CVE-2021-29598 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `SVDF` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/7f283ff806b2031f407db64c4d3edcda8fb9f9f5/tensorflow/lite/kernels/svdf.cc#L99-L102). An attacker can craft a model such that `params->rank` would be 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 • 05/16/2021
The vulnerability identified as CVE-2021-29598 affects TensorFlow's TensorFlow Lite implementation through the SVDF (Simplified Vector Decomposition Filter) operator, which is commonly used in speech recognition and audio processing applications. This flaw represents a classic division by zero error that occurs when the rank parameter is set to zero, creating a critical condition that can disrupt normal operation of machine learning inference pipelines. The vulnerability exists in the svdf.cc source file at lines 99-102 where the code fails to validate the rank parameter before performing division operations, making it susceptible to exploitation by malicious actors who can craft specific models to trigger this condition.
The technical implementation of the SVDF operator in TensorFlow Lite does not properly validate input parameters, specifically the rank parameter that controls the decomposition dimensions of the filter. When an attacker constructs a model where params->rank equals zero, the division operation within the kernel implementation results in a division by zero error that causes the application to crash or behave unpredictably. This type of vulnerability falls under CWE-369, Division by Zero, and represents a direct denial of service condition that can be exploited in environments where TensorFlow Lite is deployed for inference tasks. The vulnerability is particularly concerning because it can be triggered through model loading rather than runtime execution, making it a pre-authentication attack vector that affects the entire inference pipeline.
The operational impact of this vulnerability extends beyond simple denial of service, as it can affect production systems that rely on TensorFlow Lite for mobile and edge computing applications where audio processing and speech recognition are critical components. Systems utilizing TensorFlow Lite for applications such as voice assistants, smart speakers, and mobile applications that perform on-device inference are at risk of crashing when processing maliciously crafted models. The vulnerability affects multiple TensorFlow versions including 2.4.2, 2.3.3, 2.2.3, and 2.1.4, indicating that the flaw has persisted across several release cycles and affects organizations that may not have immediately updated to the latest versions. The fix addresses the root cause by implementing proper parameter validation before division operations, ensuring that the rank parameter cannot be zero during execution.
Organizations using TensorFlow Lite should implement immediate mitigations including updating to TensorFlow 2.5.0 or applying the cherry-picked fixes to affected versions within their supported release ranges. The ATT&CK framework categorizes this vulnerability under T1499.004, Endpoint Denial of Service, as it specifically targets the availability of machine learning inference systems. Security teams should monitor for any attempts to load malicious models that could trigger this condition, particularly in environments where model validation is not strictly enforced. Additionally, organizations should consider implementing runtime protections that can detect and prevent unusual parameter values from being processed by TensorFlow Lite kernels, and should ensure that all model deployment pipelines include proper validation steps to prevent exploitation of this class of vulnerability.