CVE-2022-35982 in TensorFlow
Summary
by MITRE • 09/17/2022
TensorFlow is an open source platform for machine learning. If `SparseBincount` is given inputs for `indices`, `values`, and `dense_shape` that do not make a valid sparse tensor, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 40adbe4dd15b582b0210dfbf40c243a62f5119fa. 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 • 10/19/2022
The vulnerability identified as CVE-2022-35982 affects TensorFlow, a widely-used open source machine learning platform that powers numerous artificial intelligence applications across industries. This security flaw resides within the SparseBincount operation, which is designed to perform bin counting operations on sparse tensor data structures. The issue manifests when the SparseBincount function receives malformed inputs for its indices, values, and dense_shape parameters that fail to constitute a valid sparse tensor representation. This particular weakness represents a classic buffer over-read scenario that can lead to system instability and denial of service conditions.
The technical implementation of this vulnerability stems from insufficient input validation within the SparseBincount function's processing pipeline. When TensorFlow encounters invalid sparse tensor parameters, the internal memory management routines fail to properly handle the malformed data structure, resulting in a segmentation fault that terminates the application process. This type of flaw falls under CWE-125, which describes out-of-bounds read conditions, and more specifically aligns with CWE-129, representing insufficient input validation. The segmentation fault occurs because the function attempts to access memory locations that are either invalid or outside the bounds of the allocated memory regions, causing the operating system to terminate the process abruptly.
From an operational perspective, this vulnerability presents a significant risk to machine learning deployments that rely on TensorFlow's sparse tensor operations. Attackers could exploit this weakness by crafting malicious input data that violates the expected sparse tensor format constraints, thereby triggering a denial of service condition that would disrupt legitimate service operations. The impact extends beyond simple service interruption as this vulnerability could potentially be leveraged in broader attack scenarios where system availability is critical. The vulnerability affects multiple TensorFlow versions including 2.7.2, 2.8.1, 2.9.1, and the upcoming 2.10.0 release, indicating the widespread nature of this flaw within the platform's codebase. This affects organizations running machine learning workloads in production environments, cloud services, and edge computing deployments where TensorFlow is extensively utilized.
The mitigation strategy for this vulnerability involves applying the official patch provided by TensorFlow developers through GitHub commit 40adbe4dd15b582b0210dfbf40c243a62f5119fa. Organizations should immediately upgrade to TensorFlow 2.10.0 or apply the cherry-picked fixes to their affected versions 2.9.1, 2.8.1, and 2.7.2. This patch implements proper input validation checks that prevent the processing of malformed sparse tensor parameters from causing segmentation faults. The fix aligns with ATT&CK technique T1499.004, which covers network denial of service attacks, by ensuring that malformed inputs cannot trigger system termination. Given that no known workarounds exist for this specific vulnerability, the recommended approach is to implement the official patches as soon as possible. Security teams should prioritize this update in their vulnerability management processes and ensure that all TensorFlow deployments are protected against this denial of service attack vector that could compromise system availability in machine learning environments.