CVE-2022-29200 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.LSTMBlockCell` 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 does not validate the ranks of any of the arguments to this API call. This results in `CHECK`-failures when the elements of the tensor are accessed. 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-2022-29200 affects TensorFlow's implementation of the `tf.raw_ops.LSTMBlockCell` operation, which is a critical component in machine learning workflows involving recurrent neural networks. This flaw exists in TensorFlow versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4, representing a significant security concern for organizations relying on these platforms for their artificial intelligence infrastructure. The vulnerability stems from insufficient input validation within the raw operations layer of TensorFlow, specifically targeting the LSTM block cell implementation that forms the foundation of many sequence processing models.
The technical flaw manifests in the complete absence of rank validation for input arguments within the `tf.raw_ops.LSTMBlockCell` implementation. This lack of proper parameter validation creates a condition where malformed tensor inputs can trigger internal CHECK failures during tensor element access operations. The vulnerability operates at the core of TensorFlow's computational graph execution, where the system performs assertions to verify tensor dimensions and structures before processing. When these assertions fail due to improper input validation, the system experiences a controlled crash that manifests as a denial of service condition rather than a more severe security breach.
The operational impact of this vulnerability extends beyond simple service disruption, as it can be exploited by malicious actors to systematically crash TensorFlow-based services and applications. Attackers can craft specially formatted inputs that bypass normal validation mechanisms and cause the CHECK assertions to fail, leading to application termination and service unavailability. This type of denial of service attack can be particularly damaging in production environments where TensorFlow is used for critical AI workloads, potentially affecting model training processes, inference services, and automated decision-making systems. The vulnerability affects the raw operations layer, which means it can impact any application utilizing LSTM architectures regardless of the higher-level API used, making it particularly concerning for widespread exploitation.
The patch implemented in versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 addresses this issue by introducing proper input validation mechanisms that verify tensor ranks before processing operations. This fix aligns with security best practices outlined in CWE-20, which addresses "Improper Input Validation" as a fundamental weakness in software systems. The remediation follows established security principles from the ATT&CK framework, specifically targeting the privilege escalation and denial of service categories where improper input handling can lead to system instability. Organizations should prioritize upgrading to patched versions to eliminate this vulnerability, as the lack of input validation creates a predictable attack surface that can be exploited to disrupt machine learning workflows and compromise service availability. The fix represents a defensive programming approach that ensures proper tensor dimension validation before any computational operations are executed, preventing the CHECK failures that previously led to system crashes and denial of service conditions.