CVE-2021-29517: Tensorflow Division By Zero Vulnerability

LOW
Advisory Identifier: GHSA-772p-x54p-hjrv
Share
CVSS 2.5
CWE-369
Published
Advisory Summary

CVE-2021-29517 is a LOW (CVSS 2.5) division by zero vulnerability (CWE-369: Divide By Zero) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves division by zero in `conv3d`. Remediation is available by upgrading affected packages to version 2.1.4, 2.2.3, 2.3.3, 2.4.2.

CVE-2021-29517 at a Glance

CVE Identifier CVE-2021-29517
GHSA Identifier GHSA-772p-x54p-hjrv
Severity LOW
CVSS v3.1 Base Score 2.5
CWE Classification CWE-369: Divide By Zero
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 2.5

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

Attack Vector Local (AV:L)
Attack Complexity High (AC:H)
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-2021-29517

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: 2.1.4 
pip install tensorflow>=2.1.4
tensorflow fixed: 2.2.3 
pip install tensorflow>=2.2.3
tensorflow fixed: 2.3.3 
pip install tensorflow>=2.3.3
tensorflow fixed: 2.4.2 
pip install tensorflow>=2.4.2
tensorflow-cpu fixed: 2.1.4 
pip install tensorflow-cpu>=2.1.4
tensorflow-cpu fixed: 2.2.3 
pip install tensorflow-cpu>=2.2.3
tensorflow-cpu fixed: 2.3.3 
pip install tensorflow-cpu>=2.3.3
tensorflow-cpu fixed: 2.4.2 
pip install tensorflow-cpu>=2.4.2
tensorflow-gpu fixed: 2.1.4 
pip install tensorflow-gpu>=2.1.4
tensorflow-gpu fixed: 2.2.3 
pip install tensorflow-gpu>=2.2.3
tensorflow-gpu fixed: 2.3.3 
pip install tensorflow-gpu>=2.3.3
tensorflow-gpu fixed: 2.4.2 
pip install tensorflow-gpu>=2.4.2
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-2021-29517, 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 2.1.4), 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-369: Divide By Zero, where the product divides a value by zero. 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 A malicious user could trigger a division by 0 in `Conv3D` implementation: ```python import tensorflow as tf input_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32) filter_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32) tf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 56, 56, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 23, 1]) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input: ```cc const int64 out_depth = filter.dim_size(4); OP_REQUIRES(context, in_depth % filter_depth == 0, ...); ``` Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash: ```python import tensorflow as tf input_tensor = tf.constant([], shape=[2, 2, 2, 2, 0], dtype=tf.float32) filter_tensor = tf.constant([], shape=[0, 0, 2, 6, 2], dtype=tf.float32) tf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 39, 34, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 1, 1]) ``` The shape of the two tensors must follow the constraints specified in the [op description](https://www.tensorflow.org/api_docs/python/tf/raw_ops/Conv3D). ### Patches We have patched the issue in GitHub commit [799f835a3dfa00a4d852defa29b15841eea9d64f](https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.

CVE-2021-29517 Affected Tensorflow Versions

12 component(s)
Fixed Versions:
2.1.42.2.32.3.32.4.2
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
pypi/tensorflow
PyPI
< 2.1.4
2.1.4
tensorflow
pypi/tensorflow
PyPI
≥ 2.2.0
< 2.2.3
2.2.3
tensorflow
pypi/tensorflow
PyPI
≥ 2.3.0
< 2.3.3
2.3.3
tensorflow
pypi/tensorflow
PyPI
≥ 2.4.0
< 2.4.2
2.4.2
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
< 2.1.4
2.1.4
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.2.0
< 2.2.3
2.2.3
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.3.0
< 2.3.3
2.3.3
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.4.0
< 2.4.2
2.4.2
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
< 2.1.4
2.1.4
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.2.0
< 2.2.3
2.2.3
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.3.0
< 2.3.3
2.3.3
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.4.0
< 2.4.2
2.4.2

References

GitHub Advisory / Wiki ADVISORY
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv
NVD — CVE-2021-29517 NVD
https://nvd.nist.gov/vuln/detail/CVE-2021-29517
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-445.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-643.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-154.yaml

Frequently Asked Questions

What is CVE-2021-29517?
CVE-2021-29517 is a security vulnerability in Tensorflow involving Division by zero in `Conv3D` under CWE-369: Divide By Zero.
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2021-29517?
Upgrading to 2.1.4, 2.2.3, 2.3.3, 2.4.2 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade your dependency to 2.1.4, 2.2.3, 2.3.3, 2.4.2 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade your dependency to 2.1.4, 2.2.3, 2.3.3, 2.4.2 or later.
What is the CVSS score of CVE-2021-29517?
CVE-2021-29517 has a CVSS v3.1 base score of 2.5 (LOW) with vector string CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L.
What CWE is associated with CVE-2021-29517?
CVE-2021-29517 is classified under CWE-369: Divide By Zero (The product divides a value by zero.).
How can I check PyPI dependencies for CVE-2021-29517?
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 
{
  "schema_version" : "1.4.0",
  "id" : "GHSA-772p-x54p-hjrv",
  "modified" : "2024-10-28T21:26:22Z",
  "published" : "2021-05-21T14:21:01Z",
  "aliases" : [ "CVE-2021-29517" ],
  "summary" : "Division by zero in `Conv3D`",
  "details" : "### Impact\nA malicious user could trigger a division by 0 in `Conv3D` implementation:\n\n```python\nimport tensorflow as tf\n\ninput_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)\nfilter_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)\n\ntf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 56, 56, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 23, 1])\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input:\n\n```cc\n  const int64 out_depth = filter.dim_size(4);\n  OP_REQUIRES(context, in_depth % filter_depth == 0, ...);\n```\n\nThus, when `filter` has a 0 as the fifth element, this results in a division by 0.\n\nAdditionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash:\n\n```python\nimport tensorflow as tf\n\ninput_tensor = tf.constant([], shape=[2, 2, 2, 2, 0], dtype=tf.float32)\nfilter_tensor = tf.constant([], shape=[0, 0, 2, 6, 2], dtype=tf.float32)\n\ntf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 39, 34, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])\n```\n\nThe shape of the two tensors must follow the constraints specified in the [op description](https://www.tensorflow.org/api_docs/python/tf/raw_ops/Conv3D).\n\n### Patches\nWe have patched the issue in GitHub commit [799f835a3dfa00a4d852defa29b15841eea9d64f](https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f).\n\nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.",
  "severity" : [ {
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    "score" : "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L"
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    "url" : "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv"
  }, {
    "type" : "ADVISORY",
    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2021-29517"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f"
  }, {
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    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-445.yaml"
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    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-643.yaml"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-154.yaml"
  } ],
  "database_specific" : {
    "cwe_ids" : [ "CWE-369" ],
    "severity" : "LOW",
    "github_reviewed" : true,
    "github_reviewed_at" : "2021-05-18T23:32:11Z",
    "nvd_published_at" : "2021-05-14T20:15:00Z"
  }
}