CVE-2020-15201: Tensorflow Heap Buffer Overflow Vulnerability

MODERATE
Advisory Identifier: GHSA-p5f8-gfw5-33w4
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CVSS 4.8
CWE-122 CWE-787 CWE-20
Published
Advisory Summary

CVE-2020-15201 is a MODERATE (CVSS 4.8) 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 2.3.1.

CVE-2020-15201 at a Glance

CVE Identifier CVE-2020-15201
GHSA Identifier GHSA-p5f8-gfw5-33w4
Severity MODERATE
CVSS v3.1 Base Score 4.8
CWE Classification CWE-122: Heap-based Buffer Overflow
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 4.8

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

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

How to Fix CVE-2020-15201

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: 2.3.1 
pip install tensorflow>=2.3.1
tensorflow-cpu fixed: 2.3.1 
pip install tensorflow-cpu>=2.3.1
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-2020-15201, 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 2.3.1), 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 `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, this code is prone to heap buffer overflow: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L248-L251 If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. ### Patches We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release. We recommend users to upgrade to TensorFlow 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-2020-15201 Affected Tensorflow Versions

3 component(s)
Fixed Versions:
2.3.1
Product / Package Ecosystem Affected Versions Fixed Version
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
≥ 2.3.0
< 2.3.1
All 1 versions
2.3.0
2.3.1
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-p5f8-gfw5-33w4
NVD — CVE-2020-15201 NVD
https://nvd.nist.gov/vuln/detail/CVE-2020-15201
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-281.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-316.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-124.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow
Tensorflow Release RELEASE
https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1

Frequently Asked Questions

What is CVE-2020-15201?
CVE-2020-15201 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 2.3.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2020-15201?
Upgrading to 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 2.3.1. Upgrade your dependency to 2.3.1 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.3.1. Upgrade your dependency to 2.3.1 or later.
What is the CVSS score of CVE-2020-15201?
CVE-2020-15201 has a CVSS v3.1 base score of 4.8 (MODERATE) with vector string CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N.
What CWE is associated with CVE-2020-15201?
CVE-2020-15201 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-2020-15201?
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-p5f8-gfw5-33w4",
  "modified" : "2024-10-28T21:20:25Z",
  "published" : "2020-09-25T18:28:34Z",
  "aliases" : [ "CVE-2020-15201" ],
  "summary" : "Heap buffer overflow in Tensorflow",
  "details" : "### Impact\nThe `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, this code is prone to heap buffer overflow:\nhttps://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L248-L251\n\nIf `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. \n\n### Patches\nWe have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.\n\nWe recommend users to upgrade to TensorFlow 2.3.1.\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.\n\n### Attribution\nThis vulnerability has been reported by members of the Aivul Team from Qihoo 360.",
  "severity" : [ {
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  }, {
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  "database_specific" : {
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    "severity" : "MODERATE",
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}