CVE-2020-15195: Tensorflow Heap Buffer Overflow Vulnerability

Q: What is CVE-2020-15195?

CVE-2020-15195 is a security vulnerability in Tensorflow involving Heap buffer overflow in Tensorflow under CWE-122: Heap-based Buffer Overflow.

Q: Which Tensorflow versions are affected?

Affected versions include releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.

Q: What version fixes CVE-2020-15195?

Upgrading to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 (or the latest patched release for your respective branch) resolves the vulnerability.

Q: Is tensorflow affected?

Yes, tensorflow is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

Q: Is tensorflow-cpu affected?

Yes, tensorflow-cpu is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

Q: What is the CVSS score of CVE-2020-15195?

CVE-2020-15195 has a CVSS v3.1 base score of 8.5 (MODERATE) with vector string CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:H/I:H/A:H.

Q: What CWE is associated with CVE-2020-15195?

CVE-2020-15195 is classified under CWE-122: Heap-based Buffer Overflow (A heap overflow condition is a buffer overflow, where the buffer that can be overwritten is allocated in the heap portion of memory, generally meaning that the buffer was allocated using a routine such as malloc().).

Q: How can I check PyPI dependencies for CVE-2020-15195?

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.

MODERATE

Advisory Identifier: GHSA-63xm-rx5p-xvqr

CVSS 8.5

CWE-122 CWE-787 CWE-119

Published 2020-09-25

Advisory Summary

CVE-2020-15195 is a MODERATE (CVSS 8.5) heap buffer overflow vulnerability (CWE-122: Heap-based Buffer Overflow) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves heap buffer overflow in tensorflow. Remediation is available by upgrading affected packages to version 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1.

CVE-2020-15195 at a Glance

CVE Identifier CVE-2020-15195

GHSA Identifier GHSA-63xm-rx5p-xvqr

Severity MODERATE

CVSS v3.1 Base Score 8.5

CWE Classification CWE-122: Heap-based Buffer Overflow

Affected Product Tensorflow

Package Ecosystem PyPI

Published Date 2020-09-25

Last Updated 2024-10-28

Security Impact

CVSS: 8.5

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

Attack Vector Network (AV:N)

Attack Complexity High (AC:H)

Privileges Required Low (PR:L)

User Interaction None (UI:N)

Scope Changed (S:C)

Confidentiality High (C:H)

Integrity High (I:H)

Availability High (A:H)

How to Fix CVE-2020-15195

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: 1.15.4

pip install tensorflow>=1.15.4

tensorflow fixed: 2.0.3

pip install tensorflow>=2.0.3

tensorflow fixed: 2.1.2

pip install tensorflow>=2.1.2

tensorflow fixed: 2.2.1

pip install tensorflow>=2.2.1

tensorflow fixed: 2.3.1

pip install tensorflow>=2.3.1

tensorflow-cpu fixed: 1.15.4

pip install tensorflow-cpu>=1.15.4

tensorflow-cpu fixed: 2.0.3

pip install tensorflow-cpu>=2.0.3

tensorflow-cpu fixed: 2.1.2

pip install tensorflow-cpu>=2.1.2

tensorflow-cpu fixed: 2.2.1

pip install tensorflow-cpu>=2.2.1

tensorflow-cpu fixed: 2.3.1

pip install tensorflow-cpu>=2.3.1

tensorflow-gpu fixed: 1.15.4

pip install tensorflow-gpu>=1.15.4

tensorflow-gpu fixed: 2.0.3

pip install tensorflow-gpu>=2.0.3

tensorflow-gpu fixed: 2.1.2

pip install tensorflow-gpu>=2.1.2

tensorflow-gpu fixed: 2.2.1

pip install tensorflow-gpu>=2.2.1

tensorflow-gpu fixed: 2.3.1

pip install tensorflow-gpu>=2.3.1

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-2020-15195, 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 1.15.4), 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-122: Heap-based Buffer Overflow, where a heap overflow condition is a buffer overflow, where the buffer that can be overwritten is allocated in the heap portion of memory, generally meaning that the buffer was allocated using a routine such as malloc(). 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 The implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L263-L269 It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overflow. ### Patches We have patched the issue in 390611e0d45c5793c7066110af37c8514e6a6c54 and will release a patch release for all affected versions. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. ### 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. ### Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

