GitHub Advisoryadvisory · PyPI

GHSA-wp3c-xw9g-gpcg Lack of validation in `SparseDenseCwiseMul`

LOW Published 2021-05-21 Modified 2024-11-01 Official page ↗
Aliases
CVE-2021-29567
Ecosystem
PyPI
Packages
tensorflow, tensorflow-cpu, tensorflow-gpu
Severity
Low
CVSS vector
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Weaknesses
CWE-617
Reviewed
2021-05-18

Affected packages

• PyPI tensorflow: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-cpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-cpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-cpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-cpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-gpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-gpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-gpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-gpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2

Description

Impact Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data: ```python import tensorflow as tf indices = tf.constant([], shape=[10, 0], dtype=tf.int64) values = tf.constant([], shape=[0], dtype=tf.int64) shape = tf.constant([0, 0], shape=[2], dtype=tf.int64) dense = tf.constant([], shape=[0], dtype=tf.int64) tf.raw_ops.SparseDenseCwiseMul( sp_indices=indices, sp_values=values, sp_shape=shape, dense=dense) ``` Since the implementation only validates the rank of the input arguments but no constraints between dimensions, an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers. Patches We have patched the issue in GitHub commit 7ae2af34087fb4b5c8915279efd03da3b81028bc. 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 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.

Related

Alias · CVE (1)

  • CVE-2021-29567 TensorFlow is an end-to-end open source platform for machine learning. vulnerability

Weakness · CWE (1)

  • CWE-617 Reachable Assertion weakness

References