CVE-2022-29199 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.LoadAndRemapMatrix does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `initializing_values` is a vector but there is no validation for this before accessing its value. 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-2022-29199 affects TensorFlow, a widely-used open-source machine learning platform that powers numerous applications across industries. This issue resides within the `tf.raw_ops.LoadAndRemapMatrix` operation which is part of TensorFlow's internal raw operations API. The flaw represents a classic validation bypass vulnerability that can be exploited to cause system instability and denial of service conditions. The vulnerability impacts multiple major versions of TensorFlow including 2.9.0, 2.8.1, 2.7.2, and 2.6.4, with earlier versions being particularly susceptible to exploitation. This vulnerability falls under CWE-20, which describes improper input validation, and represents a specific case of unchecked return values in critical system operations.
The technical implementation flaw occurs when TensorFlow processes the `initializing_values` parameter within the LoadAndRemapMatrix operation. The code makes an implicit assumption that this parameter must be a vector, but fails to perform proper validation before attempting to access its values. This unchecked assumption leads to a CHECK-failure condition that can be triggered through malicious input manipulation. The vulnerability manifests as a runtime assertion failure that terminates the application process or causes it to enter an unstable state. The lack of input sanitization allows attackers to craft specific inputs that bypass the expected parameter structure, resulting in memory access violations or other internal system failures that cannot be recovered from gracefully.
The operational impact of this vulnerability extends beyond simple denial of service as it can affect any system utilizing TensorFlow's matrix loading functionality, particularly in production environments where reliability is critical. Attackers can exploit this vulnerability by providing malformed input to the LoadAndRemapMatrix operation, causing the system to crash and potentially leading to service disruption for legitimate users. This type of vulnerability is particularly concerning in machine learning pipelines where TensorFlow components may be exposed to untrusted input data, such as in web applications or data processing systems that accept user uploads. The vulnerability can be leveraged in various attack scenarios including resource exhaustion attacks, where repeated exploitation attempts can consume system resources and lead to complete service unavailability.
Mitigation strategies for this vulnerability primarily involve upgrading to the patched versions of TensorFlow that contain the necessary input validation fixes. Organizations should prioritize updating their TensorFlow installations to versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4 where the vulnerability has been addressed through proper parameter validation. Additionally, implementing input sanitization measures at the application level can provide defense-in-depth protection against similar vulnerabilities. System administrators should monitor for any unauthorized access attempts or unusual behavior patterns that may indicate exploitation attempts. From a security perspective, this vulnerability aligns with ATT&CK technique T1499.004 which covers network denial of service attacks, and represents a critical weakness in software design that could be exploited to compromise system availability. Regular security assessments and vulnerability scanning should be conducted to identify and remediate similar validation issues across the entire software stack.