CVE-2020-15195 Affected Tensorflow Versions

15 component(s)

Fixed Versions:

1.15.42.0.32.1.22.2.12.3.1

Product / Package	Ecosystem	Affected Versions	Fixed Version
tensorflow pypi/tensorflow	PyPI	< 1.15.4	1.15.4
tensorflow pypi/tensorflow	PyPI	≥ 2.0.0 < 2.0.3	2.0.3
tensorflow pypi/tensorflow	PyPI	≥ 2.1.0 < 2.1.2	2.1.2
tensorflow pypi/tensorflow	PyPI	≥ 2.2.0 < 2.2.1 All 1 versions 2.2.0	2.2.1
tensorflow pypi/tensorflow	PyPI	≥ 2.3.0 < 2.3.1 All 1 versions 2.3.0	2.3.1
tensorflow-cpu pypi/tensorflow-cpu	PyPI	< 1.15.4	1.15.4
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.0.0 < 2.0.3	2.0.3
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.1.0 < 2.1.2	2.1.2
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.2.0 < 2.2.1 All 1 versions 2.2.0	2.2.1
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.3.0 < 2.3.1 All 1 versions 2.3.0	2.3.1
tensorflow-gpu pypi/tensorflow-gpu	PyPI	< 1.15.4	1.15.4
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.0.0 < 2.0.3	2.0.3
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.1.0 < 2.1.2	2.1.2
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.2.0 < 2.2.1 All 1 versions 2.2.0	2.2.1
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.3.0 < 2.3.1 All 1 versions 2.3.0	2.3.1

References

GitHub Advisory / Wiki ADVISORY

https://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr

NVD — CVE-2020-15195 NVD

https://nvd.nist.gov/vuln/detail/CVE-2020-15195

Security Fix Commit FIX COMMIT

https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-275.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/tensorflow/tensorflow

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-310.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-118.yaml

Tensorflow Release RELEASE

https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1

Tensorflow Security Information SECURITY

http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html

Frequently Asked Questions

What is CVE-2020-15195?

CVE-2020-15195 is a security vulnerability in Tensorflow involving Heap buffer overflow in Tensorflow under CWE-122: Heap-based Buffer Overflow.

Which Tensorflow versions are affected?

Affected versions include releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.

What version fixes CVE-2020-15195?

Upgrading to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 (or the latest patched release for your respective branch) resolves the vulnerability.

Is tensorflow affected?

Yes, tensorflow is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

Is tensorflow-cpu affected?

Yes, tensorflow-cpu is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

What is the CVSS score of CVE-2020-15195?

CVE-2020-15195 has a CVSS v3.1 base score of 8.5 (MODERATE) with vector string CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:H/I:H/A:H.

What CWE is associated with CVE-2020-15195?

CVE-2020-15195 is classified under CWE-122: Heap-based Buffer Overflow (A heap overflow condition is a buffer overflow, where the buffer that can be overwritten is allocated in the heap portion of memory, generally meaning that the buffer was allocated using a routine such as malloc().).

How can I check PyPI dependencies for CVE-2020-15195?

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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CVSS V3

8.5 MODERATE

Attack Vector Network (AV:N)

Attack Complexity High (AC:H)

Privileges Required Low (PR:L)

User Interaction None (UI:N)

Scope Changed (S:C)

Confidentiality High (C:H)

Integrity High (I:H)

Availability High (A:H)

Vector String

CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:H/I:H/A:H

Advisory Metadata

Source ID: GHSA-63xm-rx5p-xvqr
Schema Version: 1.4.0
Published: 2020-09-25 18:28
Last Modified: 2024-10-28 20:21
Database Record File: advisories/github-reviewed/2020/09/GHSA-63xm-rx5p-xvqr/GHSA-63xm-rx5p-xvqr.json