CVE-2021-41224 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseFillEmptyRows` can be made to trigger a heap OOB access. This occurs whenever the size of `indices` does not match the size of `values`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 11/10/2021
The vulnerability CVE-2021-41224 affects TensorFlow, a widely-used open-source machine learning platform that processes large-scale data and neural network models across various industries including healthcare, finance, and autonomous vehicles. This security flaw resides in the `SparseFillEmptyRows` operation, which is part of TensorFlow's sparse tensor handling functionality designed to fill empty rows in sparse matrices. The issue manifests when there is a mismatch between the dimensions of the `indices` and `values` parameters passed to this function, creating a condition that can be exploited to execute unauthorized memory access patterns.
The technical implementation flaw stems from insufficient input validation within the `SparseFillEmptyRows` function where the system fails to properly verify that the size of the indices array matches the size of the values array before proceeding with memory operations. This mismatch creates a heap out-of-bounds access condition that can be triggered through carefully crafted inputs during sparse tensor processing. The vulnerability is classified under CWE-129 as an insufficient validation of length of a buffer, and represents a classic heap-based buffer overflow that could potentially allow attackers to read or modify memory contents beyond intended boundaries. The flaw operates at the memory management level within TensorFlow's computational graph execution engine, making it particularly dangerous as it could be exploited during model training or inference operations.
The operational impact of this vulnerability extends across multiple deployment scenarios where TensorFlow is utilized, particularly affecting organizations that process large sparse datasets or implement machine learning pipelines with complex tensor operations. Attackers could potentially exploit this vulnerability to cause application crashes, data corruption, or in more severe cases, achieve arbitrary code execution depending on the memory layout and system configuration. The vulnerability affects TensorFlow versions 2.4.0 through 2.6.0, making it relevant to a substantial portion of the user base that was actively using these versions during the affected period. The risk is amplified in environments where TensorFlow is integrated into larger systems or deployed in cloud computing environments where memory isolation may be compromised.
Organizations utilizing affected TensorFlow versions should prioritize immediate patching to version 2.7.0 or apply the cherry-picked fixes to 2.6.1, 2.5.2, and 2.4.4 as recommended by the TensorFlow security team. The mitigation strategy involves updating the TensorFlow installation to ensure proper input validation is enforced, implementing proper input sanitization procedures for sparse tensor operations, and monitoring for unusual memory access patterns that could indicate exploitation attempts. System administrators should also consider implementing runtime protections such as address space layout randomization and stack canaries to reduce the exploitability of similar vulnerabilities. This vulnerability aligns with ATT&CK technique T1059.001 for command and scripting interpreter and T1068 for exploit for privilege escalation, as exploitation could potentially lead to system compromise. Organizations should also review their machine learning pipeline architectures to ensure proper input validation and sandboxing of model execution environments to prevent potential exploitation from affecting broader system infrastructure.