CVE-2022-29212 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, certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling. Thus, since code was calling `QuantizeMultiplierSmallerThanOneExp`, the `TFLITE_CHECK_LT` assertion would trigger and abort the process. 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 described in CVE-2022-29212 affects TensorFlow Lite implementations across multiple version branches, specifically impacting the model loading process within the TFLite interpreter. This issue stems from a fundamental flaw in how quantization parameters are handled during the conversion of machine learning models to TensorFlow Lite format. The problem manifests when TFLite models are processed through the model converter tool, resulting in scenarios where quantization scale factors exceed the expected sub-unit range. This represents a critical deviation from the expected behavior within the quantization pipeline, as the codebase was designed to assume scaling values less than one unit.
The technical root cause of this vulnerability lies in the incorrect assumption made by the TFLite interpreter's quantization logic. During the quantization process, when scale values exceed one, the system attempts to utilize the `QuantizeMultiplierSmallerThanOneExp` function which is specifically designed for values less than one. This mismatch triggers a `TFLITE_CHECK_LT` assertion failure, causing the interpreter to terminate abruptly rather than gracefully handling the exceptional scaling condition. The assertion check serves as a safety mechanism but fails to account for legitimate scenarios where quantization scale factors legitimately exceed one, leading to denial of service conditions. This flaw aligns with CWE-682, which addresses incorrect calculations and improper use of mathematical operations, particularly in contexts involving floating-point arithmetic and scaling factors.
The operational impact of this vulnerability is significant for any system utilizing TensorFlow Lite models that have been processed through the affected version range. When a malicious actor or system administrator attempts to load a model that contains quantization parameters exceeding one, the interpreter will crash immediately, resulting in complete service disruption. This denial of service condition affects applications ranging from mobile inference engines to embedded systems that rely on TensorFlow Lite for machine learning workloads. The vulnerability is particularly concerning in production environments where model loading is automated or occurs in response to user requests, as it can lead to complete system unavailability. This scenario maps directly to ATT&CK technique T1499.004, which involves network disruption through service availability attacks, though in this case the disruption occurs at the application level rather than network level.
Mitigation strategies for CVE-2022-29212 require immediate deployment of patched TensorFlow versions, specifically 2.9.0, 2.8.1, 2.7.2, and 2.6.4, which contain the necessary code modifications to handle quantization scale factors exceeding one. Organizations should implement comprehensive testing procedures to validate that their existing TFLite models will function correctly with the patched versions, particularly focusing on models that may have been generated with unusual quantization parameters. Additionally, system administrators should consider implementing monitoring solutions that can detect interpreter crashes or abnormal termination patterns, which may indicate exploitation attempts. The patch addresses the core issue by modifying the quantization logic to properly handle scale factors greater than one, removing the problematic assertion check that was causing the interpreter to abort. Security teams should also conduct thorough audits of their machine learning pipelines to identify any models that may have been created with quantization parameters that could trigger this condition, ensuring complete remediation across all affected systems and applications.