Automated TEE Adaptation with LLMs: Identifying, Transforming, and Porting Sensitive Functions in Programs
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911485127557120 |
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| author | Han, Ruidong Yang, Zhou Ma, Chengyan Liu, Ye Niu, Yuqing Ma, Siqi Gao, Debin Lo, David |
| author_facet | Han, Ruidong Yang, Zhou Ma, Chengyan Liu, Ye Niu, Yuqing Ma, Siqi Gao, Debin Lo, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13379 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated TEE Adaptation with LLMs: Identifying, Transforming, and Porting Sensitive Functions in Programs Han, Ruidong Yang, Zhou Ma, Chengyan Liu, Ye Niu, Yuqing Ma, Siqi Gao, Debin Lo, David Cryptography and Security Software Engineering 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. |
| title | Automated TEE Adaptation with LLMs: Identifying, Transforming, and Porting Sensitive Functions in Programs |
| topic | Cryptography and Security Software Engineering |
| url | https://arxiv.org/abs/2502.13379 |