CVE-2020-26266: Tensorflow Uninitialized Memory Access Vulnerability

MODERATE
Advisory Identifier: GHSA-qhxx-j73r-qpm2
Share
CVSS 4.4
CWE-908
Published
Advisory Summary

CVE-2020-26266 is a MODERATE (CVSS 4.4) uninitialized memory access vulnerability (CWE-908: Use of Uninitialized Resource) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves uninitialized memory access in tensorflow. Remediation is available by upgrading affected packages to version 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2.

CVE-2020-26266 at a Glance

CVE Identifier CVE-2020-26266
GHSA Identifier GHSA-qhxx-j73r-qpm2
Severity MODERATE
CVSS v3.1 Base Score 4.4
CWE Classification CWE-908: Use of Uninitialized Resource
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 4.4

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

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

How to Fix CVE-2020-26266

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: 1.15.5 
pip install tensorflow>=1.15.5
tensorflow fixed: 2.0.4 
pip install tensorflow>=2.0.4
tensorflow fixed: 2.1.3 
pip install tensorflow>=2.1.3
tensorflow fixed: 2.2.2 
pip install tensorflow>=2.2.2
tensorflow fixed: 2.3.2 
pip install tensorflow>=2.3.2
tensorflow-cpu fixed: 1.15.5 
pip install tensorflow-cpu>=1.15.5
tensorflow-cpu fixed: 2.0.4 
pip install tensorflow-cpu>=2.0.4
tensorflow-cpu fixed: 2.1.3 
pip install tensorflow-cpu>=2.1.3
tensorflow-cpu fixed: 2.2.2 
pip install tensorflow-cpu>=2.2.2
tensorflow-cpu fixed: 2.3.2 
pip install tensorflow-cpu>=2.3.2
tensorflow-gpu fixed: 1.15.5 
pip install tensorflow-gpu>=1.15.5
tensorflow-gpu fixed: 2.0.4 
pip install tensorflow-gpu>=2.0.4
tensorflow-gpu fixed: 2.1.3 
pip install tensorflow-gpu>=2.1.3
tensorflow-gpu fixed: 2.2.2 
pip install tensorflow-gpu>=2.2.2
tensorflow-gpu fixed: 2.3.2 
pip install tensorflow-gpu>=2.3.2
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-2020-26266, 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 1.15.5), 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-908: Use of Uninitialized Resource, where the product uses or accesses a resource that has not been initialized. 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 Under certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to [default initialize the quantized floating point types in Eigen](https://github.com/tensorflow/tensorflow/blob/f70160322a579144950dff1537dcbe3c7c09d6f5/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h#L61-L104): ```cc struct QUInt8 { QUInt8() {} // ... uint8_t value; }; struct QInt16 { QInt16() {} // ... int16_t value; }; struct QUInt16 { QUInt16() {} // ... uint16_t value; }; struct QInt32 { QInt32() {} // ... int32_t value; }; ``` ### Patches We have patched the issue in GitHub commit [ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2](https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive. ### 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-2020-26266 Affected Tensorflow Versions

15 component(s)
Fixed Versions:
1.15.52.0.42.1.32.2.22.3.2
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
pypi/tensorflow
PyPI
< 1.15.5
1.15.5
tensorflow
pypi/tensorflow
PyPI
≥ 2.0.0
< 2.0.4
2.0.4
tensorflow
pypi/tensorflow
PyPI
≥ 2.1.0
< 2.1.3
2.1.3
tensorflow
pypi/tensorflow
PyPI
≥ 2.2.0
< 2.2.2
2.2.2
tensorflow
pypi/tensorflow
PyPI
≥ 2.3.0
< 2.3.2
2.3.2
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
< 1.15.5
1.15.5
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.0.0
< 2.0.4
2.0.4
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.1.0
< 2.1.3
2.1.3
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.2.0
< 2.2.2
2.2.2
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.3.0
< 2.3.2
2.3.2
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
< 1.15.5
1.15.5
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.0.0
< 2.0.4
2.0.4
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.1.0
< 2.1.3
2.1.3
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.2.0
< 2.2.2
2.2.2
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.3.0
< 2.3.2
2.3.2

References

GitHub Advisory / Wiki ADVISORY
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhxx-j73r-qpm2
NVD — CVE-2020-26266 NVD
https://nvd.nist.gov/vuln/detail/CVE-2020-26266
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-297.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-332.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-254.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow

Frequently Asked Questions

What is CVE-2020-26266?
CVE-2020-26266 is a security vulnerability in Tensorflow involving Uninitialized memory access in TensorFlow under CWE-908: Use of Uninitialized Resource.
Which Tensorflow versions are affected?
Affected versions include releases prior to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2020-26266?
Upgrading to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2. Upgrade your dependency to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2. Upgrade your dependency to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 or later.
What is the CVSS score of CVE-2020-26266?
CVE-2020-26266 has a CVSS v3.1 base score of 4.4 (MODERATE) with vector string CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L.
What CWE is associated with CVE-2020-26266?
CVE-2020-26266 is classified under CWE-908: Use of Uninitialized Resource (The product uses or accesses a resource that has not been initialized.).
How can I check PyPI dependencies for CVE-2020-26266?
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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  "id" : "GHSA-qhxx-j73r-qpm2",
  "modified" : "2024-10-28T19:57:07Z",
  "published" : "2020-12-10T19:07:24Z",
  "aliases" : [ "CVE-2020-26266" ],
  "summary" : "Uninitialized memory access in TensorFlow",
  "details" : "### Impact\nUnder certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to [default initialize the quantized floating point types in Eigen](https://github.com/tensorflow/tensorflow/blob/f70160322a579144950dff1537dcbe3c7c09d6f5/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h#L61-L104):\n\n```cc\nstruct QUInt8 {\n  QUInt8() {}\n  // ...\n  uint8_t value;\n};\n\nstruct QInt16 {\n  QInt16() {}\n  // ...\n  int16_t value;\n};\n\nstruct QUInt16 {\n  QUInt16() {}\n  // ...\n  uint16_t value;\n};\n\nstruct QInt32 {\n  QInt32() {}\n  // ...\n  int32_t value;\n};\n```\n\n### Patches\nWe have patched the issue in GitHub commit [ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2](https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.\n\nSince this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.\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.",
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    "nvd_published_at" : null
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}