CVE-2021-41207 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions the implementation of `ParallelConcat` misses some input validation and can produce a division by 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-2021-41207 resides within TensorFlow's implementation of the `ParallelConcat` operation, a critical component in machine learning workflows that handles parallel concatenation of tensors. This flaw represents a classic input validation failure that can lead to severe operational consequences in environments relying on TensorFlow's distributed computing capabilities. The issue affects multiple versions of the platform including 2.4.4, 2.5.2, 2.6.1, and the broader affected range, demonstrating the widespread impact across TensorFlow's supported release lines.
The technical root cause of this vulnerability lies in the insufficient validation of input parameters within the `ParallelConcat` function implementation. Specifically, the code fails to properly validate division operations that occur during tensor processing, creating a scenario where a division by zero condition can manifest when processing certain malformed inputs. This type of vulnerability falls under CWE-369, which categorizes improper validation leading to division by zero errors, and represents a direct violation of input sanitization principles. The flaw is particularly dangerous because it can be triggered through crafted inputs that exploit the parallel processing nature of the operation, potentially allowing malicious actors to disrupt tensor computations.
The operational impact of this vulnerability extends beyond simple computational errors, as it can lead to system instability and potential denial of service conditions in machine learning environments. When a division by zero occurs within TensorFlow's computational graph processing, it can cause the entire training or inference pipeline to crash, resulting in significant downtime and data loss. This vulnerability particularly affects environments where TensorFlow is used for large-scale machine learning workloads, including cloud-based training platforms and enterprise AI systems. The security implications are compounded by the fact that this vulnerability can be exploited through input manipulation, making it a potential vector for attackers to disrupt machine learning operations and potentially gain unauthorized access to computational resources.
Organizations using affected TensorFlow versions should immediately implement mitigation strategies focusing on input validation and system hardening. The recommended approach includes upgrading to TensorFlow 2.7.0 or applying the cherry-picked fixes to versions 2.6.1, 2.5.2, and 2.4.4 as specified in the advisory. Additionally, implementing robust input sanitization measures and monitoring for anomalous computational patterns can help detect exploitation attempts. From an ATT&CK framework perspective, this vulnerability aligns with techniques involving privilege escalation and denial of service, potentially enabling adversaries to disrupt machine learning infrastructure. Security teams should also consider implementing network segmentation and access controls around TensorFlow deployment environments to limit potential exploitation vectors and reduce the attack surface of these critical AI platforms.