CVE-2022-29191 in TensorFlow
Summary
by MITRE • 05/21/2022
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.GetSessionTensor` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.
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Analysis
by VulDB Data Team • 05/27/2022
The vulnerability identified as CVE-2022-29191 affects TensorFlow, a widely-used open source machine learning platform that powers numerous applications across industries including healthcare, finance, and autonomous systems. This issue resides within the `tf.raw_ops.GetSessionTensor` implementation which serves as a low-level interface for accessing session tensors during computation graph execution. The flaw represents a classic input validation weakness that can be exploited to disrupt service availability.
The technical implementation of this vulnerability stems from insufficient argument validation within the GetSessionTensor raw operation. When processing input parameters, the function fails to perform comprehensive checks that would normally occur in a secure implementation. This inadequate validation leads to a CHECK-failure condition that manifests as an abrupt termination of the TensorFlow runtime process. The vulnerability is particularly concerning because it operates at the core computational layer of the framework where inputs from various sources are processed without proper sanitization.
From an operational perspective, this vulnerability creates a significant denial of service risk for systems relying on TensorFlow for machine learning workloads. An attacker who can control the input parameters to the GetSessionTensor operation can force the application to crash repeatedly, effectively rendering the machine learning service unavailable. This impacts not only the immediate execution environment but also broader system availability since TensorFlow processes often serve multiple concurrent requests in production environments. The vulnerability affects multiple major versions of TensorFlow including 2.9.0, 2.8.1, 2.7.2, and 2.6.4, indicating it was present across a substantial portion of the platform's user base.
The security implications extend beyond simple service disruption as this vulnerability could be exploited in broader attack scenarios. According to CWE classification, this represents a weakness in the validation of inputs or parameters, specifically CWE-252, which deals with unchecked return values. The ATT&CK framework categorizes this as a Denial of Service attack vector that could be leveraged by adversaries to compromise system availability. Organizations using TensorFlow in production environments should prioritize immediate patching of affected versions to prevent potential exploitation. The fix implemented in versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 demonstrates proper input validation mechanisms that prevent the CHECK-failure condition from occurring. Security teams should also consider implementing monitoring for abnormal process termination patterns and input validation failures that could indicate exploitation attempts.