CVE-2021-29546 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`. This is because the implementation of the Eigen kernel(https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero. 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-2021-29546 affects TensorFlow, a widely used open source machine learning platform that powers numerous applications across industries. This issue manifests as an integer division by zero condition within the tf.raw_ops.QuantizedBiasAdd operation, representing a critical undefined behavior that can compromise system stability and potentially enable further exploitation. The flaw exists in the Eigen kernel implementation of the quantization utilities, specifically in the quantization_utils.h file where mathematical operations are performed without proper input validation. The vulnerability stems from a fundamental lack of input sanitization where the code performs division operations based on the number of elements from the smaller input tensor without first verifying that this value is non-zero, creating a direct path for arithmetic exceptions to occur.
The technical implementation of this vulnerability occurs within the quantization kernel that handles bias addition operations for quantized tensors, which are commonly used in neural network inference to reduce computational overhead and memory usage. When TensorFlow processes quantized bias addition operations, the kernel attempts to divide by the number of elements in the smaller input tensor to perform scaling calculations. This division operation becomes problematic when the smaller tensor has zero elements, causing a division by zero error that leads to undefined behavior and potential system crashes. The vulnerability is classified under CWE-369 as a division by zero error, which represents a well-known software weakness that can result in denial of service conditions and potentially more severe consequences when combined with other exploitation techniques. This flaw affects the core computational operations that underpin machine learning inference, making it particularly dangerous for production environments where TensorFlow is deployed for critical applications.
The operational impact of CVE-2021-29546 extends beyond simple system instability, as it can be exploited by attackers to cause denial of service conditions in machine learning workloads that utilize quantized operations. Systems relying on TensorFlow for inference tasks, including those in cloud computing environments, edge devices, and AI-powered applications, could experience complete service disruption when encountering malformed inputs that trigger the division by zero condition. The vulnerability affects multiple TensorFlow versions including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and the affected range includes versions that are still actively supported, indicating the severity of the issue. Attackers could potentially leverage this vulnerability in environments where TensorFlow is used for processing untrusted data inputs, such as in web applications or data ingestion pipelines, where crafted inputs could trigger the vulnerable code path and cause system instability. The impact is particularly concerning given TensorFlow's widespread adoption across enterprise and research environments where continuous operation is critical.
The recommended mitigations for this vulnerability involve immediate patching of affected TensorFlow installations to versions 2.5.0 or the cherry-picked fixes for older supported versions. Organizations should prioritize updating their TensorFlow deployments to ensure that the division by zero check is properly implemented in the quantization utilities. The fix addresses the root cause by adding proper validation to check that the divisor is non-zero before performing division operations, which aligns with standard security practices for preventing arithmetic exceptions. Additionally, system administrators should implement monitoring and logging mechanisms to detect potential exploitation attempts, particularly in environments where TensorFlow processes untrusted inputs. Security teams should also consider implementing input validation layers and sandboxing techniques to further reduce the attack surface when processing external data through TensorFlow pipelines. The vulnerability demonstrates the importance of thorough input validation in mathematical operations within machine learning frameworks, as these operations often form the core of computational pipelines that require robust error handling to prevent system-wide failures. Organizations utilizing TensorFlow for production workloads should conduct comprehensive testing after applying the patches to ensure that no regressions have occurred in their machine learning applications while maintaining the security improvements.