CVE-2022-21727 in Tensorflow
Summary
by MITRE • 02/03/2022
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.
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Analysis
by VulDB Data Team • 05/06/2025
The vulnerability identified as CVE-2022-21727 resides within TensorFlow's implementation of shape inference for the Dequantize operation, representing a critical integer overflow weakness that could potentially enable remote code execution or denial of service attacks. This flaw specifically manifests in the handling of the axis argument within the Dequantize operation's shape inference logic, where the code fails to properly validate input parameters before performing arithmetic operations. The vulnerability affects TensorFlow versions prior to 2.8.0, with affected releases including 2.7.1, 2.6.3, and 2.5.3, all of which remain within supported maintenance windows. The issue stems from the fact that the axis argument can accept values including -1, which serves as the default for the optional parameter, or any positive integer up to the number of input dimensions. However, the implementation does not validate the upper bounds of this parameter, creating a scenario where attackers can manipulate the axis value to trigger integer overflow conditions during computation.
The technical exploitation of this vulnerability occurs when the code computes `axis + 1` without proper bounds checking, allowing an attacker to supply a value that when incremented causes the integer to wrap around to a negative value or an unexpected large positive number. This integer overflow condition can lead to memory corruption, buffer overflows, or other undefined behaviors that may be leveraged to execute arbitrary code or cause system instability. The flaw falls under CWE-190, Integer Overflow or Wraparound, which is a well-documented weakness that frequently leads to security vulnerabilities in software systems. The attack surface is particularly concerning given TensorFlow's widespread adoption in machine learning applications, where attackers could potentially craft malicious model files that trigger this condition when processed by vulnerable TensorFlow implementations.
From an operational perspective, this vulnerability presents significant risks to organizations deploying TensorFlow-based machine learning systems, particularly those that process untrusted model inputs or accept model files from external sources. The impact extends beyond simple denial of service to potentially enable remote code execution, making it a critical concern for cloud providers, AI platform operators, and enterprises utilizing TensorFlow in production environments. The vulnerability's exploitation does not require specialized knowledge beyond understanding the parameter constraints, making it accessible to attackers with basic familiarity with TensorFlow's API. Organizations running affected TensorFlow versions face potential compromise of their machine learning pipelines, model serving infrastructure, and data processing systems that rely on vulnerable code paths. The ATT&CK framework categorizes this vulnerability under T1583.001, which covers the development of tools and techniques that can be used to exploit such software weaknesses, highlighting the broader threat landscape that this vulnerability represents.
The remediation strategy involves upgrading to TensorFlow 2.8.0 or applying the cherry-picked fixes to the affected maintenance versions 2.7.1, 2.6.3, and 2.5.3, ensuring that all affected systems receive the necessary patches. Organizations should prioritize this update across their infrastructure, particularly in environments where TensorFlow processes untrusted model inputs or where the software is exposed to external attack surfaces. Additional mitigations include implementing strict input validation for model files, using sandboxed execution environments for model processing, and monitoring for unusual computational patterns that might indicate exploitation attempts. Security teams should also consider implementing network segmentation and access controls around systems running vulnerable TensorFlow versions to limit potential attack vectors. The fix addresses the root cause by implementing proper bounds checking on the axis parameter before performing the arithmetic operation that leads to the overflow condition, thereby preventing the integer wraparound that enables exploitation.