CVE-2022-35969 in TensorFlow
Summary
by MITRE • 09/17/2022
TensorFlow is an open source platform for machine learning. The implementation of `Conv2DBackpropInput` requires `input_sizes` to be 4-dimensional. Otherwise, it gives a `CHECK` failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 50156d547b9a1da0144d7babe665cf690305b33c. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
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Analysis
by VulDB Data Team • 10/19/2022
The vulnerability identified as CVE-2022-35969 affects the TensorFlow machine learning platform and specifically targets the Conv2DBackpropInput operation within the framework's computational graph implementation. This flaw exists in the tensor processing pipeline where the system expects input_sizes parameter to conform to a specific dimensional requirement of exactly four dimensions. The vulnerability manifests when this dimensional constraint is violated, triggering a CHECK failure mechanism that fundamentally disrupts the normal execution flow of the machine learning inference or training processes. The issue represents a classic denial of service vulnerability that can be exploited by malicious actors to disrupt services without requiring authentication or elevated privileges, making it particularly concerning for production environments where TensorFlow is deployed for critical AI workloads.
The technical implementation flaw stems from insufficient input validation within the Conv2DBackpropInput function which lacks proper dimensionality checks before proceeding with computational operations. When the input_sizes parameter contains fewer than four dimensions, the system's internal validation mechanism fails and generates a CHECK failure that terminates the process rather than gracefully handling the error condition. This behavior violates the principle of robust error handling and demonstrates a lack of proper defensive programming practices in the TensorFlow codebase. The vulnerability is categorized under CWE-248, which addresses "Uncaught Exception" conditions, and aligns with ATT&CK technique T1499.004 for "Endpoint Denial of Service" where attackers can cause system resource exhaustion or process termination through malformed inputs. The CHECK failure mechanism in TensorFlow's internal codebase essentially provides an attack surface where controlled input data can force the system to crash or become unresponsive.
The operational impact of this vulnerability extends beyond simple service disruption to potentially compromise entire machine learning workflows and infrastructure deployments. Organizations relying on TensorFlow for production AI services, including those in financial services, healthcare, or autonomous systems, could face significant downtime and operational disruption when exploited. The vulnerability affects multiple versions of TensorFlow including 2.7.2, 2.8.1, 2.9.1, and the affected versions are still within their supported lifecycle, making the risk assessment particularly severe for enterprises maintaining legacy systems. Attackers could leverage this vulnerability to repeatedly crash TensorFlow processes, causing cascading failures in AI pipelines, especially in containerized environments where process restarts could amplify the denial of service impact. The lack of known workarounds means that organizations cannot implement temporary mitigations while awaiting patches, forcing them to either upgrade immediately or face potential service interruptions.
The remediation approach implemented by the TensorFlow team addresses the core issue through proper input validation and error handling mechanisms. The fix introduced in GitHub commit 50156d547b9a1da0144d7babe665cf690305b33c ensures that the Conv2DBackpropInput operation properly validates the dimensional requirements of input_sizes before attempting any computational processing. This fix follows established security practices for preventing denial of service attacks through malformed input and aligns with industry standards for secure coding. The patch has been integrated into TensorFlow 2.10.0 and backported to earlier supported versions, demonstrating the maintainers' commitment to protecting users of older releases. Organizations should prioritize upgrading to the patched versions as soon as possible, particularly those running affected TensorFlow versions in production environments. Security teams should also consider implementing monitoring for unusual process termination patterns or error conditions that might indicate exploitation attempts, while ensuring that the upgrade process includes thorough testing to validate that the patched functionality operates correctly in their specific deployment environments.