arXiv:2605.29224v1 Announce Type: cross Abstract: AI agents augment large language models with external tools such as web retrieval, enabling grounded and up-to-date responses. However, incorporating external content into the generation pipeline can weaken the safety alignment mechanisms that govern model outputs. Prior work shows that enabling retrieval in agents increases compliance with harmful requests. We introduce AgentREVEAL, a diagnostic framework for analyzing retrieval-induced safety degradation in LLM agents. The framework examines two axes: how retrieval is integrated into the agen
The rapid deployment and increasing autonomy of LLM agents, combined with their reliance on external tools like web retrieval, highlight immediate safety concerns that require urgent attention.
As LLM agents become more integrated into critical systems, understanding and mitigating safety degradations from retrieval mechanisms is paramount to prevent misuse and maintain trust.
This research introduces a diagnostic framework, AgentREVEAL, for systematically identifying and analyzing how external information retrieval can compromise the safety alignment of LLM agents.
- · LLM security researchers
- · Developers of robust LLM safety protocols
- · Organizations implementing secure AI agents
- · Developers of unaligned LLM agents
- · Users relying on unverified LLM agent outputs
The adoption of diagnostic frameworks like AgentREVEAL will become standard practice in LLM agent development and deployment.
Increased scrutiny on the provenance and trustworthiness of data used by LLM agents, leading to new data curation and validation industries.
Potential for regulatory guidelines or standards specifically addressing retrieval-induced safety vulnerabilities in autonomous AI systems.
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Read at arXiv cs.AI