CVE-2021-37686 in TensorFlow
Summary
by MITRE • 08/13/2021
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for [ellipsis in axis definition](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122). An attacker can craft a model such that `ellipsis_end_idx` is smaller than `i` (e.g., always negative). In this case, the inner loop does not increase `i` and the `continue` statement causes execution to skip over the preincrement at the end of the outer loop. We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. TensorFlow 2.6.0 is the only affected version.
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Analysis
by VulDB Data Team • 08/17/2021
The vulnerability CVE-2021-37686 resides within TensorFlow Lite's strided slice implementation, representing a critical logic flaw that can be exploited to create infinite loops during model execution. This issue specifically affects TensorFlow version 2.6.0 where the strided slice operation was enhanced to support ellipsis in axis definitions. The vulnerability manifests when an attacker crafts a malicious model that manipulates the `ellipsis_end_idx` parameter to be smaller than the loop counter `i`, typically through negative values that cause the loop control logic to fail. This flaw falls under CWE-835, which describes the weakness of infinite loops or infinite recursion, and demonstrates how improper loop termination conditions can lead to denial of service scenarios.
The technical exploitation of this vulnerability occurs during the execution of TFLite models where the strided slice operation processes tensor data with specific axis configurations. When the ellipsis support was introduced, the implementation failed to properly validate the relationship between `ellipsis_end_idx` and the loop variable `i`. The problematic code path begins at line 103-122 of the strided_slice.cc file, where the logic does not account for cases where negative indexing could cause the inner loop to never advance the outer loop counter. This creates a scenario where the continue statement bypasses the normal loop increment mechanism, effectively creating a condition where `i` remains constant while the loop continues indefinitely. The issue is particularly dangerous because it can be triggered through model input manipulation rather than requiring access to the underlying system, making it a remote code execution risk in certain contexts.
The operational impact of this vulnerability extends beyond simple denial of service, as it represents a potential vector for resource exhaustion attacks that could compromise system stability and availability. When exploited, the infinite loop consumes CPU cycles continuously without yielding control back to the operating system, potentially leading to system performance degradation or complete system hangs. This vulnerability is particularly concerning in edge computing environments where TensorFlow Lite is commonly deployed, as these systems may have limited resources and are more susceptible to resource exhaustion attacks. The flaw also aligns with ATT&CK technique T1499.004, which covers resource exhaustion attacks, and demonstrates how seemingly benign feature additions can introduce critical security weaknesses. Organizations deploying TensorFlow Lite applications must consider this vulnerability as a potential threat to system availability and operational continuity.
Mitigation strategies for CVE-2021-37686 should focus on immediate version upgrades to TensorFlow 2.6.1 or later, where the fix has been implemented through the GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. This patch corrects the loop termination logic by ensuring proper validation of the relationship between `ellipsis_end_idx` and the loop counter `i`, preventing the scenario where `i` remains unchanged during loop execution. Additionally, organizations should implement strict model validation procedures that include static analysis of TFLite models for potentially malicious axis configurations, particularly those involving negative indexing that could trigger the vulnerable code path. Security monitoring should be enhanced to detect unusual CPU consumption patterns that might indicate exploitation attempts, and deployment environments should be configured with resource limits to prevent complete system exhaustion. The fix also emphasizes the importance of thorough regression testing for new features, particularly those involving complex loop structures and indexing operations that could introduce subtle logic flaws.