CVE-2021-29530 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments. Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since `OP_REQUIRES`(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function. Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`. 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 • 05/19/2021
The vulnerability described in CVE-2021-29530 represents a critical null pointer dereference flaw within TensorFlow's sparse matrix sparse cholesky operation implementation. This issue affects the tf.raw_ops.SparseMatrixSparseCholesky function which is part of TensorFlow's end-to-end machine learning platform. The flaw stems from inadequate input validation mechanisms within the kernel implementation, specifically in the sparse_cholesky_op.cc file where the ValidateInputs function fails to properly terminate execution when invalid permutation inputs are provided. The root cause lies in the improper handling of the OP_REQUIRES macro which only exits the current function rather than halting the entire execution flow, allowing malformed inputs to propagate through the system.
The technical implementation details reveal that while ValidateInputs function contains necessary checks and calls to OP_REQUIRES for validation, the macro behavior creates a dangerous execution path where failed validation conditions result in early function returns but do not prevent subsequent code execution. This architectural flaw at line 85-86 of the sparse_cholesky_op.cc file allows attackers to craft malicious permutation inputs that trigger the null pointer dereference condition. The vulnerability operates at the kernel level within TensorFlow's computational graph processing framework, making it particularly dangerous as it can be exploited during model execution or training phases.
The operational impact of this vulnerability extends beyond simple denial of service scenarios, as it represents a potential security risk that could be exploited by malicious actors to disrupt machine learning workflows. The flaw affects multiple TensorFlow versions including 2.5.0 and earlier releases, with the specific affected versions being 2.4.2, 2.3.3, 2.2.3, and 2.1.4, all of which remain within supported release ranges. This widespread impact across version lines demonstrates the severity of the issue and the need for comprehensive patching strategies. The vulnerability aligns with CWE-476 which addresses null pointer dereference conditions, and could potentially be leveraged in broader attack chains under the ATT&CK framework's execution and privilege escalation domains.
The mitigation strategy for CVE-2021-29530 involves implementing proper status checking mechanisms or restructuring the ValidateInputs function to return explicit Status values rather than relying on the current macro-based approach. The TensorFlow development team has addressed this issue by incorporating the fix into version 2.5.0 and cherry-picking the commit to the affected older versions to ensure comprehensive coverage. This approach follows established security practices for preventing similar issues in software libraries and demonstrates the importance of proper input validation in mathematical computing frameworks. The fix ensures that when validation fails, the entire execution path is properly terminated rather than allowing execution to continue with potentially invalid inputs, thereby preventing the null pointer dereference condition that could be exploited by attackers.