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Representation Transitions Reveal Emerging Safety Risks in Multi-Turn LLM Agents
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Research identifies internal representation transitions in LLM agents to detect multi-turn attacks before composite harms occur.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 3 October 2026
- Collected by OneBench
- 4 Oct 2026, 03:02 UK
Stored source excerpt
arXiv:2610.00400v1 Announce Type: new Abstract: Multi-turn attacks on agentic systems can compose individually permissible actions into harmful outcomes, challenging defenses that assess actions or states…
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The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.
OneBench interpretation
Institutional assessment
So what
Multi-turn agentic workflows introduce compound safety risks that single-step guardrails miss, requiring context-aware representation monitoring.
Do what
Review agent monitoring capabilities with the team responsible for AI safety tooling.