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LLM Security and Safety: Insights from Homotopy-Inspired Prompt Obfuscation

Authors: Luis Lazo, Hamed Jelodar, Roozbeh Razavi-Far

Published: 2026-01-20

arXiv ID: 2601.14528v1

Added to Library: 2026-01-22 03:00 UTC

Safety

📄 Abstract

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.

🔍 Key Points

  • Introduction of a homotopy-inspired prompt obfuscation framework, demonstrating how latent model behaviors are influenced through structured prompts.
  • Extensive experimentation with over 15,000 prompts across multiple LLMs (LLama, Deepseek, KIMI, Claude) to evaluate the robustness of LLM safeguards against adversarial techniques.
  • Creation of a comprehensive malware dataset containing 7,374 specimens, valuable for cybersecurity research and improving malware detection models.
  • Quantitative results showcasing model-specific vulnerabilities to prompt manipulation, revealing a significant overall jailbreak success rate of 76% and varying effectiveness across models.
  • Recommendations for stronger mitigation strategies and defensive measures to enhance the security and safety of LLMs against adversarial prompts.

💡 Why This Paper Matters

This paper is vital as it highlights critical vulnerabilities in Large Language Models (LLMs) concerning security and safety mechanisms. By leveraging homotopy theory for prompt obfuscation, the authors not only advance understanding of LLM behavior under adversarial conditions but also provide valuable datasets and methodologies to further research aimed at enhancing the safety and ethical deployment of AI technologies. As biases and vulnerabilities in AI systems become increasingly scrutinized, the findings and resources presented in this paper can significantly contribute to creating more trustworthy AI frameworks.

🎯 Why It's Interesting for AI Security Researchers

The paper's exploration into the security vulnerabilities of LLMs through the lens of prompt obfuscation is highly relevant to AI security researchers, particularly in understanding and mitigating risks associated with adversarial prompts. The proposed framework and generated datasets can aid in testing and validating AI models against malicious exploits, thereby fostering better defensive systems in AI applications. The focus on practical implications for future security strategies underlines the paper's significance in the ongoing discourse on AI safety.

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