CVE-2021-29575: Tensorflow gpu Denial of Service Vulnerability

LOW
Advisory Identifier: GHSA-6qgm-fv6v-rfpv
Share
CVSS 2.5
CWE-120 CWE-787 CWE-119
Published
Advisory Summary

CVE-2021-29575 is a LOW (CVSS 2.5) denial of service vulnerability (CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')) affecting Tensorflow gpu libraries in the PyPI ecosystem. The issue involves overflow/denial of service in `tf.raw_ops.reversesequence`. Remediation is available by upgrading affected packages to version 2.3.3, 2.1.4, 2.2.3, 2.4.2.

CVE-2021-29575 at a Glance

CVE Identifier CVE-2021-29575
GHSA Identifier GHSA-6qgm-fv6v-rfpv
Severity LOW
CVSS v3.1 Base Score 2.5
CWE Classification CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')
Affected Product Tensorflow gpu
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-2021-29575

To remediate this vulnerability, upgrade all affected Tensorflow gpu 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-gpu fixed: 2.3.3 
pip install tensorflow-gpu>=2.3.3
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: 2.2.3 
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: 2.4.2 
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-gpu

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2021-29575, inspect your installed packages using pip:

pip show tensorflow-gpu

Analyze the terminal output to locate any version of Tensorflow gpu in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 2.3.3), your application is affected.

Note: The pip show tensorflow-gpu command checks currently installed packages in your active Python environment.

Technical Details

Vulnerability Mechanism & Technical Analysis

This vulnerability is classified under CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow'), where the product copies an input buffer to an output buffer without verifying that the size of the input buffer is less than the size of the output buffer. In Tensorflow gpu, 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 `tf.raw_ops.ReverseSequence` allows for stack overflow and/or `CHECK`-fail based denial of service. ```python import tensorflow as tf input = tf.zeros([1, 1, 1], dtype=tf.int32) seq_lengths = tf.constant([0], shape=[1], dtype=tf.int32) tf.raw_ops.ReverseSequence( input=input, seq_lengths=seq_lengths, seq_dim=-2, batch_dim=0) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/5b3b071975e01f0d250c928b2a8f901cd53b90a7/tensorflow/core/kernels/reverse_sequence_op.cc#L114-L118) fails to validate that `seq_dim` and `batch_dim` arguments are valid. Negative values for `seq_dim` can result in stack overflow or `CHECK`-failure, depending on the version of Eigen code used to implement the operation. Similar behavior can be exhibited by invalid values of `batch_dim`. ### Patches We have patched the issue in GitHub commit [ecf768cbe50cedc0a45ce1ee223146a3d3d26d23](https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23). 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 Ying Wang and Yakun Zhang of Baidu X-Team.

CVE-2021-29575 Affected Tensorflow gpu Versions

12 component(s)
Fixed Versions:
2.3.32.1.42.2.32.4.2
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.3.0
< 2.3.3
2.3.3
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
≥ 2.2.0
< 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
≥ 2.4.0
< 2.4.2
2.4.2

References

GitHub Advisory / Wiki ADVISORY
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6qgm-fv6v-rfpv
NVD — CVE-2021-29575 NVD
https://nvd.nist.gov/vuln/detail/CVE-2021-29575
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23
Tensorflow gpu Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-503.yaml
Tensorflow gpu Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-701.yaml
Tensorflow gpu Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-212.yaml
Tensorflow gpu Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow

Frequently Asked Questions

What is CVE-2021-29575?
CVE-2021-29575 is a security vulnerability in Tensorflow gpu involving Overflow/denial of service in `tf.raw_ops.ReverseSequence` under CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow').
Which Tensorflow gpu versions are affected?
Affected versions include releases prior to 2.3.3, 2.1.4, 2.2.3, 2.4.2 across affected packages: tensorflow-gpu, tensorflow, tensorflow-cpu.
What version fixes CVE-2021-29575?
Upgrading to 2.3.3, 2.1.4, 2.2.3, 2.4.2 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow-gpu affected?
Yes, tensorflow-gpu is affected in releases prior to 2.3.3, 2.1.4, 2.2.3, 2.4.2. Upgrade your dependency to 2.3.3, 2.1.4, 2.2.3, 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-2021-29575?
CVE-2021-29575 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-2021-29575?
CVE-2021-29575 is classified under CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow') (The product copies an input buffer to an output buffer without verifying that the size of the input buffer is less than the size of the output buffer.).
How can I check PyPI dependencies for CVE-2021-29575?
Run the dependency tree command: 'pip show tensorflow-gpu'. 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-6qgm-fv6v-rfpv",
  "modified" : "2024-11-01T17:10:29Z",
  "published" : "2021-05-21T14:26:13Z",
  "aliases" : [ "CVE-2021-29575" ],
  "summary" : "Overflow/denial of service in `tf.raw_ops.ReverseSequence`",
  "details" : "### Impact\nThe implementation of `tf.raw_ops.ReverseSequence` allows for stack overflow and/or `CHECK`-fail based denial of service.\n\n```python\nimport tensorflow as tf\n\ninput = tf.zeros([1, 1, 1], dtype=tf.int32)\nseq_lengths = tf.constant([0], shape=[1], dtype=tf.int32)\n\ntf.raw_ops.ReverseSequence(\n    input=input, seq_lengths=seq_lengths, seq_dim=-2, batch_dim=0)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/5b3b071975e01f0d250c928b2a8f901cd53b90a7/tensorflow/core/kernels/reverse_sequence_op.cc#L114-L118) fails to validate that `seq_dim` and `batch_dim` arguments are valid.\n  \nNegative values for `seq_dim` can result in stack overflow or `CHECK`-failure, depending on the version of Eigen code used to implement the operation. Similar behavior can be exhibited by invalid values of `batch_dim`.\n  \n### Patches\nWe have patched the issue in GitHub commit [ecf768cbe50cedc0a45ce1ee223146a3d3d26d23](https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23).\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 Ying Wang and Yakun Zhang of Baidu X-Team.",
  "severity" : [ {
    "type" : "CVSS_V3",
    "score" : "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L"
  }, {
    "type" : "CVSS_V4",
    "score" : "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N"
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  }, {
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  }, {
    "type" : "WEB",
    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-701.yaml"
  }, {
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    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-212.yaml"
  }, {
    "type" : "PACKAGE",
    "url" : "https://github.com/tensorflow/tensorflow"
  } ],
  "database_specific" : {
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    "severity" : "LOW",
    "github_reviewed" : true,
    "github_reviewed_at" : "2021-05-18T18:28:23Z",
    "nvd_published_at" : "2021-05-14T20:15:00Z"
  }
}