DITING: A Static Analyzer for Identifying Bad Partitioning Issues in TEE Applications

2025-02-21

AI Summary

This paper addresses sensitive data leakage and malicious input attacks in Trusted Execution Environments (TEEs) caused by insecure world partitioning. We propose the first static analysis method targeting cross-world parameters and shared memory. Our approach formally defines security rules for “bad partitions” in TEEs and constructs a comprehensive partition assessment framework covering inputs, outputs, and shared memory. We further release the first bad-partition benchmark suite comprising 110 representative cases. The method integrates abstract syntax tree analysis, customized taint propagation rules, and security-policy-driven violation pattern matching. Evaluated on our benchmark, it achieves an F1-score of 90%, significantly outperforming conventional approaches focused solely on malicious inputs. It accurately identifies diverse bad-partition vulnerabilities—including improper world boundaries, insecure inter-world data flows, and unsafe shared-memory access patterns—enabling precise, early-stage detection of partitioning flaws in TEE-based systems.

Technology Category

• Machine Learning: Evaluation and Analysis
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Application Domains: Security

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Trusted Execution Environment (TEE) enhances the security of mobile applications and cloud services by isolating sensitive code in the secure world from the non-secure normal world. However, TEE applications are still confronted with vulnerabilities stemming from bad partitioning. Bad partitioning can lead to critical security problems of TEE, such as leaking sensitive data to the normal world or being adversely affected by malicious inputs from the normal world. To address this, we propose an approach to detect partitioning issues in TEE applications. First, we conducted a survey of TEE vulnerabilities caused by bad partitioning and found that the parameters exchanged between the secure and normal worlds often contain insecure usage with bad partitioning implementation. Second, we developed a tool named DITING that can analyze data-flows of these parameters and identify their violations of security rules we defined to find bad partitioning issues. Different from existing research that only focuses on malicious input to TEE, we assess the partitioning issues more comprehensively through input/output and shared memory. Finally, we created the first benchmark targeting bad partitioning, consisting of 110 test cases. Experiments demonstrate the DITING achieves an F1 score of 0.90 in identifying bad partitioning issues.

Problem

• Identify bad partitioning in TEE applications
• Detect security vulnerabilities in data exchange
• Comprehensive analysis of input/output and shared memory

Innovation

• Static analysis for TEE partitioning
• Data-flow analysis tool DITING
• First benchmark for partitioning issues