CVE-2021-29593 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `BatchToSpaceNd` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/b5ed552fe55895aee8bd8b191f744a069957d18d/tensorflow/lite/kernels/batch_to_space_nd.cc#L81-L82). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` 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 CVE-2021-29593 affects TensorFlow's TensorFlow Lite implementation and specifically targets the BatchToSpaceNd operator. This flaw manifests as a division by zero error within the kernel implementation, creating a potential denial of service condition that could be exploited by malicious actors. The issue resides in the batch_to_space_nd.cc file where the code fails to properly validate input parameters before performing mathematical operations. The vulnerability is particularly concerning because it can be triggered through crafted machine learning models that manipulate the block input dimensions to zero values. When such conditions occur, the corresponding block_shape values become zero, leading to division operations that attempt to divide by zero, causing the application to crash or behave unpredictably.
The technical implementation of this vulnerability stems from inadequate input validation within the TFLite operator. The BatchToSpaceNd operator is designed to rearrange data from batch dimensions to spatial dimensions, but the implementation does not sufficiently check that block dimensions are non-zero before performing division operations. This represents a classic software flaw where mathematical operations are performed without proper boundary checking, falling under CWE-369 - Divide by Zero. The specific lines of code referenced in the vulnerability description show the problematic division operations that occur when block_shape values equal zero, creating a condition where the program cannot continue execution properly. This type of vulnerability is particularly dangerous in machine learning environments where models are often loaded from untrusted sources and executed in production systems.
The operational impact of this vulnerability extends beyond simple denial of service to potentially compromise entire machine learning pipelines. Attackers can craft malicious models that exploit this division by zero condition to crash TensorFlow Lite inference engines, making it impossible to process legitimate requests. In production environments, this could lead to service disruption, resource exhaustion, or even allow for more sophisticated attacks if the system is not properly isolated. The vulnerability affects multiple TensorFlow versions including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0, indicating a widespread issue across the supported release lines. The fix implemented addresses the core validation issue by ensuring proper input parameter checking before mathematical operations are performed, which aligns with ATT&CK technique T1059.001 - Command and Scripting Interpreter for preventing execution of malicious code through input manipulation.
Security practitioners should prioritize patching affected TensorFlow versions as soon as possible, particularly in environments where untrusted models are processed. The mitigation strategy involves updating to TensorFlow 2.5.0 or applying the cherry-picked fixes to older supported versions. Organizations should also implement strict model validation procedures and sandboxing mechanisms when processing machine learning models from external sources. The vulnerability highlights the importance of input validation in mathematical operations within machine learning frameworks, particularly for operators that perform complex data transformations. Additionally, continuous monitoring and automated testing of model inputs can help detect and prevent exploitation attempts before they can cause system disruption, aligning with security best practices for protecting machine learning infrastructure against similar mathematical error vulnerabilities.