CVE-2022-41908 in TensorFlow
Summary
by MITRE • 11/19/2022
TensorFlow is an open source platform for machine learning. An input `token` that is not a UTF-8 bytestring will trigger a `CHECK` fail in `tf.raw_ops.PyFunc`. We have patched the issue in GitHub commit 9f03a9d3bafe902c1e6beb105b2f24172f238645. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
Analysis
by VulDB Data Team • 12/20/2022
The vulnerability identified as CVE-2022-41908 affects TensorFlow, a widely used open source machine learning platform that processes various data inputs through its computational graph operations. This issue specifically targets the tf.raw_ops.PyFunc operation which serves as a bridge between TensorFlow's computational framework and Python functions, enabling users to integrate custom Python code within their machine learning pipelines. The flaw manifests when the system encounters an input token that does not conform to UTF-8 bytestring format, creating a critical condition that causes the system to fail during execution.
The technical nature of this vulnerability stems from insufficient input validation within the PyFunc operation implementation. When TensorFlow processes a token that is not properly formatted as a UTF-8 bytestring, the system triggers a CHECK fail, which represents a fundamental assertion failure in the codebase. This type of error typically occurs when the program encounters an unexpected condition that violates its internal assumptions, causing the execution to terminate abruptly. The vulnerability falls under CWE-129, Input Validation, and specifically relates to improper handling of malformed input data. The underlying issue demonstrates a classic buffer overflow or input sanitization problem where the system fails to properly validate data types before processing them through critical operations.
The operational impact of this vulnerability extends beyond simple execution failure, as it can be exploited to cause denial of service conditions within machine learning applications that rely on TensorFlow's PyFunc operations. Attackers could potentially craft malicious input tokens that would trigger the CHECK fail, causing trained models or inference pipelines to crash, thereby disrupting machine learning workflows. This vulnerability particularly affects environments where TensorFlow is used for production inference, as any unexpected input could cause system-wide failures. The issue is especially concerning in scenarios where TensorFlow is integrated into larger systems or deployed in cloud environments where service availability is critical. The ATT&CK framework categorizes this under T1499.004, Server Software Component, as it exploits a flaw in the TensorFlow platform's core functionality.
Mitigation strategies for this vulnerability involve immediate patch application through the specified GitHub commit 9f03a9d3bafe902c1e6beb105b2f24172f238645, which addresses the input validation issue by properly handling non-UTF-8 bytestring inputs. Organizations should prioritize upgrading to TensorFlow 2.11 where the fix is included, or apply the cherry-picked patches to older supported versions 2.10.1, 2.9.3, and 2.8.4. Additionally, implementing input sanitization measures at the application level can provide defense-in-depth protection by validating all data inputs before they reach the TensorFlow processing pipeline. System administrators should also monitor for potential exploitation attempts and consider implementing runtime input validation checks to prevent malformed tokens from reaching the vulnerable PyFunc operations. The fix addresses the root cause by ensuring proper UTF-8 validation before processing, thereby preventing the CHECK fail condition that would otherwise terminate execution.