HARD: A Self-Evolving Defense Framework for LLM Agents
A recent study published on arXiv (2608.12977) introduces HARD (Harness-based Autonomous Runtime Defense Evolution), a framework that autonomously evolves to protect large language model (LLM) agents from advanced security threats. The authors contend that current runtime defenses are overly dependent on manual designs and lack a systematic approach for their creation and upkeep. To remedy this, they propose a harness-level framework that defines how harness mechanisms facilitate defense development, offering a cohesive perspective on existing interventions. Utilizing this framework, HARD can autonomously determine effective intervention strategies and enhance defense elements through data analysis. This innovative approach aims to provide a more resilient and adaptable security method for LLM agents. The full paper can be accessed via the arXiv link.
Key facts
- The paper is titled 'Beyond Handcrafted Security: Towards Self-Evolving Defense for LLM Agents'.
- It is published on arXiv with identifier 2608.12977.
- The announcement type is 'cross'.
- The paper proposes a framework called HARD (Harness-based Autonomous Runtime Defense Evolution).
- HARD is a self-evolving runtime defense framework for LLM agents.
- Existing runtime defenses rely on manually designed interventions.
- The paper introduces a harness-level formulation of runtime defense.
- HARD automatically identifies intervention strategies and iteratively improves defense artifacts.
Entities
Institutions
- arXiv