Position, Role or Label? A Controlled Study of Instruction Conflicts in LLM Agents
Factual evidence
What the source reports
Researchers introduced WhoWins, a benchmark testing how LLM agents handle conflicting instructions from system prompts, tools, and memory.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 27 October 2026
- Collected by OneBench
- 28 Oct 2026, 02:01 UK
Stored source excerpt
arXiv:2610.33201v1 Announce Type: new Abstract: LLM agents take instructions from the operator, repository configuration, user, tool results and saved memory, which often conflict. Instruction-hierarchy benchmarks…
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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
Unpredictable agent behavior under conflicting system and tool instructions creates security vulnerabilities and compliance risks in automated workflows.
Do what
Review agentic workflow controls with the team responsible for AI safety and model governance.