CVE-2022-41883 in TensorFlow
Summary
by MITRE • 11/19/2022
TensorFlow is an open source platform for machine learning. When ops that have specified input sizes receive a differing number of inputs, the executor will crash. We have patched the issue in GitHub commit f5381e0e10b5a61344109c1b7c174c68110f7629. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 12/20/2022
TensorFlow represents one of the most widely adopted machine learning frameworks globally, serving as the foundation for countless artificial intelligence applications across industries. The vulnerability identified as CVE-2022-41883 specifically targets the framework's execution engine and demonstrates a critical flaw in input validation mechanisms. This issue manifests when operations within TensorFlow's computational graph receive inputs that differ from their expected specifications, leading to abrupt system termination. The root cause lies in the executor's inadequate handling of mismatched input counts, which creates a denial of service condition that can be exploited to disrupt machine learning workflows.
The technical implementation of this vulnerability stems from insufficient bounds checking within TensorFlow's operation execution pipeline. When a computational operation is defined with specific input size requirements, the framework's executor fails to properly validate incoming data against these constraints. This validation gap allows malformed input sequences to bypass normal execution paths, causing the system to crash during operation execution. The vulnerability is particularly concerning because it affects core framework functionality that underpins all machine learning processes, making it a critical security concern for any organization relying on TensorFlow for AI workloads.
From an operational perspective, this vulnerability creates significant risk for machine learning environments that process external or user-provided data inputs. Attackers could exploit this flaw to cause service disruption by submitting carefully crafted inputs that trigger the executor crash. The impact extends beyond simple denial of service, as it can interrupt ongoing training processes, inference operations, and automated ML pipelines. Organizations utilizing TensorFlow in production environments face potential business disruption, data loss, and service unavailability when this vulnerability is exploited. The vulnerability affects multiple supported versions of TensorFlow, including 2.8.4, 2.9.3, 2.10.1, and requires immediate attention for all affected deployments.
The fix implemented by TensorFlow developers addresses this issue through enhanced input validation mechanisms that properly check input count consistency before operation execution. The patch, committed to GitHub as f5381e0e10b5a61344109c1b7c174c68110f7629, implements proper bounds checking that prevents the executor from crashing when encountering mismatched input specifications. This remediation follows established security practices for preventing denial of service attacks and aligns with CWE-129, which addresses improper validation of input boundaries. The fix has been integrated into TensorFlow 2.11 and backported to older supported versions, demonstrating the project's commitment to maintaining security across its supported release lifecycle. Organizations should prioritize upgrading to patched versions or implementing the cherry-picked fixes to mitigate this vulnerability effectively.
This vulnerability relates to ATT&CK technique T1499.004, which involves network denial of service attacks through resource exhaustion or system crashes. The patched implementation enhances the framework's resilience against malformed input processing, reducing attack surface for adversarial exploitation. Security teams should monitor their TensorFlow deployments for proper patch application and consider implementing additional input validation layers as defensive measures. The vulnerability serves as a reminder of the importance of robust input validation in complex software systems and the critical need for thorough testing of edge cases in machine learning frameworks.