{"total":159,"rows":[{"id":"2405.06211","title":"A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models","created":"2024-05-10","cited_by_count":452,"area":"rag"},{"id":"2501.00309","title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","created":"2024-12-31","cited_by_count":172,"area":"rag"},{"id":"2406.02630","title":"AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways","created":"2024-06-04","cited_by_count":127,"area":null},{"id":"2504.15585","title":"A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment","created":"2025-04-22","cited_by_count":116,"area":"lr"},{"id":"2406.13629","title":"InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales","created":"2024-06-19","cited_by_count":101,"area":"rag"},{"id":"2502.13172","title":"Unveiling Privacy Risks in LLM Agent Memory","created":"2025-02-17","cited_by_count":101,"area":"agents"},{"id":"2503.09648","title":"A Survey on Trustworthy LLM Agents: Threats and Countermeasures","created":"2025-03-12","cited_by_count":96,"area":"agents"},{"id":"2406.00083","title":"BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models","created":"2024-06-03","cited_by_count":86,"area":"rag"},{"id":"2409.10102","title":"Trustworthiness in Retrieval-Augmented Generation Systems: A Survey","created":"2024-09-16","cited_by_count":83,"area":"rag"},{"id":"2508.14111","title":"From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery","created":"2025-08-18","cited_by_count":77,"area":"agents"},{"id":"2402.17840","title":"Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems","created":"2024-02-27","cited_by_count":76,"area":"rag"},{"id":"2411.09523","title":"Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents","created":"2024-11-14","cited_by_count":65,"area":"agents"},{"id":"2406.14773","title":"Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data","created":"2024-06-20","cited_by_count":52,"area":"rag"},{"id":"2502.08586","title":"Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks","created":"2025-02-12","cited_by_count":49,"area":"agents"},{"id":"2405.20446","title":"Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation","created":"2024-05-30","cited_by_count":44,"area":"rag"},{"id":"2412.04697","title":"Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy","created":"2024-12-06","cited_by_count":39,"area":"rag"},{"id":"2411.14110","title":"Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applications","created":"2024-11-21","cited_by_count":37,"area":"rag"},{"id":"2409.08045","title":"Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using Jailbreaking","created":"2024-09-12","cited_by_count":30,"area":"safety"},{"id":"2502.06872","title":"Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey","created":"2025-02-08","cited_by_count":30,"area":"rag"},{"id":"2410.02650","title":"Undesirable Memorization in Large Language Models: A Survey","created":"2024-10-03","cited_by_count":29,"area":null},{"id":"2410.20142","title":"Mask-based Membership Inference Attacks for Retrieval-Augmented Generation","created":"2024-10-26","cited_by_count":29,"area":"rag"},{"id":"2506.00054","title":"Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers","created":"2025-05-28","cited_by_count":27,"area":"rag"},{"id":"2506.13666","title":"We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems","created":"2025-06-16","cited_by_count":27,"area":"agents"},{"id":"2502.00306","title":"Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation","created":"2025-02-01","cited_by_count":26,"area":"rag"},{"id":"2406.19234","title":"Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation","created":"2024-06-27","cited_by_count":25,"area":"rag"},{"id":"2501.02968","title":"FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models","created":"2025-01-06","cited_by_count":25,"area":"rag"},{"id":"2504.18041","title":"RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models","created":"2025-04-25","cited_by_count":24,"area":"rag"},{"id":"2404.16251","title":"Prompt Leakage effect and defense strategies for multi-turn LLM interactions","created":"2024-04-24","cited_by_count":23,"area":null},{"id":"2411.01705","title":"Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors","created":"2024-11-03","cited_by_count":23,"area":"rag"},{"id":"2412.13746","title":"RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment","created":"2024-12-18","cited_by_count":23,"area":"rag"},{"id":"2408.04870","title":"ConfusedPilot: Confused Deputy Risks in RAG-based LLMs","created":"2024-08-09","cited_by_count":22,"area":"rag"},{"id":"2411.14572","title":"Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective","created":"2024-11-21","cited_by_count":20,"area":"rag"},{"id":"2504.14891","title":"Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey","created":"2025-04-21","cited_by_count":20,"area":"rag"},{"id":"2505.15420","title":"Silent Leaks: Implicit Knowledge Extraction Attack on RAG Systems through Benign Queries","created":"2025-05-21","cited_by_count":19,"area":"rag"},{"id":"2412.12775","title":"RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service","created":"2024-12-17","cited_by_count":17,"area":"rag"},{"id":"2410.03537","title":"Ward: