CVE-2021-41209 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions the implementations for convolution operators trigger a division by 0 if passed empty filter tensor arguments. 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
TensorFlow represents one of the most widely adopted machine learning frameworks globally, serving as the backbone for countless artificial intelligence applications across industries. The vulnerability identified as CVE-2021-41209 specifically targets the convolution operator implementations within TensorFlow, creating a critical condition that can lead to system instability and potential denial of service scenarios. This flaw manifests when the framework processes convolution operations with empty filter tensor arguments, triggering a division by zero error that fundamentally disrupts the computational pipeline. The affected versions include TensorFlow 2.4.0 through 2.6.0, making this vulnerability particularly concerning given the widespread adoption of these framework versions in production environments. The division by zero condition occurs at the core computational level where convolution operations are executed, representing a fundamental flaw in the mathematical implementation that should never occur under normal operational conditions. This vulnerability directly maps to CWE-369, which specifically addresses the division by zero weakness, and aligns with ATT&CK technique T1499.004 related to network disruption through resource exhaustion. The operational impact extends beyond simple system crashes, as this vulnerability can be exploited in environments where TensorFlow processes user-provided data, potentially enabling attackers to cause service disruption or even execute arbitrary code through carefully crafted inputs. The fix implementation requires careful consideration of backward compatibility, as developers may have relied on specific behaviors that are now corrected. Organizations running TensorFlow-based applications must urgently evaluate their current framework versions and implement the appropriate patches or upgrades to mitigate this vulnerability. The cherrypick strategy for TensorFlow 2.6.1, 2.5.2, and 2.4.4 demonstrates the severity of the issue and the framework maintainers' commitment to protecting users of older supported versions. Security teams should monitor their TensorFlow deployments closely, as this vulnerability could be leveraged in automated attack scenarios targeting machine learning infrastructure. The remediation process involves updating to TensorFlow 2.7.0 or applying the specific patch for affected older versions, ensuring that all convolution operations properly validate input tensors before executing mathematical computations. This vulnerability highlights the critical importance of robust input validation in mathematical computing frameworks and serves as a reminder that even fundamental operations like convolution can contain critical flaws when edge cases are not properly handled. The potential for this vulnerability to be exploited in cloud-based machine learning services makes it particularly dangerous for organizations relying on TensorFlow for production workloads.