CVE-2021-29595 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthToSpace` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/depth_to_space.cc#L63-L69). An attacker can craft a model such that `params->block_size` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 05/19/2021
The vulnerability identified as CVE-2021-29595 affects TensorFlow's TensorFlow Lite implementation through a critical division by zero error in the DepthToSpace operator. This operator is commonly used in machine learning models for transforming tensor data from depth to spatial dimensions, particularly in convolutional neural networks and image processing pipelines. The flaw exists in the kernel implementation where the code does not properly validate input parameters before performing mathematical operations, creating a scenario where maliciously crafted models can trigger unexpected behavior. The specific issue occurs at line 63-69 in the depth_to_space.cc file where the block_size parameter is used directly in a division operation without proper bounds checking, making it susceptible to zero division errors.
The technical exploitation of this vulnerability requires an attacker to construct a malicious TensorFlow Lite model that sets the params->block_size parameter to zero during the DepthToSpace operation execution. This condition directly leads to a division by zero error that can cause the application to crash or behave unpredictably, potentially leading to denial of service conditions. The vulnerability is classified under CWE-369 as a division by zero error, which represents a fundamental programming flaw that can result in application instability and potential information disclosure. When such a model is processed by TensorFlow Lite, the runtime environment encounters the mathematical impossibility of dividing by zero, causing an immediate system failure that prevents legitimate model execution.
The operational impact of this vulnerability extends beyond simple denial of service scenarios, as it can affect any system utilizing TensorFlow Lite for mobile or embedded machine learning applications. This includes mobile applications, edge devices, and IoT systems that rely on TensorFlow Lite for on-device inference. The vulnerability affects multiple TensorFlow versions including 2.4.2, 2.3.3, 2.2.3, and 2.1.4, indicating a widespread exposure across supported release lines. Attackers could potentially exploit this vulnerability to disrupt services, particularly in environments where TensorFlow Lite is used for critical inference tasks such as image recognition, natural language processing, or autonomous vehicle systems. The fix addresses the core issue by implementing proper parameter validation before any mathematical operations are performed, ensuring that block_size values are checked for zero before division operations occur.
Mitigation strategies for this vulnerability should prioritize immediate patching of affected TensorFlow versions to 2.5.0 or the corresponding cherry-picked fixes for older supported releases. Organizations should also implement runtime monitoring to detect anomalous model loading patterns that might indicate attempts to exploit this vulnerability. Additional defensive measures include validating all input models through static analysis tools that can identify potentially malicious parameter configurations before deployment. Security teams should consider implementing sandboxing mechanisms for model execution environments to limit the impact of any potential exploitation attempts. The vulnerability demonstrates the importance of input validation in machine learning frameworks and highlights the need for comprehensive testing of mathematical operations within AI inference engines, particularly those handling user-provided models in production environments. This case represents a typical example of how seemingly simple mathematical operations in AI frameworks can become security risks when proper validation mechanisms are absent, aligning with ATT&CK technique T1059.001 for execution through command and scripting interpreter.