The paper introduces GRID, an end-to-end framework that significantly improves the construction of security knowledge graphs from cyber threat intelligence by replacing unstable LLM-based supervision…

This paper proposes a comprehensive taxonomy (SLOT) to systematically categorize security risks, attacks, and defenses specific to Retrieval-Augmented Generation (RAG), clarifying that these risks are…

The paper evaluates four RAG architectures under knowledge base poisoning, demonstrating that advanced architectures significantly improve robustness against adversarial contradictions, localizing the…

The paper proposes the Sentinel-Strategist architecture, an adaptive defense mechanism that selectively deploys security measures in Retrieval-Augmented Generation (RAG) systems to significantly reduc…

This paper empirically evaluates the use of Retrieval-Augmented Generation (RAG) for malware explanation and finds that RAG frequently degrades explanation quality by adding noise when structured secu…

The paper introduces TechGraphRAG, an advanced, agentic RAG framework that enhances technical literature reasoning by integrating multi-step query refinement, external database searching, and knowledg…

This paper introduces GraphSteal, an attack framework demonstrating that Graph RAG systems can leak substantial portions of a hidden knowledge graph by treating them as structural oracles.

This paper provides the first comprehensive, end-to-end survey dedicated to the security of Retrieval-Augmented Generation (RAG) systems, systematically mapping threats, defenses, and benchmarks acros…

The paper introduces ANCHOR, a schema-agnostic system that constructs knowledge graphs from Cyber Threat Intelligence by dynamically discovering and validating against large ontologies, overcoming lim…

MemGraphRAG introduces a novel memory-based multi-agent system to construct globally consistent and structurally sound knowledge graphs, significantly improving retrieval-augmented generation for comp…

The paper proposes a layered, server-side isolation architecture to secure Retrieval-Augmented Generation (RAG) and agentic AI systems in multitenant enterprise environments, ensuring that retrieval a…

The paper introduces DiscourseFlip, a novel black-box, graph-guided attack that manipulates opinions across an entire multi-topic query network, demonstrating a significant leap in scope and effective…

The paper introduces DiscourseFlip, a novel graph-guided attack that demonstrates how coordinated poisoning across a multi-topic query space can manipulate the overall opinion generated by black-box R…

The paper introduces a Retrieval-Augmented Generation (RAG) system that uses targeted query filtering and LLM semantic reasoning to accurately and cost-effectively analyze complex cybersecurity incide…

The paper introduces STRIATUM-CTF, a modular agentic framework that uses a standardized context protocol to enable LLMs to perform multi-step, stateful reasoning for general-purpose CTF solving, achie…

The paper proposes InSemRAG, an enhanced RAG framework that improves retrieval accuracy and knowledge integrity by incorporating intent-aware retrieval and semantics-preserving chunking, achieving sta…

MoG proposes a novel Mixture of Experts framework for graph-based RAG, which uses hub graphs to guide the sparse activation of domain-specific expert graphs, significantly improving retrieval accuracy…

E-MIA introduces a novel, stealthy black-box membership inference attack that converts verifiable hard evidence within a candidate document into an objective, multi-part exam score to determine if the…

The paper introduces RAVEN, a Retrieval-Augmented Vulnerability Exploration Network, which uses LLM agents and RAG to automatically generate comprehensive, structured vulnerability analysis reports fo…

The paper proposes a neuro-symbolic framework to construct highly consistent knowledge graphs for complex question answering by performing ontology-grounded corrections in a post-extraction stage.