GitHub Advisoryadvisory · PyPI

GHSA-pqhm-4wvf-2jg8 Missing validation results in undefined behavior in `QuantizedConv2D`

MEDIUM Published 2022-05-24 Modified 2022-05-24 Official page ↗
Aliases
CVE-2022-29201
Ecosystem
PyPI
Packages
tensorflow, tensorflow-cpu, tensorflow-gpu
Severity
Moderate
CVSS vector
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Weaknesses
CWE-20, CWE-476
Reviewed
2022-05-24

Affected packages

• PyPI tensorflow: < 2.6.4 → fixed in 2.6.4 • PyPI tensorflow: >= 2.7.0, < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow: >= 2.8.0, < 2.8.1 → fixed in 2.8.1 • PyPI tensorflow-cpu: < 2.6.4 → fixed in 2.6.4 • PyPI tensorflow-cpu: >= 2.7.0, < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow-cpu: >= 2.8.0, < 2.8.1 → fixed in 2.8.1 • PyPI tensorflow-gpu: < 2.6.4 → fixed in 2.6.4 • PyPI tensorflow-gpu: >= 2.7.0, < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow-gpu: >= 2.8.0, < 2.8.1 → fixed in 2.8.1

Description

Impact The implementation of `tf.raw_ops.QuantizedConv2D` does not fully validate the input arguments: ```python import tensorflow as tf input = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8) filter = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8) bad args min_input = tf.constant([], shape=[0], dtype=tf.float32) max_input = tf.constant(0, shape=[], dtype=tf.float32) min_filter = tf.constant(0, shape=[], dtype=tf.float32) max_filter = tf.constant(0, shape=[], dtype=tf.float32) tf.raw_ops.QuantizedConv2D( input=input, filter=filter, min_input=min_input, max_input=max_input, min_filter=min_filter, max_filter=max_filter, strides=[1, 1, 1, 1], padding="SAME") ``` In this case, references get bound to `nullptr` for each argument that is empty (in the example, all arguments in the `bad args` section). Patches We have patched the issue in GitHub commit 0f0b080ecde4d3dfec158d6f60da34d5e31693c4. 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 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.

Related

Alias · CVE (1)

  • CVE-2022-29201 TensorFlow is an open source platform for machine learning. vulnerability

Weakness · CWE (2)

  • CWE-20 Improper Input Validation weakness
  • CWE-476 NULL Pointer Dereference weakness

References