arXiv subjects

Chengyan Ma

Publications and source records attributed to Chengyan Ma.

7 recordsLinked to original sources

arXiv2026

Finding Missing Input Validation in TEEs via LLM-Assisted Symbolic Execution

Trusted Execution Environments (TEEs) provide hardware-enforced isolation that protects sensitive code and data from untrusted software. Despite their strong security guarantees, analyzing TEE applications remains challenging due to the high cost and complexity of configuring complete TEE build and runtime environments, as well as the limited observability imposed by hardware isolation. This paper presents SymTEE, a novel large language model (LLM)-assisted symbolic execution framework for detecting missing input validation issues in TEE applications without requiring real TEE setups. SymTEE begins by leveraging Abstract Syntax Tree (AST) analysis to extract TEE code slices that may lack sufficient input validation, and then employs an LLM (GPT-5 in our case) to automatically convert the extracted slices into KLEE-compatible harness programs containing lightweight mock execution environments for symbolic analysis. Evaluations on 26 vulnerabilities (11 real-world and 15 synthetic) show that SymTEE achieves 100% precision and 92.3% recall in detecting missing input validation vulnerabilities while incurring an average analysis cost of only $0.05. These results demonstrate the effectiveness and practicality of SymTEE's pioneering paradigm of LLM-assisted symbolic execution, where LLMs autonomously generate mock environments to enable automated security analysis without complex setup, providing a more accessible and scalable framework for trusted computing systems.

cs.SE↗

arXiv2026

Automated Repair of TEE Partitioning Issues via DSL-Guided and LLM-Assisted Patching

Trusted Execution Environments (TEEs) provide hardware-based isolation to protect sensitive data and computations from potentially compromised operating systems (OS). However, TEE applications inevitably interact with the untrusted OS through SDK interfaces, and improper partitioning can introduce severe vulnerabilities such as data leakage and code injection. While prior work has proposed static analysis tools to detect such issues, automated repair remains largely unexplored. This problem is particularly challenging due to three TEE-specific factors: the lack of standardized secure development guidelines, the difficulty of extracting semantic information from low-level C code, and the absence of mature testing and validation methods. In this work, we present TEERepair, a framework for automatically repairing bad partitioning issues in TEE applications. Our approach tackles the above challenges by introducing a domain-specific language (DSL) to encode repair rules that express and capture common TEE security patterns, which are instantiated as patch templates with placeholders for context-specific variables. We then leverage large language models (LLMs) to reason about code semantics and synthesize context-aware patches, and further generate test clients to validate the repairs. We evaluate TEERepair on the TEE Partitioning Errors Benchmark (PartitioningE-Bench), achieving a significantly higher repair success rate of 87.6% compared to baselines. Furthermore, applying TEERepair to real-world TEE projects, we submitted 5 repair pull requests, 2 of which have been confirmed and merged by project maintainers.

cs.SE↗

arXiv2025

What You Trust Is Insecure: Demystifying How Developers (Mis)Use Trusted Execution Environments in Practice

Trusted Execution Environments (TEEs), such as Intel SGX and ARM TrustZone, provide isolated regions of CPU and memory for secure computation and are increasingly used to protect sensitive data and code across diverse application domains. However, little is known about how developers actually use TEEs in practice. This paper presents the first large-scale empirical study of real-world TEE applications. We collected and analyzed 241 open-source projects from GitHub that utilize the two most widely-adopted TEEs, Intel SGX and ARM TrustZone. By combining manual inspection with customized static analysis scripts, we examined their adoption contexts, usage patterns, and development practices across three phases. First, we categorized the projects into 8 application domains and identified trends in TEE adoption over time. We found that the dominant use case is IoT device security (30%), which contrasts sharply with prior academic focus on blockchain and cryptographic systems (7%), while AI model protection (12%) is rapidly emerging as a growing domain. Second, we analyzed how TEEs are integrated into software and observed that 32.4% of the projects reimplement cryptographic functionalities instead of using official SDK APIs, suggesting that current SDKs may have limited usability and portability to meet developers' practical needs. Third, we examined security practices through manual inspection and found that 25.3% (61 of 241) of the projects exhibit insecure coding behaviors when using TEEs, such as hardcoded secrets and missing input validation, which undermine their intended security guarantees. Our findings have important implications for improving the usability of TEE SDKs and supporting developers in trusted software development.

cs.SE↗

arXiv2025

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

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 that DITING achieves an F1 score of 0.90 in identifying bad partitioning issues.

cs.CR↗

arXiv2025

Towards Secure Program Partitioning for Smart Contracts with LLM's In-Context Learning

Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging because they stem from inherent data confidentiality issues rather than straightforward implementation bugs. To tackle this by preventing sensitive information leakage, we present PartitionGPT, the first LLM-driven approach that combines static analysis with the in-context learning capabilities of large language models (LLMs) to partition smart contracts into privileged and normal codebases, guided by a few annotated sensitive data variables. We evaluated PartitionGPT on 18 annotated smart contracts containing 99 sensitive functions. The results demonstrate that PartitionGPT successfully generates compilable, and verified partitions for 78% of the sensitive functions while reducing approximately 30% code compared to function-level partitioning approach. Furthermore, we evaluated PartitionGPT on nine real-world manipulation attacks that lead to a total loss of 25 million dollars, PartitionGPT effectively prevents eight cases, highlighting its potential for broad applicability and the necessity for secure program partitioning during smart contract development to diminish manipulation vulnerabilities.

cs.SE↗

arXiv2025

Automated TEE Adaptation with LLMs: Identifying, Transforming, and Porting Sensitive Functions in Programs

Trusted Execution Environments (TEEs) isolate a special space within a device memory that is not accessible to the normal world (also known as the untrusted environment), even when the device is compromised. Therefore, developers can utilize TEEs to provide robust security guarantees for their programs, protecting sensitive operations, such as encrypted data storage, fingerprint verification, and remote attestation, from software-based attacks. Despite the robust protections offered by TEEs, adapting existing programs to leverage such security guarantees is challenging, often requiring extensive domain knowledge and manual intervention, which makes TEEs less accessible to developers. This motivates us to design AUTOTEE, the first Large Language Model (LLM) enabled approach that can automatically identify, transform, and port functions containing sensitive operations into TEEs with minimal developer intervention. By manually reviewing 68 repositories, we constructed a benchmark dataset consisting of 385 sensitive functions eligible for transformation, on which AUTOTEE achieves a F1 score of 0.94 on Java and 0.87 on Python. AUTOTEE effectively transforms these sensitive functions into TEE-compatible versions, achieving success rates of 91.8% and 84.3% for Java and Python, respectively, when using GPT-4o.

cs.CR↗

arXiv2023

CToMP: A Cycle-task-oriented Memory Protection Scheme for Unmanned Systems

Memory corruption attacks (MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and performance. MCAs in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms, we found that MCAs are mainly categorized into two types--return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection (CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CToMP resists the ROP attack, a novel variant of return2libc attacks. Finally, we implement CToMP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CToMP is resilient to various MCAs in unmanned systems with low footprints and system overhead.

cs.CR↗