CVE-2022-29197: Tensorflow Denial of Service Vulnerability

MODERATE
Advisory Identifier: GHSA-hrg5-737c-2p56
CVSS 5.5
CWE-20
Published
Advisory Summary

CVE-2022-29197 is a MODERATE (CVSS 5.5) denial of service vulnerability (CWE-20: Improper Input Validation) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves missing validation causes denial of service via `unsortedsegmentjoin`. Remediation is available by upgrading affected packages to version 2.6.4, 2.7.2, 2.8.1.

CVE-2022-29197 at a Glance

CVE Identifier CVE-2022-29197
GHSA Identifier GHSA-hrg5-737c-2p56
Severity MODERATE
CVSS v3.1 Base Score 5.5
CWE Classification CWE-20: Improper Input Validation
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

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-2022-29197

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: 2.6.4
pip install tensorflow>=2.6.4
tensorflow fixed: 2.7.2
pip install tensorflow>=2.7.2
tensorflow fixed: 2.8.1
pip install tensorflow>=2.8.1
tensorflow-cpu fixed: 2.6.4
pip install tensorflow-cpu>=2.6.4
tensorflow-cpu fixed: 2.7.2
pip install tensorflow-cpu>=2.7.2
tensorflow-cpu fixed: 2.8.1
pip install tensorflow-cpu>=2.8.1
tensorflow-gpu fixed: 2.6.4
pip install tensorflow-gpu>=2.6.4
tensorflow-gpu fixed: 2.7.2
pip install tensorflow-gpu>=2.7.2
tensorflow-gpu fixed: 2.8.1
pip install tensorflow-gpu>=2.8.1
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-2022-29197, 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 2.6.4), 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-20: Improper Input Validation, where the product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly. 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 implementation of [`tf.raw_ops.UnsortedSegmentJoin`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L95) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf tf.raw_ops.UnsortedSegmentJoin( inputs=tf.constant("this", shape=[12], dtype=tf.string), segment_ids=tf.constant(0, shape=[12], dtype=tf.int64), num_segments=tf.constant(0, shape=[12], dtype=tf.int64)) ``` The code assumes `num_segments` is a scalar but there is no validation for this before accessing its value: ```cc const Tensor& num_segments_tensor = context->input(2); OP_REQUIRES(context, num_segments_tensor.NumElements() != 0, errors::InvalidArgument("Number of segments cannot be empty.")); auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()(); ``` ### Patches We have patched the issue in GitHub commit [13d38a07ce9143e044aa737cfd7bb759d0e9b400](https://github.com/tensorflow/tensorflow/commit/13d38a07ce9143e044aa737cfd7bb759d0e9b400). The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.

CVE-2022-29197 Affected Tensorflow Versions

9 component(s)
Fixed Versions:
2.6.42.7.22.8.1
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
PyPI
< 2.6.4
2.6.4
tensorflow
PyPI
≥ 2.7.0
< 2.7.2
2.7.2
tensorflow
PyPI
≥ 2.8.0
< 2.8.1
2.8.1
tensorflow-cpu
PyPI
< 2.6.4
2.6.4
tensorflow-cpu
PyPI
≥ 2.7.0
< 2.7.2
2.7.2
tensorflow-cpu
PyPI
≥ 2.8.0
< 2.8.1
2.8.1
tensorflow-gpu
PyPI
< 2.6.4
2.6.4
tensorflow-gpu
PyPI
≥ 2.7.0
< 2.7.2
2.7.2
tensorflow-gpu
PyPI
≥ 2.8.0
< 2.8.1
2.8.1

References

Frequently Asked Questions

What is CVE-2022-29197?
CVE-2022-29197 is a security vulnerability in Tensorflow involving Missing validation causes denial of service via `UnsortedSegmentJoin` under CWE-20: Improper Input Validation.
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.6.4, 2.7.2, 2.8.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2022-29197?
Upgrading to 2.6.4, 2.7.2, 2.8.1 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 2.6.4, 2.7.2, 2.8.1. Upgrade your dependency to 2.6.4, 2.7.2, 2.8.1 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.6.4, 2.7.2, 2.8.1. Upgrade your dependency to 2.6.4, 2.7.2, 2.8.1 or later.
What is the CVSS score of CVE-2022-29197?
CVE-2022-29197 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-2022-29197?
CVE-2022-29197 is classified under CWE-20: Improper Input Validation (The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.).
How can I check PyPI dependencies for CVE-2022-29197?
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
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  "summary" : "Missing validation causes denial of service via `UnsortedSegmentJoin`",
  "details" : "### Impact\nThe implementation of [`tf.raw_ops.UnsortedSegmentJoin`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L95) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack:\n\n```python\nimport tensorflow as tf\n\ntf.raw_ops.UnsortedSegmentJoin(\n  inputs=tf.constant(\"this\", shape=[12], dtype=tf.string),\n  segment_ids=tf.constant(0, shape=[12], dtype=tf.int64),\n  num_segments=tf.constant(0, shape=[12], dtype=tf.int64))\n``` \n  \nThe code assumes `num_segments` is a scalar but there is no validation for this before accessing its value:\n\n```cc\nconst Tensor& num_segments_tensor = context->input(2);\nOP_REQUIRES(context, num_segments_tensor.NumElements() != 0,\n            errors::InvalidArgument(\"Number of segments cannot be empty.\"));\nauto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();\n``` \n\n### Patches\nWe have patched the issue in GitHub commit [13d38a07ce9143e044aa737cfd7bb759d0e9b400](https://github.com/tensorflow/tensorflow/commit/13d38a07ce9143e044aa737cfd7bb759d0e9b400).\n\nThe fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.\n",
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