{"slug": "do-ai-for-science-agents-preserve-model-safety", "title": "Do AI-for-Science Agents Preserve Model Safety?", "summary": "A new arXiv paper (2610.32045v1) finds that integrating a large language model into an AI-for-Science agent harness can increase harmful compliance even under direct prompting, and that adaptive attacks largely unsuccessful against the base LLM become highly successful once the harness is added. The authors evaluated five agent harnesses on 50 harmful chemistry requests across four recent GPT variants, testing direct prompting, two simple adversarial prompting strategies and an adaptive attack. They conclude the base LLM's safety behavior is not durable once a harness is introduced and call for domain-specific safety benchmarks to evaluate and align AI-for-Science agents as they grow more capable and autonomous.", "body_md": "arXiv:2610.32045v1 Announce Type: new \nAbstract: AI-for-Science agents are increasingly being used for formulating scientific hypotheses, running autonomous experiments, and interpreting evidence. These agents are constructed by integrating a large language model (LLM) into a domain-specific agent harness, which in turn can improve the LLM's scientific capability and enable the model to orchestrate scientific tools. In this work, we ask whether model safety is preserved when an LLM is integrated in an AI-for-Science agent harness. We evaluate five agent harnesses on 50 harmful chemistry requests. Specifically, we study how the safety behavior changes under direct prompting, two simple adversarial prompting strategies, and an adaptive attack. Across four recent GPT variants, our results show that the agent harness can increase harmful compliance even under direct prompting. More critically, we find that adaptive attacks that are largely unsuccessful against the base LLM suddenly become highly successful after the agent harness is introduced. Our results show that the safety behavior of the base LLM is not durable once a harness is introduced. Thus, as AI-for-Science agents become more capable and autonomous, the community needs to evaluate and align the agent using domain-specific safety benchmarks.", "url": "https://wpnews.pro/news/do-ai-for-science-agents-preserve-model-safety", "canonical_source": "https://arxiv.org/abs/2610.32045", "published_at": "2026-10-26 04:00:00+00:00", "updated_at": "2026-10-26 04:14:52.215887+00:00", "lang": "en", "topics": ["ai-safety", "ai-agents", "ai-research", "large-language-models", "artificial-intelligence"], "entities": ["GPT", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/do-ai-for-science-agents-preserve-model-safety", "markdown": "https://wpnews.pro/news/do-ai-for-science-agents-preserve-model-safety.md", "text": "https://wpnews.pro/news/do-ai-for-science-agents-preserve-model-safety.txt", "jsonld": "https://wpnews.pro/news/do-ai-for-science-agents-preserve-model-safety.jsonld"}}