CVE-2022-41895 in TensorFlow
Summary
by MITRE • 11/19/2022
TensorFlow is an open source platform for machine learning. If `MirrorPadGrad` is given outsize input `paddings`, TensorFlow will give a heap OOB error. We have patched the issue in GitHub commit 717ca98d8c3bba348ff62281fdf38dcb5ea1ec92. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
If you want to get best quality of vulnerability data, you may have to visit VulDB.
Analysis
by VulDB Data Team • 12/20/2022
The vulnerability identified as CVE-2022-41895 affects TensorFlow, a widely-used open source machine learning platform that powers numerous artificial intelligence applications across industries. This security flaw specifically resides within the MirrorPadGrad operation, which is part of TensorFlow's tensor manipulation capabilities. The issue manifests when the MirrorPadGrad function receives improperly sized input paddings parameter, creating a condition that leads to heap out-of-bounds memory access errors. Such vulnerabilities are particularly dangerous in machine learning environments where TensorFlow processes large datasets and complex models, as they can potentially be exploited to execute arbitrary code or cause system instability.
The technical implementation of this vulnerability stems from inadequate bounds checking within the MirrorPadGrad function's memory allocation and handling logic. When developers provide oversized paddings inputs to this specific operation, the underlying memory management routines fail to validate the input parameters properly, resulting in buffer overflows that can corrupt adjacent memory regions. This type of flaw falls under the Common Weakness Enumeration category CWE-129, which specifically addresses "Improper Validation of Array Index" and represents a fundamental issue in input validation and memory safety. The vulnerability's impact is amplified by TensorFlow's extensive use in production environments where model training and inference operations often process sensitive data through complex computational graphs that may include this particular operation.
Operationally, this vulnerability presents significant risks to organizations relying on TensorFlow for machine learning workloads, particularly those handling confidential or regulated data. The heap out-of-bounds error can lead to application crashes, data corruption, or potentially more severe exploitation scenarios if attackers can craft malicious inputs that leverage the memory corruption to execute arbitrary code. Given TensorFlow's integration into various cloud services, edge computing deployments, and enterprise AI platforms, the potential attack surface is extensive. The vulnerability affects multiple TensorFlow versions including 2.8.4, 2.9.3, 2.10.1, and 2.11, indicating that a substantial portion of the user base remains at risk until proper patches are applied.
The security community has addressed this vulnerability through coordinated patching efforts that include the GitHub commit 717ca98d8c3bba348ff62281fdf38dcb5ea1ec92, which implements proper input validation and bounds checking for the MirrorPadGrad operation. This fix ensures that oversized paddings inputs are properly rejected before they can cause memory corruption issues. Organizations should prioritize applying these patches across their TensorFlow deployments, particularly since the maintainers have specifically cherry-picked the fix for older supported versions to ensure comprehensive coverage. The mitigation strategy aligns with standard security practices outlined in the MITRE ATT&CK framework, specifically addressing the mitigation of memory corruption vulnerabilities through proper input validation and code review processes that prevent exploitation of such flaws in machine learning infrastructure.