CVE-2021-37669: Tensorflow Crash Vulnerability

Q: What is CVE-2021-37669?

CVE-2021-37669 is a security vulnerability in Tensorflow involving Crash in NMS ops caused by integer conversion to unsigned under CWE-681: Incorrect Conversion between Numeric Types.

Q: Which Tensorflow versions are affected?

Affected versions include releases prior to 2.3.4, 2.4.3, 2.5.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.

Q: What version fixes CVE-2021-37669?

Upgrading to 2.3.4, 2.4.3, 2.5.1 (or the latest patched release for your respective branch) resolves the vulnerability.

Q: Is tensorflow affected?

Yes, tensorflow is affected in releases prior to 2.3.4, 2.4.3, 2.5.1. Upgrade your dependency to 2.3.4, 2.4.3, 2.5.1 or later.

Q: Is tensorflow-cpu affected?

Yes, tensorflow-cpu is affected in releases prior to 2.3.4, 2.4.3, 2.5.1. Upgrade your dependency to 2.3.4, 2.4.3, 2.5.1 or later.

Q: What is the CVSS score of CVE-2021-37669?

CVE-2021-37669 has a CVSS v3.1 base score of 5.5 (MODERATE) with vector string CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H.

Q: What CWE is associated with CVE-2021-37669?

CVE-2021-37669 is classified under CWE-681: Incorrect Conversion between Numeric Types (When converting from one data type to another, such as long to integer, data can be omitted or translated in a way that produces unexpected values. If the resulting values are used in a sensitive context, then dangerous behaviors may occur.).

Q: How can I check PyPI dependencies for CVE-2021-37669?

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-vmjw-c2vp-p33c

CVSS 5.5

CWE-681

Published 2021-08-25

Advisory Summary

CVE-2021-37669 is a MODERATE (CVSS 5.5) crash vulnerability (CWE-681: Incorrect Conversion between Numeric Types) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves crash in nms ops caused by integer conversion to unsigned. Remediation is available by upgrading affected packages to version 2.3.4, 2.4.3, 2.5.1.

CVE-2021-37669 at a Glance

CVE Identifier CVE-2021-37669

GHSA Identifier GHSA-vmjw-c2vp-p33c

Severity MODERATE

CVSS v3.1 Base Score 5.5

CWE Classification CWE-681: Incorrect Conversion between Numeric Types

Affected Product Tensorflow

Package Ecosystem PyPI

Published Date 2021-08-25

Last Updated 2024-11-13

Security Impact

CVSS: 5.5

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:N/A:H).

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 None (I:N)

Availability High (A:H)

How to Fix CVE-2021-37669

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: 2.3.4

pip install tensorflow>=2.3.4

tensorflow fixed: 2.4.3

pip install tensorflow>=2.4.3

tensorflow fixed: 2.5.1

pip install tensorflow>=2.5.1

tensorflow-cpu fixed: 2.3.4

pip install tensorflow-cpu>=2.3.4

tensorflow-cpu fixed: 2.4.3

pip install tensorflow-cpu>=2.4.3

tensorflow-cpu fixed: 2.5.1

pip install tensorflow-cpu>=2.5.1

tensorflow-gpu fixed: 2.3.4

pip install tensorflow-gpu>=2.3.4

tensorflow-gpu fixed: 2.4.3

pip install tensorflow-gpu>=2.4.3

tensorflow-gpu fixed: 2.5.1

pip install tensorflow-gpu>=2.5.1

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-2021-37669, 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 2.3.4), 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-681: Incorrect Conversion between Numeric Types, where when converting from one data type to another, such as long to integer, data can be omitted or translated in a way that produces unexpected values. if the resulting values are used in a sensitive context, then dangerous behaviors may occur. 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 An attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0: ```python import tensorflow as tf tf.raw_ops.NonMaxSuppressionV5( boxes=[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]], scores=[1.0,2.0,3.0], max_output_size=-1, iou_threshold=0.5, score_threshold=0.5, soft_nms_sigma=1.0, pad_to_max_output_size=True) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`: ```cc const int output_size = max_output_size.scalar<int>()(); // ... std::vector<int> selected; // ... if (pad_to_max_output_size) { selected.resize(output_size, 0); // ... } ``` However, as `std::vector::resize` takes the size argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to usigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in `CombinedNonMaxSuppression`: ```python import tensorflow as tf tf.raw_ops.NonMaxSuppressionV5( boxes=[[[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]]]], scores=[[[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]]], max_output_size_per_class=-1, max_total_size=10, iou_threshold=score_threshold=0.5, pad_per_class=True, clip_boxes=True) ``` ### Patches We have patched the issue in GitHub commit [3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d](https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d) and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58](https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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. ### Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

CVE-2021-37669 Affected Tensorflow Versions

9 component(s)

Fixed Versions:

