Arbiter: Detecting Interference in LLM Agent System Prompts
Arbiter treats LLM agent system prompts as testable software artifacts, detecting interference and conflicts that could silently alter coding-agent behaviour in production environments.
Summary written by editorial AI · Source link below
arXiv:2603.08993v2 Announce Type: replace-cross Abstract: System prompts for LLM-based coding agents are software artifacts that govern agent behavior, yet lack the testing infrastructure applied to conventional software. We present Arbiter, a framework combining formal evaluation rules with multi-model LLM scouring to detect interference patterns in system prompts. Applied to three major coding agent system prompts: Claude Code (Anthropic), Codex CLI (OpenAI), and Gemini CLI (Google), we ident
Editorial Analysis
Untested system prompts governing AI coding agents are a configuration-as-code blind spot; systematic interference detection prevents silent behavioural drift in automated development pipelines.
Add prompt-level testing and interference detection to your LLM agent deployment pipeline, treating system prompts with the same rigour as infrastructure-as-code.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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