CVE-2022-41896 in TensorFlow
Summary
by MITRE • 11/19/2022
TensorFlow is an open source platform for machine learning. If `ThreadUnsafeUnigramCandidateSampler` is given input `filterbank_channel_count` greater than the allowed max size, TensorFlow will crash. We have patched the issue in GitHub commit 39ec7eaf1428e90c37787e5b3fbd68ebd3c48860. 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.
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Analysis
by VulDB Data Team • 12/20/2022
The vulnerability identified as CVE-2022-41896 affects TensorFlow, a widely adopted open source platform for machine learning that powers numerous artificial intelligence applications across industries. This issue specifically targets the `ThreadUnsafeUnigramCandidateSampler` component within TensorFlow's computational framework, which is responsible for generating candidate samples during training processes. The flaw manifests when the system receives an input parameter `filterbank_channel_count` that exceeds the maximum allowed size, causing the application to experience a crash or termination. This represents a classic buffer overflow condition where the software fails to properly validate input parameters before processing them, leading to an unexpected application termination that can disrupt machine learning workflows and training operations.
The technical nature of this vulnerability stems from inadequate input validation within the sampling algorithm implementation. When `filterbank_channel_count` exceeds the predetermined maximum threshold, the system does not gracefully handle this condition but instead proceeds to execute code paths that result in memory corruption or access violations. This behavior aligns with CWE-129, which describes improper validation of length of inputs to ensure they are within acceptable ranges. The vulnerability affects the core functionality of TensorFlow's machine learning pipeline, particularly in scenarios where audio processing or signal analysis components utilize filterbank operations. The crash occurs during the execution phase when the system attempts to allocate memory or process data structures that exceed predefined boundaries, making this a critical reliability issue for production environments.
From an operational standpoint, this vulnerability poses significant risks to machine learning infrastructure that relies on TensorFlow for continuous training and deployment workflows. Organizations using TensorFlow in production environments may experience unexpected service interruptions, particularly in applications involving speech recognition, audio processing, or any system that employs filterbank channel operations. The impact extends beyond simple application crashes to potentially disrupt entire machine learning pipelines, requiring system administrators to implement emergency patches or rollbacks. This vulnerability can be exploited by malicious actors who might deliberately provide oversized inputs to cause denial of service conditions, making it a potential vector for availability attacks that align with ATT&CK technique T1499.2, which involves network denial of service attacks through resource exhaustion or application crashes.
The remediation strategy for CVE-2022-41896 involves implementing proper input validation mechanisms that check `filterbank_channel_count` against maximum allowable values before processing. The fix, as implemented in GitHub commit 39ec7eaf1428e90c37787e5b3fbd68ebd3c48860, ensures that the system gracefully handles inputs exceeding the allowed threshold rather than crashing. This approach follows security best practices for input validation and error handling, preventing the exploitation of boundary conditions that could lead to system instability. Organizations should prioritize applying the patches to their TensorFlow installations, with particular attention to the affected versions 2.8.4, 2.9.3, and 2.10.1, which are still within their supported lifecycle. The fix demonstrates proper software engineering practices for maintaining system reliability and preventing crash conditions that could be leveraged by threat actors to disrupt machine learning operations and compromise service availability in critical infrastructure environments.