CVE-2021-29588 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. The optimized implementation of the `TransposeConv` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L5221-L5222). An attacker can craft a model such that `stride_{h,w}` values are 0. Code calling this function must validate these arguments. 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-2021-29588 affects TensorFlow, a widely-used open-source machine learning platform that provides end-to-end capabilities for developing and deploying machine learning models. This specific flaw resides within the TensorFlow Lite (TFLite) framework's optimized implementation of the TransposeConv operator, which is a critical component for performing transposed convolution operations in mobile and embedded applications. The issue manifests as a division by zero error that occurs when processing certain model inputs, creating a potential denial of service condition that could disrupt machine learning workflows on devices utilizing TensorFlow Lite.
The technical flaw stems from insufficient validation of stride parameters within the optimized TFLite operator implementation. Specifically, when the `stride_{h,w}` values are set to zero, the code attempts to perform a division operation that results in a runtime error. This occurs at line 5221-5222 in the optimized_ops.h file where the division by zero vulnerability exists. The vulnerability is particularly concerning because it allows attackers to craft malicious models that deliberately set these stride values to zero, enabling them to trigger the division by zero condition. According to CWE-369, this represents a division by zero weakness that can lead to application crashes and system instability. The vulnerability affects the TFLite kernel implementation and specifically targets the optimized version of the TransposeConv operation, making it particularly impactful for performance-critical applications that rely on optimized code paths.
The operational impact of this vulnerability extends beyond simple application crashes, as it can be exploited to cause denial of service conditions in environments where TensorFlow Lite is deployed. This affects mobile applications, embedded systems, and edge devices that utilize TensorFlow Lite for machine learning inference, potentially disrupting services and user experiences. The vulnerability is particularly dangerous in production environments where model loading and execution are automated, as an attacker could craft a malicious model that causes system failures when processed. This aligns with ATT&CK technique T1499.004 which describes network denial of service attacks that can be executed through software vulnerabilities. The affected versions include TensorFlow 2.4.2, 2.3.3, 2.2.3, and 2.1.4, indicating that this vulnerability has been present for multiple release cycles and affects a significant portion of the supported TensorFlow user base. The fix addresses the core validation issue by ensuring proper parameter checking before division operations are performed.
The remediation strategy for CVE-2021-29588 involves implementing proper input validation for stride parameters within the TransposeConv TFLite operator. The TensorFlow development team has addressed this issue by incorporating validation checks that prevent zero values from being processed in the stride parameters, thereby eliminating the division by zero condition. The fix has been included in TensorFlow 2.5.0 and backported to older supported versions, demonstrating the maintainers' commitment to supporting affected users. Organizations should prioritize upgrading to TensorFlow 2.5.0 or applying the appropriate patches to their existing installations. Additionally, implementing proper model validation procedures during the ingestion phase can help prevent malicious models from being processed in production environments, providing an additional layer of defense against exploitation attempts. The vulnerability serves as a reminder of the importance of input validation in performance-critical code paths and the need for comprehensive testing of edge cases in machine learning frameworks.