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

2026-01-20

AI Summary

This work addresses the vulnerability of large language models (LLMs) to carefully crafted prompt attacks, which expose critical gaps in existing safety mechanisms. For the first time, homotopy theory is introduced into prompt engineering to develop a systematic prompt obfuscation framework. By designing and injecting specialized prompts, the approach reveals anomalous behaviors and latent vulnerabilities in LLMs during tasks such as code generation. Extensive experiments across prominent models—including LLaMA, Deepseek, KIMI, and Claude—comprising 15,732 prompts (with 10,000 high-priority cases)—demonstrate the efficacy of this method. The study establishes a novel paradigm and provides theoretical grounding for building more reliable, detectable, and resilient AI systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Prompt Engineering / Prompting
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• LLM security
• prompt obfuscation
• safety vulnerabilities
• adversarial prompts
• AI trustworthiness

Innovation

• homotopy-inspired
• prompt obfuscation
• LLM security
• safety vulnerabilities
• adversarial probing