CVE-2022-36000 in TensorFlow
Summary
by MITRE • 09/17/2022
TensorFlow is an open source platform for machine learning. When `mlir::tfg::ConvertGenericFunctionToFunctionDef` is given empty function attributes, it gives a null dereference. We have patched the issue in GitHub commit aed36912609fc07229b4d0a7b44f3f48efc00fd0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
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Analysis
by VulDB Data Team • 10/20/2022
The vulnerability identified as CVE-2022-36000 affects the TensorFlow machine learning platform, specifically within the MLIR (Multi-Level Intermediate Representation) component of the TensorFlow Graph (TFG) module. This issue manifests when the `mlir::tfg::ConvertGenericFunctionToFunctionDef` function processes empty function attributes, leading to a null pointer dereference condition that can cause application crashes or potential system instability. The flaw exists in the function's handling of malformed input parameters, where the absence of expected attributes results in improper memory management and execution flow.
The technical implementation of this vulnerability stems from inadequate input validation within the TensorFlow graph conversion process. When TensorFlow attempts to convert generic functions to function definitions, it expects certain attribute structures to be present and properly initialized. However, the code fails to properly check for null or empty attribute containers before attempting to dereference pointers to these structures. This null pointer dereference represents a classic software security flaw that can be exploited to cause denial of service conditions or potentially enable more sophisticated attacks depending on the execution context.
This vulnerability impacts TensorFlow versions prior to 2.10.0, with specific affected releases including 2.9.1, 2.8.1, and 2.7.2, all of which remain within supported release cycles. The security implications extend beyond simple application crashes as this null dereference can potentially be leveraged to disrupt machine learning workflows, particularly in environments where TensorFlow serves as a critical component for model deployment and inference. The absence of known workarounds means that affected systems must either be patched or upgraded to avoid exposure to this vulnerability.
The mitigation strategy involves applying the fix implemented in GitHub commit aed36912609fc07229b4d0a7b44f3f48efc00fd0, which properly validates function attributes before attempting dereference operations. Organizations should prioritize upgrading to TensorFlow 2.10.0 or applying the cherry-picked fixes to older supported versions to ensure complete protection. This vulnerability aligns with CWE-476, which describes null pointer dereference conditions, and could potentially map to ATT&CK technique T1499.001 for denial of service attacks. The fix demonstrates proper defensive programming practices by implementing precondition checks and graceful error handling when processing function attributes in the TensorFlow graph conversion pipeline.