Vulnerabilities > CVE-2022-21727 - Integer Overflow or Wraparound vulnerability in Google Tensorflow

047910
CVSS 8.8 - HIGH
Attack vector
NETWORK
Attack complexity
LOW
Privileges required
LOW
Confidentiality impact
HIGH
Integrity impact
HIGH
Availability impact
HIGH
network
low complexity
google
CWE-190
NVD
Published: 2022-02-03
Updated: 2022-02-08

Summary

Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Vulnerable Configurations

Part Description Count
Application
Google
403

Common Weakness Enumeration (CWE)

Common Attack Pattern Enumeration and Classification (CAPEC)

  • Forced Integer Overflow
    This attack forces an integer variable to go out of range. The integer variable is often used as an offset such as size of memory allocation or similarly. The attacker would typically control the value of such variable and try to get it out of range. For instance the integer in question is incremented past the maximum possible value, it may wrap to become a very small, or negative number, therefore providing a very incorrect value which can lead to unexpected behavior. At worst the attacker can execute arbitrary code.

References