LLM Guardrails Fail to Distinguish Legitimate Use from Attacks

A new research paper titled "Influence Is Not Authority" highlights a critical limitation in current influence-based guardrails for Large Language Models (LLMs). The study reveals that these guardrails struggle to differentiate between legitimate user-authorized actions and malicious, unauthorized ones when both rely on external tool information. This ambiguity can lead to unnecessary interventions, reducing LLM utility and increasing latency. The research demonstrates this issue using Llama and Gemma scorers, showing that even harmless actions can trigger larger score shifts indicative of an attack compared to actual unauthorized actions. AI

IMPACT Highlights a critical flaw in LLM safety mechanisms, potentially impacting the reliability and security of AI agents.

RANK_REASON The item is a research paper published on arXiv discussing limitations in LLM guardrail technology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• Connected Papers
• DagsHub
• Gemma
• Gotit.pub
• Hugging Face
• Influence Flower
• Influence Is Not Authority: When Causal Guardrail Signals Make Legitimate Tool Use Look Like an Attack in Tool-Using LLM Agents
• Litmaps
• Llama
• ScienceCast
• scite Smart Citations

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