2.3.42.4.32.5.1

Product / Package	Ecosystem	Affected Versions	Fixed Version
tensorflow pypi/tensorflow	PyPI	< 2.3.4	2.3.4
tensorflow pypi/tensorflow	PyPI	≥ 2.4.0 < 2.4.3	2.4.3
tensorflow pypi/tensorflow	PyPI	≥ 2.5.0 < 2.5.1 All 1 versions 2.5.0	2.5.1
tensorflow-cpu pypi/tensorflow-cpu	PyPI	< 2.3.4	2.3.4
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.4.0 < 2.4.3	2.4.3
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.5.0 < 2.5.1 All 1 versions 2.5.0	2.5.1
tensorflow-gpu pypi/tensorflow-gpu	PyPI	< 2.3.4	2.3.4
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.4.0 < 2.4.3	2.4.3
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.5.0 < 2.5.1 All 1 versions 2.5.0	2.5.1

References

GitHub Advisory / Wiki ADVISORY

https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vmjw-c2vp-p33c

NVD — CVE-2021-37669 NVD

https://nvd.nist.gov/vuln/detail/CVE-2021-37669

Security Fix Commit FIX COMMIT

https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d

Security Fix Commit FIX COMMIT

https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-582.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-780.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-291.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/tensorflow/tensorflow

Frequently Asked Questions

What is CVE-2021-37669?

CVE-2021-37669 is a security vulnerability in Tensorflow involving Crash in NMS ops caused by integer conversion to unsigned under CWE-681: Incorrect Conversion between Numeric Types.

Which Tensorflow versions are affected?

Affected versions include releases prior to 2.3.4, 2.4.3, 2.5.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.

What version fixes CVE-2021-37669?

Upgrading to 2.3.4, 2.4.3, 2.5.1 (or the latest patched release for your respective branch) resolves the vulnerability.

Is tensorflow affected?

Yes, tensorflow is affected in releases prior to 2.3.4, 2.4.3, 2.5.1. Upgrade your dependency to 2.3.4, 2.4.3, 2.5.1 or later.

Is tensorflow-cpu affected?

Yes, tensorflow-cpu is affected in releases prior to 2.3.4, 2.4.3, 2.5.1. Upgrade your dependency to 2.3.4, 2.4.3, 2.5.1 or later.

What is the CVSS score of CVE-2021-37669?

CVE-2021-37669 has a CVSS v3.1 base score of 5.5 (MODERATE) with vector string CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H.

What CWE is associated with CVE-2021-37669?

CVE-2021-37669 is classified under CWE-681: Incorrect Conversion between Numeric Types (When converting from one data type to another, such as long to integer, data can be omitted or translated in a way that produces unexpected values. If the resulting values are used in a sensitive context, then dangerous behaviors may occur.).

How can I check PyPI dependencies for CVE-2021-37669?

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-vmjw-c2vp-p33c",
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  "aliases" : [ "CVE-2021-37669" ],
  "summary" : "Crash in NMS ops caused by integer conversion to unsigned",
  "details" : "### Impact\nAn attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0:\n\n```python\nimport tensorflow as tf\n\ntf.raw_ops.NonMaxSuppressionV5(\n  boxes=[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],\n  scores=[1.0,2.0,3.0],\n  max_output_size=-1,\n  iou_threshold=0.5,\n  score_threshold=0.5,\n  soft_nms_sigma=1.0,\n  pad_to_max_output_size=True)\n```\n  \nThe [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`:\n\n```cc\n  const int output_size = max_output_size.scalar<int>()();\n  // ...\n  std::vector<int> selected;\n  // ...\n  if (pad_to_max_output_size) {\n    selected.resize(output_size, 0);\n    // ...\n  }\n```\n    \nHowever, as `std::vector::resize` takes the size argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to usigned. If the attacker supplies a negative value, this conversion results in a crash.\n\nA similar issue occurs in `CombinedNonMaxSuppression`:\n\n```python\nimport tensorflow as tf\n\ntf.raw_ops.NonMaxSuppressionV5(\n  boxes=[[[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]]]],\n  scores=[[[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]]],\n  max_output_size_per_class=-1,\n  max_total_size=10,\n  iou_threshold=score_threshold=0.5,\n  pad_per_class=True,\n  clip_boxes=True)\n```\n  \n### Patches\nWe have patched the issue in GitHub commit [3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d](https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d) and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58](https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58).\n\nThe fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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.\n\n### Attribution\nThis vulnerability has been reported by members of the Aivul Team from Qihoo 360.",
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}

CVSS V3

5.5 MODERATE

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 None (I:N)

Availability High (A:H)

Vector String

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

Advisory Metadata

Source ID: GHSA-vmjw-c2vp-p33c
Schema Version: 1.4.0
Published: 2021-08-25 14:42
Last Modified: 2024-11-13 21:00
Database Record File: advisories/github-reviewed/2021/08/GHSA-vmjw-c2vp-p33c/GHSA-vmjw-c2vp-p33c.json