CVE-2021-41219 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. 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 identified as CVE-2021-41219 affects TensorFlow, a widely used open source machine learning platform that serves as the foundation for numerous artificial intelligence applications across industries. This security flaw resides within the sparse matrix multiplication functionality of TensorFlow's computational framework, specifically manifesting as undefined behavior through improper reference binding to null pointers. The vulnerability represents a critical concern for organizations relying on TensorFlow for their machine learning workloads, as it could potentially be exploited to disrupt system operations or compromise data integrity. The issue is particularly significant given TensorFlow's extensive adoption in both research and production environments where reliable computational behavior is paramount for maintaining system stability and security.
The technical flaw occurs when the sparse matrix multiplication operations process input tensors where one or both of the dimensions of matrices a or b are zero or negative values. This condition creates a scenario where the code attempts to bind a reference to a null pointer, resulting in undefined behavior that can manifest as memory corruption or system instability. When dimensions are zero or less, the expected behavior should be to allocate an empty output tensor to maintain the invariant that output tensors are always allocated upon successful operation completion, but the kernel implementation should return early without writing to this empty tensor. The vulnerability stems from the lack of proper validation and early termination handling in the kernel implementation, which fails to account for these edge cases in dimension validation.
The operational impact of this vulnerability extends beyond simple computational errors to potentially enable heap out-of-bounds memory access when attempting to write to empty tensors that should have been allocated but never populated. This scenario creates a potential attack surface where maliciously crafted inputs could trigger memory corruption, leading to application crashes, data loss, or in more severe cases, arbitrary code execution depending on the system's memory layout and security controls. The vulnerability affects multiple TensorFlow versions including 2.4.4, 2.5.2, 2.6.1, and the affected versions, making it a widespread concern for organizations maintaining older TensorFlow installations. The potential for heap overflow access patterns makes this vulnerability particularly dangerous in environments where TensorFlow is used for processing sensitive data or in systems with limited memory protections.
The fix for this vulnerability involves implementing proper dimension validation and early termination logic within the sparse matrix multiplication kernel, ensuring that when zero or negative dimensions are detected, the system allocates appropriate empty tensors without attempting to write to them, thereby preventing heap out-of-bounds access. The TensorFlow team has addressed this issue by including the fix in version 2.7.0 and backporting it to versions 2.6.1, 2.5.2, and 2.4.4, which remain in supported ranges. This approach aligns with industry best practices for vulnerability management and ensures that organizations using older TensorFlow versions can protect themselves against this specific undefined behavior exploit. The resolution demonstrates proper adherence to security patch management protocols and maintains backward compatibility while addressing the core memory safety concern. Organizations should prioritize upgrading to the patched versions or applying the cherry-picked fixes to maintain system integrity and prevent potential exploitation of this undefined behavior vulnerability.