SymTEE combines AST slicing with LLM-generated KLEE harnesses and mock TEE environments to detect missing input validation, reporting 100% precision and 92.3% recall on 26 vulnerabilities at $0.05 average cost.
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Merlin generates CodeQL queries from natural language questions via RAG-based iteration and a self-test technique using assistive queries, achieving 3.8x higher task accuracy and 31% less completion time in user studies while finding additional software issues.
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Finding Missing Input Validation in TEEs via LLM-Assisted Symbolic Execution
SymTEE combines AST slicing with LLM-generated KLEE harnesses and mock TEE environments to detect missing input validation, reporting 100% precision and 92.3% recall on 26 vulnerabilities at $0.05 average cost.
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Generating Complex Code Analyzers from Natural Language Questions
Merlin generates CodeQL queries from natural language questions via RAG-based iteration and a self-test technique using assistive queries, achieving 3.8x higher task accuracy and 31% less completion time in user studies while finding additional software issues.