CVE-2021-41226 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseBinCount` is vulnerable to a heap OOB access. This is because of missing validation between the elements of the `values` argument and the shape of the sparse output. 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 CVE-2021-41226 affects TensorFlow, a widely-used open source machine learning platform that powers numerous applications across industries. This security flaw resides within the SparseBinCount operation, which is part of TensorFlow's sparse tensor manipulation capabilities. The issue represents a heap out-of-bounds (OOB) access vulnerability that can potentially be exploited by malicious actors to execute arbitrary code or cause system instability. The vulnerability impacts multiple TensorFlow versions including 2.4.4, 2.5.2, 2.6.1, and affects the broader TensorFlow 2.x ecosystem.
The technical flaw stems from insufficient validation mechanisms within the SparseBinCount implementation where the system fails to properly validate the relationship between the elements of the values argument and the shape of the sparse output tensor. This missing validation creates a condition where memory access operations can extend beyond allocated heap boundaries, leading to potential memory corruption. The vulnerability manifests when processing sparse tensor data structures where the input values exceed the bounds of the expected output dimensions, creating a scenario where adjacent memory locations can be accessed or modified. This type of vulnerability falls under CWE-125, which specifically addresses out-of-bounds read conditions, and can be categorized under ATT&CK technique T1059.001 for command and scripting interpreter usage in exploitation scenarios.
The operational impact of this vulnerability extends beyond simple memory corruption, as it can lead to denial of service conditions, data integrity compromises, and potentially remote code execution depending on the system configuration. Systems that process large volumes of sparse tensor data, particularly those in production environments or cloud deployments, face significant risk from this vulnerability. The affected nature of multiple TensorFlow versions means that organizations running older releases remain at risk, emphasizing the importance of timely patch management and version control strategies. This vulnerability particularly impacts machine learning pipelines that utilize sparse tensor operations, which are common in natural language processing, recommendation systems, and large-scale data analysis applications.
Mitigation strategies for CVE-2021-41226 involve immediate deployment of the patched TensorFlow versions, specifically TensorFlow 2.7.0 with cherry-picked fixes for 2.6.1, 2.5.2, and 2.4.4. Organizations should conduct comprehensive vulnerability assessments to identify systems running affected TensorFlow versions and prioritize patching efforts accordingly. Additionally, implementing runtime monitoring and input validation controls can provide additional defense-in-depth measures. Security teams should consider restricting network access to TensorFlow processing systems and implementing proper access controls to limit potential exploitation vectors. The fix addresses the core validation issue by ensuring proper bounds checking between input values and expected output dimensions, thereby preventing the heap OOB access condition that enabled the vulnerability.