CVE-2022-41902 in TensorFlow
Summary
by MITRE • 12/07/2022
TensorFlow is an open source platform for machine learning. The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered. We have patched the issue in GitHub commit a65411a1d69edfb16b25907ffb8f73556ce36bb7. The fix will be included in TensorFlow 2.11.0. We will also cherrypick this commit on TensorFlow 2.8.4, 2.9.3, and 2.10.1.
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Analysis
by VulDB Data Team • 12/31/2022
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-2022-41902 resides within the MakeGrapplerFunctionItem function, which operates as part of TensorFlow's graph optimization and execution pipeline. This function processes input parameters that define the dimensional characteristics of tensors flowing through the computational graph, making it a critical component in the framework's operational flow. The vulnerability manifests when the function receives input arguments specifying sizes that exceed or match the allocated output buffer dimensions, creating a dangerous condition that can compromise system stability and security.
The technical flaw constitutes a classic out-of-bounds memory read vulnerability that falls under the CWE-125 weakness category, specifically representing an out-of-bounds read condition where the application accesses memory beyond the allocated buffer boundaries. This occurs because the function lacks proper validation of input parameter sizes against output buffer constraints, allowing malicious or malformed input data to trigger memory access violations. The vulnerability can be exploited through careful manipulation of tensor dimensions in machine learning workflows, potentially leading to arbitrary code execution or system crashes. The issue is particularly concerning given TensorFlow's extensive use in production environments where stability and security are paramount. The vulnerability has been addressed through a specific code change in GitHub commit a65411a1d69edfb16b25907ffb8f73556ce36bb7, which implements proper bounds checking mechanisms to prevent the out-of-bounds memory access scenario.
The operational impact of this vulnerability extends beyond simple system crashes to potentially enable more sophisticated attack vectors within machine learning environments. When exploited, the vulnerability can cause denial of service conditions that disrupt machine learning workflows and training processes, potentially resulting in significant business disruption for organizations relying on TensorFlow-based solutions. The vulnerability's exploitation risk is elevated in environments where TensorFlow processes untrusted input data from external sources, as attackers could craft malicious tensor dimensions to trigger the memory access violation. Organizations using TensorFlow in production systems face potential data integrity risks and service availability issues, particularly in critical applications such as autonomous vehicles, financial services, or healthcare systems where machine learning models drive decision-making processes. The vulnerability's presence in multiple TensorFlow versions including 2.8.4, 2.9.3, 2.10.1, and the upcoming 2.11.0 release indicates a widespread impact across the framework's user base, requiring coordinated patch management across various deployment scenarios.
Mitigation strategies for this vulnerability should prioritize immediate patch deployment across all affected TensorFlow versions to ensure system security and stability. Organizations should implement comprehensive vulnerability management processes that include automated patch deployment for critical security fixes, particularly in production environments where TensorFlow components are actively processing data. The fix implemented in the GitHub commit addresses the core issue by introducing proper input validation and bounds checking mechanisms that prevent the out-of-bounds memory read condition from occurring. Security teams should also consider implementing runtime monitoring and anomaly detection systems that can identify unusual tensor dimension patterns that might indicate attempted exploitation of this vulnerability. Additionally, organizations should conduct regular security assessments of their machine learning pipelines to identify potential attack surfaces and ensure that input validation mechanisms are properly implemented throughout their TensorFlow-based applications. The vulnerability's classification under ATT&CK framework category T1587.001 (Develop Capabilities: Malware) and T1499.004 (Operational Resilience: Evasion) highlights the need for comprehensive security controls that address both the immediate technical vulnerability and potential exploitation patterns that attackers might employ in sophisticated attack campaigns.