CVE-2021-29515 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixDiag*` operations(https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 05/16/2021
The vulnerability described in CVE-2021-29515 affects TensorFlow, a widely-used open-source machine learning platform that serves as the foundation for numerous artificial intelligence applications across various industries. This security flaw specifically targets the MatrixDiag* operations within TensorFlow's linear algebra kernel implementations, which are essential functions for manipulating matrix diagonals in machine learning computations. The vulnerability resides in the core kernel code at line 195-197 of the matrix_diag_op.cc file, where the implementation fails to perform proper validation checks on tensor arguments before processing them. This particular flaw represents a classic case of insufficient input validation that could potentially lead to unexpected behavior or exploitation within machine learning workflows that rely on these operations.
The technical nature of this vulnerability stems from the absence of empty tensor validation within the MatrixDiag* operations, which are designed to extract or create matrix diagonals from tensor inputs. When these operations receive empty tensors as arguments, the lack of validation can cause undefined behavior or memory access violations that may manifest as crashes, incorrect computations, or potentially exploitable conditions. According to CWE-457, this vulnerability maps to the weakness of using uninitialized variables or failing to validate input parameters, which creates a pathway for adversaries to potentially manipulate the execution flow of machine learning applications. The flaw is particularly concerning because matrix diagonal operations are fundamental building blocks in neural network computations, optimization algorithms, and various linear algebra operations that form the backbone of modern machine learning pipelines.
The operational impact of this vulnerability extends beyond simple computational errors, as it could potentially be exploited in environments where TensorFlow is used for critical machine learning workloads. Attackers could craft malicious inputs containing empty tensors to trigger the vulnerable code path, potentially leading to denial of service conditions that disrupt machine learning services or applications. In more sophisticated attack scenarios, this weakness could be combined with other vulnerabilities to achieve arbitrary code execution within TensorFlow-based applications, particularly in environments where machine learning models are processed with untrusted data inputs. The vulnerability affects multiple TensorFlow versions including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and the subsequent 2.5.0 release, indicating that a significant portion of the TensorFlow user base was potentially exposed to this risk. The ATT&CK framework categorizes this vulnerability under the T1059.001 technique of command and scripting interpreter, as the exploitation could involve crafting specific tensor inputs that trigger the vulnerable code paths during normal TensorFlow operations.
The remediation approach for CVE-2021-29515 involves implementing proper input validation checks within the MatrixDiag* operations to ensure that tensor arguments are non-empty before processing. This fix aligns with the principle of defensive programming and input validation that is fundamental to secure software development practices. Organizations using affected TensorFlow versions should immediately upgrade to the patched releases or apply the cherry-picked fixes to their existing installations. The vulnerability serves as a reminder of the importance of comprehensive testing and validation in machine learning frameworks, particularly when dealing with mathematical operations that form the foundation of complex computational pipelines. Security teams should monitor their TensorFlow-based systems for any unusual behavior or performance degradation that might indicate exploitation attempts, while also ensuring that all machine learning workloads are properly sanitized and validated before processing untrusted inputs. The fix implementation demonstrates the critical need for maintaining robust validation mechanisms in mathematical libraries and machine learning frameworks that handle complex tensor operations.