Vulnerabilities > CVE-2022-23567 - Integer Overflow or Wraparound vulnerability in Google Tensorflow

047910
CVSS 6.5 - MEDIUM
Attack vector
NETWORK
Attack complexity
LOW
Privileges required
LOW
Confidentiality impact
NONE
Integrity impact
NONE
Availability impact
HIGH
network
low complexity
google
CWE-190
NVD
Published: 2022-02-03
Updated: 2022-02-09

Summary

Tensorflow is an Open Source Machine Learning Framework. The implementations of `Sparse*Cwise*` ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or `CHECK`-fails when building new `TensorShape` objects (so, assert failures based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. 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