CVE-2021-29528 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedMul`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55900e961ed4a23b438392024912154a2c2f5e85/tensorflow/core/kernels/quantized_mul_op.cc#L188-L198) does a division by a quantity that is controlled by the caller. 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-2021-29528 affects TensorFlow, a widely adopted open source platform for machine learning that serves as the foundation for numerous artificial intelligence applications across industries. This issue resides within the tf.raw_ops.QuantizedMul operation, which represents a critical component in the quantization process commonly used to optimize neural network models for deployment. The vulnerability manifests as a division by zero error that occurs when processing quantized multiplication operations, creating a potential denial of service condition that could disrupt machine learning workflows and applications relying on TensorFlow's inference capabilities.
The technical flaw stems from an implementation weakness in the quantized multiplication kernel located at tensorflow/core/kernels/quantized_mul_op.cc, specifically within lines 188-198 of the source code. The problematic code performs a division operation where the divisor is derived from user-provided parameters that control the quantization process, including scale factors and zero points used for converting floating point values to quantized representations. When an attacker supplies malicious input parameters that result in a zero value for the divisor, the system encounters a division by zero exception, causing the operation to crash or behave unpredictably. This represents a classic input validation vulnerability where external data directly influences arithmetic operations without proper bounds checking or sanitization.
The operational impact of this vulnerability extends beyond simple denial of service, as it could enable attackers to disrupt machine learning pipelines and inference systems that depend on TensorFlow's quantized operations. In production environments where TensorFlow serves as a core component of AI applications, this vulnerability could be exploited to terminate critical services, potentially affecting applications in healthcare, finance, autonomous vehicles, and other domains where reliable machine learning inference is essential. The vulnerability affects multiple TensorFlow versions including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and the fix is integrated into the 2.5.0 release, indicating that organizations running these older versions face significant risk and require immediate patching to maintain system integrity and availability.
Organizations should implement immediate mitigation strategies including applying the patched TensorFlow versions as specified in the advisory, deploying input validation measures to sanitize quantization parameters before processing, and implementing monitoring to detect potential exploitation attempts. The vulnerability aligns with CWE-369, which categorizes division by zero errors as a fundamental security weakness, and could be mapped to ATT&CK technique T1499.004 for network denial of service attacks. Additionally, organizations should consider implementing runtime protections such as sandboxing quantization operations and employing automated patch management systems to ensure all TensorFlow components remain current with security fixes. The remediation approach emphasizes the importance of proper parameter validation in mathematical operations and demonstrates the critical need for comprehensive input sanitization in machine learning frameworks where external inputs directly influence computational processes.