LLM Security and Safety: Insights from Homotopy-Inspired Prompt Obfuscation
This work addresses security and safety issues for LLM users and developers, providing a principled framework for analysis and mitigation, though it appears incremental in building on existing prompt engineering methods.
The study tackled security and safety vulnerabilities in Large Language Models (LLMs) by proposing a homotopy-inspired prompt obfuscation framework, revealing critical insights into current safeguards through experiments with 15,732 prompts across models like LLama and Claude.
In this study, we propose a homotopy-inspired prompt obfuscation framework to enhance understanding of security and safety vulnerabilities in Large Language Models (LLMs). By systematically applying carefully engineered prompts, we demonstrate how latent model behaviors can be influenced in unexpected ways. Our experiments encompassed 15,732 prompts, including 10,000 high-priority cases, across LLama, Deepseek, KIMI for code generation, and Claude to verify. The results reveal critical insights into current LLM safeguards, highlighting the need for more robust defense mechanisms, reliable detection strategies, and improved resilience. Importantly, this work provides a principled framework for analyzing and mitigating potential weaknesses, with the goal of advancing safe, responsible, and trustworthy AI technologies.