CVE-2021-41211 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `QuantizeV2` can trigger a read outside of bounds of heap allocated array. This occurs whenever `axis` is a negative value less than `-1`. In this case, we are accessing data before the start of a heap buffer. The code allows `axis` to be an optional argument (`s` would contain an `error::NOT_FOUND` error code). Otherwise, it assumes that `axis` is a valid index into the dimensions of the `input` tensor. If `axis` is less than `-1` then this results in a heap OOB read. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.
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Analysis
by VulDB Data Team • 11/10/2021
The vulnerability CVE-2021-41211 affects TensorFlow, a widely-used open source machine learning platform that processes tensor operations through its computational graph framework. This security flaw resides in the shape inference mechanism specifically for the QuantizeV2 operation, which is commonly employed in quantization processes to reduce model size and improve inference performance. The issue manifests when the axis parameter receives a negative value less than -1, creating a critical memory access violation that can compromise system integrity.
The technical flaw stems from improper validation of the axis parameter within the QuantizeV2 shape inference code. When axis is set to a value less than -1, the implementation performs a calculation that results in accessing memory locations before the beginning of a heap-allocated array buffer. The code structure initially checks for the presence of the axis argument, where it may return an error::NOT_FOUND status code when absent. However, when axis is present but negative, the system assumes it represents a valid tensor dimension index without proper boundary validation. This assumption leads to an out-of-bounds read condition where the program attempts to access data prior to the allocated buffer boundaries, creating potential for information disclosure or arbitrary code execution.
The operational impact of this vulnerability extends across various TensorFlow deployments including research environments, production inference systems, and mobile applications that utilize quantization techniques. Attackers could potentially exploit this flaw by crafting malicious inputs with specifically negative axis values, causing the system to read unauthorized memory regions. This could result in sensitive information leakage from adjacent memory locations, system instability, or even remote code execution depending on the specific implementation context. The vulnerability affects TensorFlow versions prior to 2.7.0, with the affected 2.6.1 version receiving a targeted patch to address the issue.
Mitigation strategies for this vulnerability should prioritize immediate deployment of TensorFlow 2.7.0 releases or the cherry-picked fixes for version 2.6.1. Organizations should also implement input validation controls to prevent negative axis values less than -1 from reaching the QuantizeV2 operation. The fix addresses the core issue by implementing proper boundary checks for axis parameter validation, ensuring that negative indices are properly constrained to valid tensor dimension ranges. This vulnerability aligns with CWE-129, which covers improper validation of array indices, and represents a specific instance of memory safety issues that can be addressed through robust input sanitization and proper bounds checking mechanisms. Security teams should monitor their TensorFlow deployments to ensure all affected versions are updated and validate that quantization workflows properly handle axis parameter validation to prevent exploitation of this heap-based out-of-bounds read condition.