CVE-2021-29517 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. 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-2021-29517 affects TensorFlow, a widely-used open-source machine learning platform that serves as the foundation for numerous artificial intelligence applications across various industries. This security flaw resides within the Conv3D implementation of TensorFlow's kernel operations, specifically in the conv_ops_3d.cc file where the mathematical operations for three-dimensional convolution are handled. The issue stems from insufficient input validation mechanisms that fail to properly sanitize user-provided parameters before processing them in critical mathematical operations.
The technical flaw manifests through a division by zero error that occurs when a malicious actor manipulates the filter parameter of the Conv3D operation. Specifically, the implementation performs a modulo operation using user-controlled input values, creating a condition where if the fifth element of the filter tensor contains a zero value, the modulo operation results in division by zero. This type of vulnerability maps directly to CWE-369, which categorizes "Division by Zero" as a critical security weakness that can lead to program crashes or unauthorized access. The mathematical operation at line 143-145 in the source code creates an exploitable condition where arbitrary input data can disrupt normal program execution flow.
The operational impact of this vulnerability extends beyond simple program crashes, as it represents a potential denial-of-service attack vector that could be exploited in environments where TensorFlow processes untrusted data inputs. When the shape validation fails between tensors, additional Eigen assertions can be triggered, causing further program instability and potential system crashes. This vulnerability affects multiple versions of TensorFlow including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and the affected versions are still within supported ranges, making the potential attack surface particularly broad. The vulnerability's exploitation risk is elevated because it can be triggered through normal machine learning workflow operations where users might provide malformed tensor shapes or filter parameters.
The mitigation strategy implemented by TensorFlow developers involves patching the codebase to prevent division by zero conditions and to properly validate tensor dimensions before performing mathematical operations. The fix will be included in TensorFlow 2.5.0 release, with cherry-picked updates for older supported versions to ensure backward compatibility and security coverage. Organizations utilizing TensorFlow should prioritize upgrading to patched versions as recommended by the security advisory. The vulnerability also aligns with ATT&CK technique T1499.004, which covers "File System Wipe" through system resource exhaustion, as the denial-of-service conditions could potentially be leveraged to disrupt machine learning workflows and data processing pipelines. Security teams should monitor for potential exploitation attempts in environments where TensorFlow processes external or untrusted data inputs, particularly in cloud-based machine learning services or automated AI processing systems.