CVE-2021-37692 in TensorFlow
Summary
by MITRE • 08/13/2021
TensorFlow is an end-to-end open source platform for machine learning. In affected versions under certain conditions, Go code can trigger a segfault in string deallocation. For string tensors, `C.TF_TString_Dealloc` is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions). To fix this, the call to set the finalizer function is deferred until `NewTensor` returns and, if encoding failed for a string tensor, deallocs are determined based on bytes written. We have patched the issue in GitHub commit 8721ba96e5760c229217b594f6d2ba332beedf22. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, which is the other affected version.
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Analysis
by VulDB Data Team • 08/18/2021
The vulnerability described in CVE-2021-37692 represents a critical memory safety issue within the TensorFlow machine learning platform that specifically affects Go code execution environments. This flaw manifests as a segmentation fault during string tensor deallocation processes, creating a potential avenue for system instability and denial of service conditions. The issue stems from improper handling of tensor deallocation within the garbage collection finalizer functions, where the system assumes successful encoding completion before performing cleanup operations. This misalignment between expected and actual tensor processing states creates a dangerous race condition that can lead to system crashes when malformed tensor data is encountered during cleanup phases.
The technical implementation of this vulnerability occurs at the intersection of Go's garbage collection mechanisms and TensorFlow's tensor memory management system. When string tensors are processed, the system employs a finalizer function that calls `C.TF_TString_Dealloc` during garbage collection cycles. However, the design flaw lies in the timing of tensor structure validation, which is deferred until encoding operations rather than being validated upfront. This approach prioritizes performance optimization by avoiding premature validation checks, but creates a dangerous assumption that all tensors will successfully encode before deallocation occurs. The segmentation fault specifically occurs when a string tensor with failed encoding attempts to undergo garbage collection, as the deallocation process assumes prior successful encoding completion.
The operational impact of this vulnerability extends beyond simple system crashes to potentially compromise the integrity of machine learning workflows and data processing pipelines. Attackers could exploit this condition by crafting malicious tensor inputs that trigger encoding failures, thereby forcing system crashes during cleanup phases of tensor processing. This creates a denial of service scenario that could affect production machine learning environments where TensorFlow is deployed for continuous processing tasks. The vulnerability particularly impacts systems running TensorFlow versions prior to 2.6.0, making it a significant concern for organizations maintaining legacy deployments. The issue follows patterns commonly associated with CWE-476 Null Pointer Dereference and CWE-125 Out-of-bounds Read categories, as the system attempts to access memory locations that may not be properly initialized or validated.
Security implications of this vulnerability align with ATT&CK technique T1499.004 for Network Denial of Service, as the segmentation fault can be triggered through controlled tensor inputs to create system instability. The exploitation requires understanding of TensorFlow's internal tensor processing mechanisms and Go's garbage collection behavior, making it a sophisticated attack vector that targets specific memory management flaws. Organizations should consider this vulnerability in their threat modeling exercises, particularly in environments where TensorFlow is used for automated processing of untrusted input data. The fix implemented in TensorFlow 2.6.0 addresses the root cause by deferring the finalizer function registration until after `NewTensor` completion and implementing conditional deallocation logic based on actual bytes written rather than assumed successful encoding states.
The mitigation strategy involves immediate deployment of TensorFlow 2.6.0 or applying the cherry-picked fix to TensorFlow 2.5.1 installations, as both versions contain the necessary code modifications to prevent the segmentation fault condition. System administrators should prioritize patching environments where TensorFlow processes untrusted or user-provided tensor data, as these scenarios present the highest risk for exploitation. Additional monitoring should be implemented to detect unusual garbage collection patterns or system crashes during tensor processing operations, as these could indicate exploitation attempts. The fix demonstrates proper defensive programming practices by ensuring tensor state validation occurs before memory cleanup operations, thereby preventing the dangerous assumptions that led to the original vulnerability. This vulnerability serves as a reminder of the importance of careful memory management in high-performance computing environments where multiple systems interact through shared memory spaces.