Researchers Develop Taxonomy for Securing Retrieval-Augmented Generation Systems
A new research paper presents SLOT, a framework for categorizing security risks and defenses in retrieval-augmented generation (RAG) systems that extend large language models with external knowledge. The taxonomy organizes attacks and defenses across four dimensions: attack surface, defense layer, security objective, and target scope. The work identifies structural gaps in current security approaches and proposes directions for stronger protections in RAG systems.
Researchers have published a comprehensive taxonomy for understanding and addressing security vulnerabilities in retrieval-augmented generation systems, which enhance large language models by integrating external knowledge sources. The framework, called SLOT, categorizes security issues along four axes: the attack surface where adversaries operate, the defense layer that protects against attacks, the security objective being compromised (following CIA properties: confidentiality, integrity, availability), and the target scope ranging from single queries to distributed manipulation across query distributions. By mapping attacks, defenses, and evaluation methods onto a six-stage knowledge-access pipeline, the authors expose two significant structural mismatches in how current security measures are deployed. The paper identifies gaps in existing approaches and proposes future research directions including more realistic threat models, comprehensive defense evaluation, stronger confidentiality protections, and security considerations for multimodal and agentic RAG systems.
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- arXiv cs.AICenter
Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions
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