CVE-2022-41899 in TensorFlow
Summary
by MITRE • 11/19/2022
TensorFlow is an open source platform for machine learning. Inputs `dense_features` or `example_state_data` not of rank 2 will trigger a `CHECK` fail in `SdcaOptimizer`. We have patched the issue in GitHub commit 80ff197d03db2a70c6a111f97dcdacad1b0babfa. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 12/20/2022
The vulnerability identified as CVE-2022-41899 affects TensorFlow, a widely adopted open source machine learning platform that serves as the foundation for numerous artificial intelligence applications across industries. This issue resides within the SdcaOptimizer component, which is responsible for implementing the Stochastic Dual Coordinate Ascent algorithm for solving machine learning optimization problems. The flaw represents a critical software defect that can be exploited through improper input handling, potentially leading to system instability and service disruption. The vulnerability specifically targets the validation of input tensors used in machine learning workflows, creating a scenario where malformed data can cause application failures.
The technical root cause of this vulnerability stems from insufficient input validation within the SdcaOptimizer's internal processing logic. When the optimizer receives input parameters labeled as `dense_features` or `example_state_data`, it performs a rank check using a `CHECK` macro that fails if these inputs do not conform to the expected two-dimensional tensor structure. This validation mechanism, designed to ensure data integrity and prevent processing errors, becomes a point of failure when encountering inputs that do not meet the required dimensional specifications. The flaw manifests as a runtime assertion failure that terminates the application process rather than gracefully handling the invalid input through proper error reporting mechanisms. This behavior aligns with CWE-617, which describes reachable assertion conditions that can be exploited to cause program termination or unexpected behavior.
The operational impact of this vulnerability extends beyond simple application crashes, as it can be leveraged by malicious actors to perform denial-of-service attacks against TensorFlow-based systems. Attackers could potentially craft specially formatted inputs that trigger the `CHECK` failure, causing the machine learning platform to become unavailable and disrupting ongoing training or inference operations. The vulnerability affects multiple versions of TensorFlow, including the major releases 2.8.4, 2.9.3, 2.10.1, and 2.11, indicating a widespread exposure across the TensorFlow ecosystem. Organizations relying on TensorFlow for critical machine learning workloads face significant risk of service interruption and potential data processing delays. The vulnerability's exploitation does not require elevated privileges, making it particularly dangerous as it can be triggered through normal input processing pathways.
The fix implemented by the TensorFlow development team addresses this issue through a comprehensive validation approach that properly handles inputs of varying dimensions. The solution involves modifying the input validation logic to ensure that `dense_features` and `example_state_data` parameters are appropriately checked before processing, with clear error handling mechanisms that prevent the assertion failure. The patch was integrated into the main codebase through GitHub commit 80ff197d03db2a70c6a111f97dcdacad1b0babfa, and the fix has been backported to support older versions within the supported release cycle. This remediation strategy aligns with the ATT&CK framework's concept of privilege escalation through software exploitation, as it addresses a fundamental flaw in the software's input handling that could otherwise be exploited to compromise system availability. Organizations should prioritize applying these patches to maintain the integrity and reliability of their machine learning infrastructure while ensuring continued service availability for critical AI workloads.