arXiv:2606.00801v1 Announce Type: cross Abstract: Current approaches to LLM adversarial testing suffer from coverage gaps: manual red-teaming does not scale, LLM-as-attacker methods exhibit mode collapse, and gradient-based approaches produce uninterpretable gibberish. We introduce a quality-diversity evolutionary framework that operates at the semantic level, evolving interpretable attack strategies rather than token sequences. Using MAP-Elites, we maintain a diverse archive of attacks across behavioral dimensions (strategy type, encoding method, prompt length). In experiments across GPT-4o-m
As LLMs become more integrated into critical systems, the urgency to robustly test their safety and identify vulnerabilities beyond current inefficient methods is growing.
This development offers a scalable and interpretable method for finding LLM vulnerabilities, which is crucial for the secure and reliable deployment of advanced AI systems.
The ability to discover diverse and interpretable attack strategies at a semantic level, moving beyond token-based or manual red-teaming limitations, fundamentally alters LLM safety testing paradigms.
- · AI Safety Researchers
- · LLM Developers
- · Cybersecurity Firms
- · Regulators
- · Malicious Actors (potentially)
- · Black-box LLM Companies
Improved and more reliable LLM safety testing and vulnerability discovery.
Faster iteration cycles for LLM developers to patch and harden models against adversarial attacks, leading to more resilient AI.
Enhanced public and institutional trust in AI systems due to demonstrably better safety protocols and fewer major security incidents.
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