arXiv:2605.27489v1 Announce Type: cross Abstract: Multi-agent LLM systems decompose workflows across agents, tools, shared context, memory, and decision gates. This modularity improves interpretability, but creates a propagation risk: a bounded perturbation to one component can be reused by other agents and amplified into system-level harm. We introduce HARP (Harm Amplification through Role Perturbation), a trace-first methodology for studying local-to-global harm amplification in multi-agent LLM systems. HARP compares paired clean and perturbed executions and records specialist outputs, tool
The rapid advancement and deployment of multi-agent LLM systems necessitate methodologies like HARP to proactively identify and mitigate emergent risks before widespread adoption.
Understanding harm amplification in AI agents is critical for building robust, safe, and trustworthy systems, directly impacting their commercial viability and regulatory acceptance.
This research introduces a novel, systematic approach to analyze how minor perturbations can lead to significant systemic harms in complex AI architectures, shifting the focus towards proactive risk assessment.
- · AI safety researchers
- · AI system developers
- · Organizations deploying AI agents
- · Regulatory bodies
- · Malicious actors exploiting AI agent vulnerabilities
- · Developers neglecting safety protocols
Refined safety protocols and architectural standards for multi-agent LLM systems will emerge.
Increased public and institutional trust in AI agents will accelerate their integration into critical workflows.
The development of 'AI safety certificates' or auditing standards could become a new industry sector, similar to cybersecurity.
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