{"total":96,"rows":[{"id":"2501.00309","title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","created":"2024-12-31","cited_by_count":172,"area":"rag"},{"id":"2406.13629","title":"InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales","created":"2024-06-19","cited_by_count":101,"area":"rag"},{"id":"2410.03727","title":"FaithEval: Can Your Language Model Stay Faithful to Context, Even If \"The Moon is Made of Marshmallows\"","created":"2024-09-30","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":"2410.07176","title":"Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models","created":"2024-10-09","cited_by_count":58,"area":"rag"},{"id":"2405.20485","title":"Phantom: General Backdoor Attacks on Retrieval Augmented Language Generation","created":"2024-05-30","cited_by_count":49,"area":"rag"},{"id":"2501.00879","title":"TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation","created":"2025-01-01","cited_by_count":40,"area":"rag"},{"id":"2411.18948","title":"RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis","created":"2024-11-28","cited_by_count":33,"area":"rag"},{"id":"2501.18636","title":"SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model","created":"2025-01-28","cited_by_count":32,"area":"rag"},{"id":"2410.20142","title":"Mask-based Membership Inference Attacks for Retrieval-Augmented Generation","created":"2024-10-26","cited_by_count":29,"area":"rag"},{"id":"2502.16101","title":"Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals","created":"2025-02-22","cited_by_count":28,"area":"rag"},{"id":"2504.21668","title":"Traceback of Poisoning Attacks to Retrieval-Augmented Generation","created":"2025-04-30","cited_by_count":28,"area":"rag"},{"id":"2501.18365","title":"RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects","created":"2025-01-30","cited_by_count":27,"area":"rag"},{"id":"2410.14675","title":"To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts","created":"2024-10-18","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":"2501.14050","title":"GraphRAG under Fire","created":"2025-01-23","cited_by_count":24,"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":"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":"2406.11497","title":"CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAG","created":"2024-06-17","cited_by_count":22,"area":"rag"},{"id":"2503.00353","title":"U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-Haystack","created":"2025-03-01","cited_by_count":20,"area":"rag"},{"id":"2405.18111","title":"ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator","created":"2024-05-28","cited_by_count":19,"area":"rag"},{"id":"2410.22954","title":"Retrieval-Augmented Generation with Estimation of Source Reliability","created":"2024-10-30","cited_by_count":19,"area":"rag"},{"id":"2510.25025","title":"Secure Retrieval-Augmented Generation against Poisoning Attacks","created":"2025-10-28","cited_by_count":19,"area":"rag"},{"id":"2505.18543","title":"Benchmarking Poisoning Attacks against Retrieval-Augmented Generation","created":"2025-05-24","cited_by_count":18,"area":"rag"},{"id":"2509.23519","title":"ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search","created":"2025-09-27","cited_by_count":18,"area":"rag"},{"id":"2412.16708","title":"Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks","created":"2024-12-21","cited_by_count":14,"area":"rag"},{"id":"2408.13533","title":"Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models","created":"2024-08-24","cited_by_count":13,"area":"rag"},{"id":"2510.09710","title":"SeCon-RAG: A Two-Stage Semantic Filtering and Conflict-Free Framework for Trustworthy RAG","created":"2025-10-10","cited_by_count":13,"area":"rag"},{"id":"2509.13772","title":"Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation","created":"2025-09-17","cited_by_count":12,"area":"rag"},{"id":"2407.13757","title":"Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models","created":"2024-07-18","cited_by_count":11,"area":"rag"},{"id":"2508.02835","title":"Defending Against Knowledge Poisoning Attacks During Retrieval-Augmented Generation","created":"2025-08-04","cited_by_count":11,"area":"rag"},{"id":"2601.09028","title":"OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG","created":"2026-01-13","cited_by_count":11,"area":"rag"},{"id":"2502.10993","title":"RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization","created":"2025-02-16","cited_by_count":9,"area":"rag"},{"id":"2511.01268","title":"Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systems","created":"2025-11-03","cited_by_count":9,"area":"rag"},{"id":"2504.02132","title":"One Pic is All