Arbiter: Detecting Interference in LLM Agent System Prompts
Read the original on arXiv AI →Arbiter is a framework that uses formal evaluation rules and multi-model LLM scouring to detect interference patterns in system prompts for large language model (LLM) coding agents. Applied to Claude Code, Codex CLI, and Gemini CLI, it uncovered 152 findings in an undirected phase and 21 hand‑labeled interference patterns in a directed analysis. The study shows that prompt architecture correlates with failure class, multi-model evaluation reveals different vulnerability classes than single-model analysis, and a structural data loss issue in Gemini CLI was identified before Google’s patch addressed only the symptom.
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