Shiyin Lin

Submitted: 19 November 2025 You are already at the latest version Safety alignment and jailbreak defense remain central challenges for large language models (LLMs). Despite capability gains, modern systems are vulnerable to direct and indirect prompt-injection and long-context attacks.

This version is not peer-reviewed. Submitted: 02 October 2025 You are already at the latest version In response to the problems of low coverage, high blindness and missed detection of complex vulnerabilities in traditional fuzz testing for binary programs, this paper proposes an enhanced fuzz testing framework based on reverse analysis and large language model (LLM).

This version is not peer-reviewed. Submitted: 10 September 2025 Posted: 23 September 2025 You are already at the latest version Software fuzzing has become a cornerstone in automated vulnerability discovery, yet existing mutation strategies often lack semantic awareness, leading to redundant test cases and slow exploration of deep program states.