Provable RAG Dataset Inference via LLM Watermarks","created":"2024-10-04","cited_by_count":16,"area":"safety"},{"id":"2504.21035","title":"A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage","created":"2025-04-28","cited_by_count":16,"area":null},{"id":"2503.13804","title":"Empowering GraphRAG with Knowledge Filtering and Integration","created":"2025-03-18","cited_by_count":15,"area":null},{"id":"2602.11327","title":"Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP","created":"2026-02-11","cited_by_count":15,"area":"agents"},{"id":"2606.20023","title":"When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents","created":"2026-06-18","cited_by_count":13,"area":"agents"},{"id":"2503.06254","title":"Poisoned-MRAG: Knowledge Poisoning Attacks to Multimodal Retrieval Augmented Generation","created":"2025-03-08","cited_by_count":12,"area":"lr"},{"id":"2410.04749","title":"LLaVA Needs More Knowledge: Retrieval Augmented Natural Language Generation with Knowledge Graph for Explaining Thoracic Pathologies","created":"2024-10-07","cited_by_count":11,"area":"rag"},{"id":"2505.09921","title":"PIG: Privacy Jailbreak Attack on LLMs via Gradient-based Iterative In-Context Optimization","created":"2025-05-15","cited_by_count":11,"area":"safety"},{"id":"2503.15548","title":"Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval","created":"2025-03-17","cited_by_count":10,"area":"rag"},{"id":"2503.21315","title":"Tricking Retrievers with Influential Tokens: An Efficient Black-Box Corpus Poisoning Attack","created":"2025-03-27","cited_by_count":10,"area":"rag"},{"id":"2505.22061","title":"Safeguarding Privacy of Retrieval Data against Membership Inference Attacks: Is This Query Too Close to Home?","created":"2025-05-28","cited_by_count":9,"area":"rag"},{"id":"2407.20181","title":"Blockchain for Large Language Model Security and Safety: A Holistic Survey","created":"2024-07-26","cited_by_count":8,"area":"safety"},{"id":"2408.04643","title":"Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey","created":"2024-08-01","cited_by_count":8,"area":null},{"id":"2504.19101","title":"Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation","created":"2025-04-27","cited_by_count":8,"area":"rag"},{"id":"2508.03098","title":"Privacy-Aware Decoding: Mitigating Privacy Leakage of Large Language Models in Retrieval-Augmented Generation","created":"2025-08-05","cited_by_count":8,"area":"rag"},{"id":"2509.20324","title":"RAG Security and Privacy: Formalizing the Threat Model and Attack Surface","created":"2025-09-24","cited_by_count":8,"area":"rag"},{"id":"2504.05276","title":"Enhancing LLM-Based Short Answer Grading with Retrieval-Augmented Generation","created":"2025-04-07","cited_by_count":7,"area":"rag"},{"id":"2601.03979","title":"SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems","created":"2026-01-07","cited_by_count":7,"area":"rag"},{"id":"2605.18775","title":"Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guarantees","created":"2026-04-21","cited_by_count":7,"area":"lr"},{"id":"2410.03026","title":"Estimating Privacy Leakage of Augmented Contextual Knowledge in Language Models","created":"2024-10-03","cited_by_count":6,"area":null},{"id":"2502.14100","title":"Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach","created":"2025-02-19","cited_by_count":6,"area":"rag"},{"id":"2506.04836","title":"Oversight Structures for Agentic AI in Public-Sector Organizations","created":"2025-06-05","cited_by_count":6,"area":null},{"id":"2506.17336","title":"PPMI: Privacy-Preserving LLM Interaction with Socratic Chain-of-Thought Reasoning and Homomorphically Encrypted Vector Databases","created":"2025-06-19","cited_by_count":6,"area":"lr"},{"id":"2510.06719","title":"Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)","created":"2025-10-08","cited_by_count":6,"area":"rag"},{"id":"2510.08109","title":"VersionRAG: Version-Aware Retrieval-Augmented Generation for Evolving Documents","created":"2025-10-09","cited_by_count":6,"area":"rag"},{"id":"2505.13957","title":"Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation","created":"2025-05-20","cited_by_count":5,"area":"rag"},{"id":"2508.03110","title":"Token-Level Precise Attack on RAG: Searching for the Best Alternatives to Mislead Generation","created":"2025-08-05","cited_by_count":5,"area":"rag"},{"id":"2508.17222","title":"Exposing Privacy Risks in Graph Retrieval-Augmented Generation","created":"2025-08-24","cited_by_count":5,"area":"rag"},{"id":"2509.15159","title":"AIP: Subverting Retrieval-Augmented Generation via Adversarial Instructional Prompt","created":"2025-09-18","cited_by_count":5,"area":"rag"},{"id":"2510.07505","title":"PEAR: Planner-Executor Agent Robustness Benchmark","created":"2025-10-08","cited_by_count":5,"area":"agents"},{"id":"2511.05901","title":"Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations","created":"2025-11-08","cited_by_count":5,"area":"rag"},{"id":"2601.10923","title":"Hidden-in-Plain-Text: A Benchmark for Social-Web Indirect Prompt Injection in RAG","created":"2026-01-16","cited_by_count":5,"area":"rag"},{"id":"2602.06616","title":"Confundo: Learning to Generate Robust Poison for Practical RAG