CVE-2022-41887: Tensorflow gpu Overflow Vulnerability

MODERATE
Advisory Identifier: GHSA-8fvv-46hw-vpg3
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CVSS 4.8
CWE-131
Published
Advisory Summary

CVE-2022-41887 is a MODERATE (CVSS 4.8) overflow vulnerability (CWE-131: Incorrect Calculation of Buffer Size) affecting Tensorflow gpu libraries in the PyPI ecosystem. The issue involves overflow in `tf.keras.losses.poisson`. Remediation is available by upgrading affected packages to version 2.10.1, 2.9.3.

CVE-2022-41887 at a Glance

CVE Identifier CVE-2022-41887
GHSA Identifier GHSA-8fvv-46hw-vpg3
Severity MODERATE
CVSS v3.1 Base Score 4.8
CWE Classification CWE-131: Incorrect Calculation of Buffer Size
Affected Product Tensorflow gpu
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 4.8

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

Attack Vector Network (AV:N)
Attack Complexity High (AC:H)
Privileges Required Low (PR:L)
User Interaction Required (UI:R)
Scope Unchanged (S:U)
Confidentiality None (C:N)
Integrity None (I:N)
Availability High (A:H)

How to Fix CVE-2022-41887

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: 2.10.1 
pip install tensorflow-gpu>=2.10.1
tensorflow fixed: 2.10.1 
pip install tensorflow>=2.10.1
tensorflow fixed: 2.9.3 
pip install tensorflow>=2.9.3
tensorflow-cpu fixed: 2.9.3 
pip install tensorflow-cpu>=2.9.3
tensorflow-gpu fixed: 2.9.3 
pip install tensorflow-gpu>=2.9.3
tensorflow-cpu fixed: 2.10.1 
pip install tensorflow-cpu>=2.10.1
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-gpu

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2022-41887, 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 2.10.1), 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-131: Incorrect Calculation of Buffer Size, where the product does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow. 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 [`tf.keras.losses.poisson`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/losses.py) receives a `y_pred` and `y_true` that are passed through `functor::mul` in [`BinaryOp`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cwise_ops_common.h). If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. ```python import numpy as np import tensorflow as tf true_value = tf.reshape(shape=[1, 2500000000], tensor = tf.zeros(dtype=tf.bool, shape=[50000, 50000])) pred_value = np.array([[[-2]], [[8]]], dtype = np.float64) tf.keras.losses.poisson(y_true=true_value,y_pred=pred_value) ``` ### Patches We have patched the issue in GitHub commit [c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c](https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c). The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9. ### 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 Pattarakrit Rattankul.

CVE-2022-41887 Affected Tensorflow gpu Versions

6 component(s)
Fixed Versions:
2.10.12.9.3
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.10.0
< 2.10.1
2.10.1
tensorflow
pypi/tensorflow
PyPI
≥ 2.10.0
< 2.10.1
2.10.1
tensorflow
pypi/tensorflow
PyPI
< 2.9.3
2.9.3
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
< 2.9.3
2.9.3
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
< 2.9.3
2.9.3
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.10.0
< 2.10.1
2.10.1

References

GitHub Advisory / Wiki ADVISORY
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fvv-46hw-vpg3
NVD — CVE-2022-41887 NVD
https://nvd.nist.gov/vuln/detail/CVE-2022-41887
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c
Tensorflow gpu Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow
Tensorflow gpu Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cwise_ops_common.h
Tensorflow gpu Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/losses.py

Frequently Asked Questions

What is CVE-2022-41887?
CVE-2022-41887 is a security vulnerability in Tensorflow gpu involving Overflow in `tf.keras.losses.poisson` under CWE-131: Incorrect Calculation of Buffer Size.
Which Tensorflow gpu versions are affected?
Affected versions include releases prior to 2.10.1, 2.9.3 across affected packages: tensorflow-gpu, tensorflow, tensorflow-cpu.
What version fixes CVE-2022-41887?
Upgrading to 2.10.1, 2.9.3 (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 2.10.1, 2.9.3. Upgrade your dependency to 2.10.1, 2.9.3 or later.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 2.10.1, 2.9.3. Upgrade your dependency to 2.10.1, 2.9.3 or later.
What is the CVSS score of CVE-2022-41887?
CVE-2022-41887 has a CVSS v3.1 base score of 4.8 (MODERATE) with vector string CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H.
What CWE is associated with CVE-2022-41887?
CVE-2022-41887 is classified under CWE-131: Incorrect Calculation of Buffer Size (The product does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow.).
How can I check PyPI dependencies for CVE-2022-41887?
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 
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  "schema_version" : "1.4.0",
  "id" : "GHSA-8fvv-46hw-vpg3",
  "modified" : "2022-11-21T20:41:35Z",
  "published" : "2022-11-21T20:41:35Z",
  "aliases" : [ "CVE-2022-41887" ],
  "summary" : "Overflow in `tf.keras.losses.poisson`",
  "details" : "### Impact\n[`tf.keras.losses.poisson`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/losses.py) receives a `y_pred` and `y_true` that are passed through `functor::mul` in [`BinaryOp`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cwise_ops_common.h). If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment.\n```python\nimport numpy as np\nimport tensorflow as tf\n\ntrue_value = tf.reshape(shape=[1, 2500000000], tensor = tf.zeros(dtype=tf.bool, shape=[50000, 50000]))\npred_value = np.array([[[-2]], [[8]]], dtype = np.float64)\n\ntf.keras.losses.poisson(y_true=true_value,y_pred=pred_value)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c](https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c).\n\nThe fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.\n\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\n### Attribution\nThis vulnerability has been reported by Pattarakrit Rattankul.\n",
  "severity" : [ {
    "type" : "CVSS_V3",
    "score" : "CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H"
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    "type" : "ADVISORY",
    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2022-41887"
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  "database_specific" : {
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}