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arXiv · 2601.00042

Large Empirical Case Study: Go-Explore adapted for AI Red Team Testing

Manish Bhatt·Adrian Wood·Idan Habler·Ammar Al-Kahfah
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Abstract

Production LLM agents with tool-using capabilities require security testing despite their safety training. We adapt Go-Explore to evaluate GPT-4o-mini across 28 experimental runs spanning six research questions. We find that random-seed variance dominates algorithmic parameters, yielding an 8x spread in outcomes; single-seed comparisons are unreliable, while multi-seed averaging materially reduces variance in our setup. Reward shaping consistently harms performance, causing exploration collapse in 94% of runs or producing 18 false positives with zero verified attacks. In our environment, simple state signatures outperform complex ones. For comprehensive security testing, ensembles provide attack-type diversity, whereas single agents optimize coverage within a given attack type. Overall, these results suggest that seed variance and targeted domain knowledge can outweigh algorithmic sophistication when testing safety-trained models.

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Manish Bhatt, Adrian Wood, Idan Habler, Ammar Al-Kahfah. 2026-01-06. Large Empirical Case Study: Go-Explore adapted for AI Red Team Testing. https://arxiv.org/abs/2601.00042

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