CVE-2021-41203 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions an attacker can trigger undefined behavior, integer overflows, segfaults and `CHECK`-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats. The fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 11/10/2021
The vulnerability identified as CVE-2021-41203 affects TensorFlow, a widely used open source machine learning platform that processes and analyzes large datasets through neural network models. This security flaw resides within TensorFlow's checkpoint loading infrastructure, which is responsible for restoring model states from saved files that contain learned parameters and configurations. The issue manifests when external entities can manipulate checkpoint files that are loaded into TensorFlow processes, creating a path for malicious actors to exploit the platform's data handling mechanisms.
The technical root cause of this vulnerability stems from insufficient validation within TensorFlow's checkpoint loading system, which allows for the processing of malformed or invalid file formats without proper sanitization. This missing validation creates conditions where attackers can craft specially formatted checkpoint files that trigger undefined behavior within the TensorFlow runtime environment. The flaw specifically enables integer overflows that can cause memory corruption, segmentation faults that result in program crashes, and CHECK-fail crashes that terminate TensorFlow processes abruptly. These behaviors represent fundamental instability conditions that can be leveraged to disrupt service availability or potentially execute arbitrary code depending on the execution context.
From an operational perspective, this vulnerability presents significant risks to machine learning environments that rely on TensorFlow for model deployment and training processes. The impact extends beyond simple service disruption as the undefined behavior and crashes can lead to data loss, model corruption, and extended downtime for ML workloads. Organizations using TensorFlow in production environments face potential exposure when checkpoint files might be compromised through various attack vectors such as unauthorized file system access, supply chain attacks, or manipulation of model distribution channels. The vulnerability affects multiple TensorFlow versions including 2.4.4, 2.5.2, 2.6.1, and requires immediate attention as these versions remain within supported release cycles, making them prime targets for exploitation.
The remediation strategy involves implementing comprehensive input validation for checkpoint file formats before processing, ensuring that all checkpoint data undergoes strict format verification and bounds checking. This approach aligns with CWE-129, which addresses validation of input buffers, and CWE-191, which covers integer underflow and overflow conditions. Security teams should also implement proper access controls and file integrity monitoring for checkpoint files, as recommended by the MITRE ATT&CK framework's technique T1078 for valid accounts and T1566 for phishing and supply chain compromise. The TensorFlow team has addressed this issue in versions 2.7.0 and has also backported the fixes to older supported versions, emphasizing the importance of timely patch management for maintaining secure machine learning environments and preventing exploitation of these fundamental runtime vulnerabilities.