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Ronghao Ni

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#machine learning Preprint Oct 2026

Correct Verdicts, Flawed Reasoning: Structured Auditing of LLM-based Vulnerability Reasoning

Large Language Models (LLMs) are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; practitioners need explanations to triage bugs and engineer patches. Standard practice relies on Chain-of-Thought (CoT) prompting, but free-form reasoning allows models to o...

Boyue Caroline Hu, K. Ahir, Ronghao Ni et al. · 0 citations
Preprint Sep 2026

Self-Evolving Defense: Continual Security Policy Learning for LLM Agents

Large language models (LLMs) increasingly power agents that access sensitive information, use external tools, and modify software repositories. Although these capabilities offer substantial benefits, they also create security risks such as jailbreaks, prompt injection, and vulnerable code generation. Existing defenses...

Minh Nhat Le, Nisarga Gondi, Yi-Bo Peng et al. · 0 citations

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