 2.5.3 
pip install tensorflow-gpu>=2.5.3
tensorflow-gpu fixed: 2.6.3 
pip install tensorflow-gpu>=2.6.3
tensorflow-gpu fixed: 2.7.1 
pip install tensorflow-gpu>=2.7.1
Verify Direct and Transitive Dependencies

Run the following command in your project directory to verify that all resolved dependencies use the patched release: 

pip show tensorflow

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2022-23578, inspect your installed packages using pip:

pip show tensorflow

Analyze the terminal output to locate any version of Tensorflow in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 2.5.3), your application is affected.

Note: The pip show tensorflow command checks currently installed packages in your active Python environment.

Technical Details

Vulnerability Mechanism & Technical Analysis

This vulnerability is classified under CWE-401: Missing Release of Memory after Effective Lifetime, where the product does not sufficiently track and release allocated memory after it has been used, making the memory unavailable for reallocation and reuse. In Tensorflow, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.

Source Advisory Statement

### Impact If a graph node is invalid, TensorFlow can leak memory in the [implementation of `ImmutableExecutorState::Initialize`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/common_runtime/immutable_executor_state.cc#L84-L262): ```cc Status s = params_.create_kernel(n->properties(), &item->kernel); if (!s.ok()) { item->kernel = nullptr; s = AttachDef(s, *n); return s; } ``` Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak. ### Patches We have patched the issue in GitHub commit [c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd](https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd). 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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

CVE-2022-23578 Affected Tensorflow Versions

9 component(s)
Fixed Versions:
2.5.32.6.32.7.1
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
pypi/tensorflow
PyPI
< 2.5.3
2.5.3
tensorflow
pypi/tensorflow
PyPI
≥ 2.6.0
< 2.6.3
2.6.3
tensorflow
pypi/tensorflow
PyPI
≥ 2.7.0
< 2.7.1
All 1 versions
2.7.0
2.7.1
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
< 2.5.3
2.5.3
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.6.0
< 2.6.3
2.6.3
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.7.0
< 2.7.1
All 1 versions
2.7.0
2.7.1
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
< 2.5.3
2.5.3
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.6.0
< 2.6.3
2.6.3
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.7.0
< 2.7.1
All 1 versions
2.7.0
2.7.1

References

GitHub Advisory / Wiki ADVISORY
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8r7c-3cm2-3h8f
NVD — CVE-2022-23578 NVD
https://nvd.nist.gov/vuln/detail/CVE-2022-23578
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-87.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-142.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow
Tensorflow Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/common_runtime/immutable_executor_state.cc#L84-L262

Frequently Asked Questions

What is CVE-2022-23578?
CVE-2022-23578 is a security vulnerability in Tensorflow involving Memory leak in Tensorflow under CWE-401: Missing Release of Memory after Effective Lifetime.
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.5.3, 2.6.3, 2.7.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2022-23578?
Upgrading to 2.5.3, 2.6.3, 2.7.1 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 2.5.3, 2.6.3, 2.7.1. Upgrade your dependency to 2.5.3, 2.6.3, 2.7.1 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.5.3, 2.6.3, 2.7.1. Upgrade your dependency to 2.5.3, 2.6.3, 2.7.1 or later.
What is the CVSS score of CVE-2022-23578?
CVE-2022-23578 has a CVSS v3.1 base score of 4.3 (MODERATE) with vector string CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L.
What CWE is associated with CVE-2022-23578?
CVE-2022-23578 is classified under 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 can I check PyPI dependencies for CVE-2022-23578?
Run the dependency tree command: 'pip show tensorflow'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.
Machine-readable OSV JSON OSV v1.5 Schema
Normalized schema output
View Raw JSON 
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  "schema_version" : "1.4.0",
  "id" : "GHSA-8r7c-3cm2-3h8f",
  "modified" : "2024-11-13T22:49:23Z",
  "published" : "2022-02-10T00:33:13Z",
  "aliases" : [ "CVE-2022-23578" ],
  "summary" : "Memory leak in Tensorflow",
  "details" : "### Impact\nIf a graph node is invalid, TensorFlow can leak memory in the [implementation of `ImmutableExecutorState::Initialize`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/common_runtime/immutable_executor_state.cc#L84-L262):\n\n```cc\nStatus s = params_.create_kernel(n->properties(), &item->kernel);\nif (!s.ok()) {\n  item->kernel = nullptr;\n  s = AttachDef(s, *n);\n  return s;           \n}                     \n```\n\nHere, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak.\n\n### Patches\nWe have patched the issue in GitHub commit [c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd](https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd).\nThe 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.\n\n### For more information \nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.",
  "severity" : [ {
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}