CVE-2021-41218 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `AllToAll` can be made to execute a division by 0. This occurs whenever the `split_count` argument is 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 11/10/2021
The vulnerability identified as CVE-2021-41218 affects TensorFlow, a widely-used open source machine learning platform that powers numerous artificial intelligence applications across various industries. This security flaw resides within the shape inference mechanism of the AllToAll operation, which is a collective communication primitive used in distributed machine learning scenarios. The issue manifests when the split_count argument is explicitly set to zero, creating a condition that leads to division by zero errors during the computational graph analysis phase.
The technical implementation of this vulnerability stems from inadequate input validation within TensorFlow's internal shape inference routines. When processing the AllToAll operation, the system attempts to perform mathematical operations without properly verifying that the split_count parameter remains within valid bounds. This particular edge case creates a division by zero scenario that can cause the TensorFlow runtime to crash or behave unpredictably, potentially leading to denial of service conditions that disrupt machine learning workflows. The flaw exists in the symbolic execution phase of the computational graph rather than during actual model execution, making it particularly insidious as it can be triggered during model compilation or analysis stages.
From an operational perspective, this vulnerability presents significant risks to organizations relying on TensorFlow for production machine learning workloads, especially in distributed computing environments where AllToAll operations are commonly employed. The division by zero condition can cause complete system termination, forcing administrators to restart services and potentially lose ongoing training processes. Given that TensorFlow is used extensively in critical applications such as autonomous vehicles, financial modeling, healthcare diagnostics, and recommendation systems, this vulnerability could impact business continuity and data processing workflows. The affected versions include several major releases that remain in active support, amplifying the potential impact across the TensorFlow user base.
The remediation strategy for CVE-2021-41218 involves implementing proper input validation to ensure that the split_count parameter cannot assume a zero value during AllToAll operation processing. This fix aligns with common software security practices and follows the principle of defensive programming, where all inputs are validated before processing. Organizations should prioritize upgrading to TensorFlow 2.7.0 or applying the cherry-picked fixes to their supported versions 2.6.1, 2.5.2, and 2.4.4. This vulnerability classification corresponds to CWE-369, which specifically addresses the division by zero error condition, and could be leveraged by threat actors as part of broader attack vectors targeting machine learning infrastructure. The fix demonstrates proper error handling and input validation practices that are essential for maintaining system stability in distributed computing environments.
This vulnerability also relates to ATT&CK technique T1499.004, which covers network disruption through resource exhaustion or system crashes, as the division by zero condition can cause system instability. The remediation approach should include implementing comprehensive testing procedures that validate edge cases in distributed communication operations, particularly focusing on parameter validation in collective communication primitives. Organizations should also consider implementing monitoring solutions that can detect abnormal system behavior patterns that might indicate exploitation attempts, ensuring that machine learning infrastructure remains resilient against both intentional attacks and accidental system failures. The vulnerability serves as a reminder of the importance of robust error handling in complex distributed systems where mathematical operations can have cascading effects on overall system stability.