CVE-2021-29599 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `Split` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e2752089ef7ce9bcf3db0ec618ebd23ea119d0c7/tensorflow/lite/kernels/split.cc#L63-L65). An attacker can craft a model such that `num_splits` 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 • 05/19/2021
The vulnerability identified as CVE-2021-29599 affects TensorFlow's TensorFlow Lite implementation where the Split operator contains a division by zero error in its kernel implementation. This flaw exists within the tflite/kernels/split.cc file at lines 63-65, where the code does not properly validate the number of splits parameter before performing division operations. The issue stems from the absence of input validation that would prevent the num_splits variable from being set to zero, which directly leads to the division by zero condition. When an attacker crafts a malicious model with num_splits equal to zero, the system encounters a runtime error that can potentially lead to application crashes or unexpected behavior.
This vulnerability represents a classic implementation flaw that falls under CWE-369, which specifically addresses the division by zero error condition. The flaw occurs in the operational context of machine learning inference where TensorFlow Lite models are executed on edge devices or mobile platforms. The attack vector involves supplying a malformed model that includes a Split operator with zero splits, which causes the kernel to attempt division by zero during the model execution phase. The vulnerability is particularly concerning because it can be exploited during model loading and execution, potentially leading to denial of service conditions where legitimate model execution is disrupted.
The operational impact of this vulnerability extends beyond simple crash conditions to potentially enable more sophisticated attacks depending on the execution environment. When the division by zero occurs during TensorFlow Lite model processing, it can cause the application to terminate abruptly or enter an undefined state, effectively preventing legitimate machine learning workloads from executing properly. This represents a significant risk in production environments where TensorFlow Lite is used for critical inference tasks, as the vulnerability can be triggered through model input manipulation without requiring elevated privileges or complex attack chains. The affected versions include TensorFlow 2.1.4 through 2.4.1, making it a widespread issue that impacts multiple release branches of the platform.
The fix for this vulnerability involves implementing proper input validation to ensure that num_splits is never zero before performing the division operation. The TensorFlow team has addressed this issue by including the fix in TensorFlow 2.5.0 and cherry-picking the commit to maintain backward compatibility for supported older versions including 2.4.2, 2.3.3, 2.2.3, and 2.1.4. This approach aligns with standard security practices for maintaining supported release branches and ensuring that critical vulnerabilities are patched across all actively supported versions of the software. Organizations using TensorFlow Lite should prioritize upgrading to patched versions or applying the cherry-picked fixes to mitigate this vulnerability. The remediation process should include thorough testing of models to ensure that the validation changes do not introduce regressions in legitimate model functionality while effectively preventing the division by zero condition.