Vulnerabilities > CVE-2021-41203 - Integer Overflow or Wraparound vulnerability in Google Tensorflow
Attack vector
LOCAL Attack complexity
LOW Privileges required
LOW Confidentiality impact
HIGH Integrity impact
HIGH Availability impact
HIGH Summary
TensorFlow is an open source platform for machine learning. In affected versions an attacker can trigger undefined behavior, integer overflows, segfaults and `CHECK`-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats. The fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
Vulnerable Configurations
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
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7pxj-m4jf-r6h2
- https://github.com/tensorflow/tensorflow/commit/368af875869a204b4ac552b9ddda59f6a46a56ec
- https://github.com/tensorflow/tensorflow/commit/abcced051cb1bd8fb05046ac3b6023a7ebcc4578
- https://github.com/tensorflow/tensorflow/commit/e8dc63704c88007ee4713076605c90188d66f3d2
- https://github.com/tensorflow/tensorflow/commit/b619c6f865715ca3b15ef1842b5b95edbaa710ad