CVE-2021-29538: Tensorflow cpu Division By Zero Vulnerability

LOW
Advisory Identifier: GHSA-j8qc-5fqr-52fp
Share
CVSS 2.5
CWE-369
Published
Advisory Summary

CVE-2021-29538 is a LOW (CVSS 2.5) division by zero vulnerability (CWE-369: Divide By Zero) affecting Tensorflow cpu libraries in the PyPI ecosystem. The issue involves division by zero in `conv2dbackpropfilter`. Remediation is available by upgrading affected packages to version 2.2.3, 2.1.4, 2.3.3, 2.4.2.

CVE-2021-29538 at a Glance

CVE Identifier CVE-2021-29538
GHSA Identifier GHSA-j8qc-5fqr-52fp
Severity LOW
CVSS v3.1 Base Score 2.5
CWE Classification CWE-369: Divide By Zero
Affected Product Tensorflow cpu
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-2021-29538

To remediate this vulnerability, upgrade all affected Tensorflow cpu 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-cpu fixed: 2.2.3 
pip install tensorflow-cpu>=2.2.3
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: 2.3.3 
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-cpu

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2021-29538, inspect your installed packages using pip:

pip show tensorflow-cpu

Analyze the terminal output to locate any version of Tensorflow cpu in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 2.2.3), your application is affected.

Note: The pip show tensorflow-cpu 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 cpu, 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 a division by zero to occur in `Conv2DBackpropFilter`: ```python import tensorflow as tf input_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) filter_sizes = tf.constant([0, 0, 0, 0], shape=[4], dtype=tf.int32) out_backprop = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) tf.raw_ops.Conv2DBackpropFilter( input=input_tensor, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 1, 1, 1], use_cudnn_on_gpu=False, padding='SAME', explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1] ) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments): ```cc const size_t size_A = output_image_size * filter_total_size; const size_t size_B = output_image_size * dims.out_depth; const size_t size_C = filter_total_size * dims.out_depth; const size_t work_unit_size = size_A + size_B + size_C; const size_t shard_size = (target_working_set_size + work_unit_size - 1) / work_unit_size; ``` If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. ### Patches We have patched the issue in GitHub commit [c570e2ecfc822941335ad48f6e10df4e21f11c96](https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96). 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-2021-29538 Affected Tensorflow cpu Versions

12 component(s)
Fixed Versions:
2.2.32.1.42.3.32.4.2
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.2.0
< 2.2.3
2.2.3
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
≥ 2.3.0
< 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-j8qc-5fqr-52fp
NVD — CVE-2021-29538 NVD
https://nvd.nist.gov/vuln/detail/CVE-2021-29538
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96
Tensorflow cpu Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-466.yaml
Tensorflow cpu Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-664.yaml
Tensorflow cpu Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-175.yaml
Tensorflow cpu Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow

Frequently Asked Questions

What is CVE-2021-29538?
CVE-2021-29538 is a security vulnerability in Tensorflow cpu involving Division by zero in `Conv2DBackpropFilter` under CWE-369: Divide By Zero.
Which Tensorflow cpu versions are affected?
Affected versions include releases prior to 2.2.3, 2.1.4, 2.3.3, 2.4.2 across affected packages: tensorflow-cpu, tensorflow, tensorflow-gpu.
What version fixes CVE-2021-29538?
Upgrading to 2.2.3, 2.1.4, 2.3.3, 2.4.2 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.2.3, 2.1.4, 2.3.3, 2.4.2. Upgrade your dependency to 2.2.3, 2.1.4, 2.3.3, 2.4.2 or later.
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.
What is the CVSS score of CVE-2021-29538?
CVE-2021-29538 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-2021-29538?
CVE-2021-29538 is classified under CWE-369: Divide By Zero (The product divides a value by zero.).
How can I check PyPI dependencies for CVE-2021-29538?
Run the dependency tree command: 'pip show tensorflow-cpu'. 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-j8qc-5fqr-52fp",
  "modified" : "2024-10-30T23:26:36Z",
  "published" : "2021-05-21T14:22:38Z",
  "aliases" : [ "CVE-2021-29538" ],
  "summary" : "Division by zero in `Conv2DBackpropFilter`",
  "details" : "### Impact\nAn attacker can cause a division by zero to occur in `Conv2DBackpropFilter`:\n\n```python\nimport tensorflow as tf\n\ninput_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)\nfilter_sizes = tf.constant([0, 0, 0, 0], shape=[4], dtype=tf.int32)\nout_backprop = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)\n\ntf.raw_ops.Conv2DBackpropFilter(\n  input=input_tensor,\n  filter_sizes=filter_sizes,\n  out_backprop=out_backprop,\n  strides=[1, 1, 1, 1],\n  use_cudnn_on_gpu=False,\n  padding='SAME',\n  explicit_paddings=[],\n  data_format='NHWC',\n  dilations=[1, 1, 1, 1]\n)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments):\n\n```cc\nconst size_t size_A = output_image_size * filter_total_size; \nconst size_t size_B = output_image_size * dims.out_depth;\nconst size_t size_C = filter_total_size * dims.out_depth;\nconst size_t work_unit_size = size_A + size_B + size_C;\nconst size_t shard_size = (target_working_set_size + work_unit_size - 1) / work_unit_size;\n```\n\nIf all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. \n\n### Patches\nWe have patched the issue in GitHub commit [c570e2ecfc822941335ad48f6e10df4e21f11c96](https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96).\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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  }, {
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  }, {
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  "references" : [ {
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  }, {
    "type" : "ADVISORY",
    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2021-29538"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96"
  }, {
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  }, {
    "type" : "PACKAGE",
    "url" : "https://github.com/tensorflow/tensorflow"
  } ],
  "database_specific" : {
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    "github_reviewed" : true,
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    "nvd_published_at" : "2021-05-14T20:15:00Z"
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}