Systems","created":"2026-02-06","cited_by_count":5,"area":"rag"},{"id":"2604.09747","title":"ADAM: A Systematic Data Extraction Attack on Agent Memory via Adaptive Querying","created":"2026-04-10","cited_by_count":5,"area":"agents"},{"id":"2408.05933","title":"Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models","created":"2024-08-12","cited_by_count":4,"area":"rag"},{"id":"2412.10807","title":"Towards Action Hijacking of Large Language Model-based Agent","created":"2024-12-14","cited_by_count":4,"area":"agents"},{"id":"2504.17421","title":"Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks","created":"2025-04-24","cited_by_count":4,"area":null},{"id":"2506.16444","title":"REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing","created":"2025-06-19","cited_by_count":4,"area":null},{"id":"2508.04276","title":"A Few Words Can Distort Graphs: Knowledge Poisoning Attacks on Graph-based Retrieval-Augmented Generation of Large Language Models","created":"2025-08-06","cited_by_count":4,"area":"rag"},{"id":"2603.18382","title":"From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents","created":"2026-03-19","cited_by_count":4,"area":"agents"},{"id":"2605.24312","title":"Five Queries Are Enough: Query-Efficient and Surrogate-Free Membership Inference Attacks on RAG via Entailment","created":"2026-05-23","cited_by_count":4,"area":"rag"},{"id":"2608.23471","title":"InjecMEM: Memory Injection Attack on LLM Agent Memory Systems","created":"2026-08-24","cited_by_count":4,"area":"agents"},{"id":"2505.08728","title":"Securing RAG: A Risk Assessment and Mitigation Framework","created":"2025-05-13","cited_by_count":3,"area":"rag"},{"id":"2508.09105","title":"SMA: Who Said That? Auditing Membership Leakage in Semi-Black-box RAG Controlling","created":"2025-08-12","cited_by_count":3,"area":"rag"},{"id":"2509.05607","title":"CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines","created":"2025-09-06","cited_by_count":3,"area":null},{"id":"2510.05310","title":"RAG Makes Guardrails Unsafe? Investigating Robustness of Guardrails under RAG-style Contexts","created":"2025-10-06","cited_by_count":3,"area":"safety"},{"id":"2511.07637","title":"Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private","created":"2025-11-10","cited_by_count":3,"area":"rag"},{"id":"2511.11347","title":"Privacy Challenges and Solutions in Retrieval-Augmented Generation-Enhanced LLMs for Healthcare Chatbots: A Review of Applications, Risks, and Future Directions","created":"2025-11-14","cited_by_count":3,"area":"rag"},{"id":"2601.14662","title":"Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems","created":"2026-01-21","cited_by_count":3,"area":"lr"},{"id":"2601.15678","title":"Connect the Dots: Knowledge Graph-Guided Crawler Attack on Retrieval-Augmented Generation Systems","created":"2026-01-22","cited_by_count":3,"area":"rag"},{"id":"2601.17644","title":"Do Multimodal RAG Systems Leak Data? A Comprehensive Evaluation of Membership Inference and Image Caption Retrieval Attacks","created":"2026-01-25","cited_by_count":3,"area":"rag"},{"id":"2603.19281","title":"URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models","created":"2026-03-02","cited_by_count":3,"area":"rag"},{"id":"2407.01110","title":"SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest","created":"2024-07-01","cited_by_count":2,"area":null},{"id":"2501.16276","title":"URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT","created":"2025-01-27","cited_by_count":2,"area":null},{"id":"2505.00443","title":"Distributed Retrieval-Augmented Generation","created":"2025-05-01","cited_by_count":2,"area":"rag"},{"id":"2505.01315","title":"Helping Large Language Models Protect Themselves: An Enhanced Filtering and Summarization System","created":"2025-05-02","cited_by_count":2,"area":"safety"},{"id":"2505.02828","title":"Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review","created":"2025-05-05","cited_by_count":2,"area":null},{"id":"2506.07399","title":"MrM: Black-Box Membership Inference Attacks against Multimodal RAG Systems","created":"2025-06-09","cited_by_count":2,"area":"mllm"},{"id":"2507.23229","title":"Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitation","created":"2025-07-31","cited_by_count":2,"area":"rag"},{"id":"2509.13625","title":"Privacy Preserving In-Context-Learning Framework for Large Language Models","created":"2025-09-17","cited_by_count":2,"area":null},{"id":"2510.02964","title":"External Data Extraction Attacks against Retrieval-Augmented Large Language Models","created":"2025-10-03","cited_by_count":2,"area":"rag"},{"id":"2510.19615","title":"FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generation","created":"2025-10-22","cited_by_count":2,"area":"rag"},{"id":"2512.00804","title":"Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval","created":"2025-11-30","cited_by_count":2,"area":"rag"},{"id":"2602.05235","title":"FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters","created":"2026-02-05","cited_by_count":2,"area":"rag"},{"id":"2602.07517","title":"MemPot: Defending Against Memory Extraction Attack with Optimized Honeypots","created":"2026-02-07","cited_by_count":2,"area":null}],"next_cursor":"100"}