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An empirical evaluation of mainstream Rust SAST tools using security vulnerabilities identified in real-world Rust applications is conducted and a large language model (LLM)-assisted automated testing framework, TRUST, is proposed, which automatically analyzes the official Rust reference to derive and implement semanti...
LLM4SAST is presented, the first universal framework detecting faults in SAST tools using large language models and dynamic feedback, and its key insight is leveraging LLMs to understand and transform code in diverse programming languages.
LLM agents use skills to improve performance on specialized tasks. To complete a user request, an agent may invoke several skills in sequence, allowing information produced under one skill to guide the next. Because skills may come from open-source repositories, this handoff can also carry attacker-controlled claims in...
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