CVE-2021-29587 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division(https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero. 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 • 05/16/2021
The vulnerability identified as CVE-2021-29587 resides within the TensorFlow Lite framework, specifically within the SpaceToDepth operator implementation. This flaw manifests in the Prepare step of the TFLite operator where inadequate input validation leads to a potential division by zero condition. The affected code segment is located in the space_to_depth.cc file at lines 63-67, where the parameter block_size is used in a division operation without proper validation to ensure it is not zero. This represents a classic software security vulnerability that can be exploited through crafted malicious models, demonstrating the importance of input validation in machine learning frameworks where model files can be manipulated by attackers.
The technical nature of this vulnerability aligns with CWE-369, which describes the condition where division by zero occurs in software systems. The flaw occurs because the Prepare function of the SpaceToDepth operator does not validate that params->block_size is non-zero before performing division operations. When an attacker crafts a malicious model with a block_size parameter set to zero, the division operation results in undefined behavior that can lead to application crashes, memory corruption, or potentially more severe exploitation vectors. This type of vulnerability is particularly concerning in machine learning environments where models are often processed from untrusted sources and where the execution environment may not properly handle such exceptional conditions.
The operational impact of this vulnerability extends beyond simple application instability to potentially enable more sophisticated attack vectors within machine learning deployment environments. When TensorFlow Lite processes models with malformed parameters, the division by zero condition can cause the entire inference process to fail or behave unpredictably, potentially leading to denial of service attacks against machine learning applications. The vulnerability affects multiple TensorFlow versions including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0, indicating this was a widespread issue that required coordinated patching across the supported release lines. This vulnerability demonstrates the critical importance of proper input validation in machine learning frameworks where model parameters can be manipulated to cause system instability or potential privilege escalation.
The mitigation strategy for CVE-2021-29587 involves applying the official patches released by TensorFlow as part of their security updates. The fix implements proper validation to ensure that block_size parameters are checked for zero values before any division operations are performed. Organizations deploying TensorFlow Lite applications should immediately upgrade to the patched versions including TensorFlow 2.5.0 and the cherry-picked versions for older supported releases. Additionally, deployment practices should include input validation of model files, particularly when processing models from untrusted sources, and implementing proper error handling mechanisms to prevent exploitation of similar validation flaws in other components of the machine learning pipeline. This vulnerability serves as a reminder of the security considerations that must be applied to machine learning frameworks, particularly in production environments where robustness against malformed inputs is essential for maintaining system integrity and preventing potential exploitation through crafted model files.