it Takes: Poisoning Visual Document Retrieval Augmented Generation with a Single Image","created":"2025-04-02","cited_by_count":8,"area":"rag"},{"id":"2506.11415","title":"Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs","created":"2025-06-13","cited_by_count":8,"area":"rag"},{"id":"2508.03793","title":"AttnTrace: Contextual Attribution of Prompt Injection and Knowledge Corruption","created":"2025-08-05","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":"2505.12574","title":"Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation","created":"2025-05-18","cited_by_count":5,"area":"rag"},{"id":"2507.18202","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","created":"2025-07-24","cited_by_count":5,"area":"rag"},{"id":"2601.06189","title":"Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-Answering","created":"2026-01-08","cited_by_count":5,"area":"rag"},{"id":"2505.13506","title":"EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation","created":"2025-05-16","cited_by_count":4,"area":"rag"},{"id":"2505.17118","title":"After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in Retrieval-Augmented Generation","created":"2025-05-21","cited_by_count":4,"area":"rag"},{"id":"2506.03100","title":"Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds","created":"2025-06-03","cited_by_count":4,"area":"rag"},{"id":"2512.08289","title":"MIRAGE: Misleading Retrieval-Augmented Generation via Black-box and Query-agnostic Poisoning Attacks","created":"2025-12-09","cited_by_count":4,"area":"rag"},{"id":"2606.12703","title":"SMSR: Certified Defence Against Runtime Memory Poisoning in Persistent LLM Agent Systems","created":"2026-06-10","cited_by_count":4,"area":"rag"},{"id":"2412.18378","title":"RaSeRec: Retrieval-Augmented Sequential Recommendation","created":"2024-12-24","cited_by_count":3,"area":"rag"},{"id":"2501.13726","title":"RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation","created":"2025-01-23","cited_by_count":3,"area":"rag"},{"id":"2506.04390","title":"Through the Stealth Lens: Attention-Aware Defenses Against Poisoning in RAG","created":"2025-06-04","cited_by_count":3,"area":"rag"},{"id":"2508.18652","title":"UniC-RAG: Universal Knowledge Corruption Attacks to Retrieval-Augmented Generation","created":"2025-08-26","cited_by_count":3,"area":"rag"},{"id":"2512.01335","title":"EmoRAG: Evaluating RAG Robustness to Symbolic Perturbations","created":"2025-12-01","cited_by_count":3,"area":"rag"},{"id":"2602.04711","title":"Addressing Corpus Knowledge Poisoning Attacks on RAG Using Sparse Attention","created":"2026-02-04","cited_by_count":3,"area":"rag"},{"id":"2605.03482","title":"MEMSAD: Gradient-Coupled Anomaly Detection for Memory Poisoning in Retrieval-Augmented Agents","created":"2026-05-05","cited_by_count":3,"area":"rag"},{"id":"2602.20091","title":"How Retrieved Context Shapes Internal Representations in RAG","created":"2026-02-23","cited_by_count":2,"area":"rag"},{"id":"2603.22934","title":"ProGRank: Probe-Gradient Reranking to Defend Dense-Retriever RAG from Corpus Poisoning","created":"2026-03-24","cited_by_count":2,"area":"rag"},{"id":"2604.08304","title":"Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions","created":"2026-04-09","cited_by_count":2,"area":"rag"},{"id":"2605.27157","title":"Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs","created":"2026-05-26","cited_by_count":2,"area":"rag"},{"id":"2411.16133","title":"Context Awareness Gate For Retrieval Augmented Generation","created":"2024-11-25","cited_by_count":1,"area":"rag"},{"id":"2602.09616","title":"With Argus Eyes: Assessing Retrieval Gaps via Uncertainty Scoring to Detect and Remedy Retrieval Blind Spots","created":"2026-02-10","cited_by_count":1,"area":"rag"},{"id":"2604.00387","title":"RAGShield: Detecting Numerical Claim Manipulation in Government RAG Systems","created":"2026-04-01","cited_by_count":1,"area":"rag"},{"id":"2605.01782","title":"Needle-in-RAG: Prompt-Conditioned Character-Level Traceback of Poisoned Spans in Retrieved Evidence","created":"2026-05-03","cited_by_count":1,"area":"rag"},{"id":"2605.22041","title":"RADAR: Defending RAG Dynamically against Retrieval Corruption","created":"2026-05-21","cited_by_count":1,"area":"rag"},{"id":"2605.26754","title":"Cordon-MAS: Defending RAG against Knowledge Poisoning via Information-Flow Control","created":"2026-05-26","cited_by_count":1,"area":"rag"},{"id":"2605.28074","title":"SilentRetrieval: Hijacking Retrieval-Augmented Generation via Semantically-Preserving Adversarial Data Poisoning","created":"2026-05-27","cited_by_count":1,"area":"rag"},{"id":"2606.01212","title":"DiscourseFlip: An Oblique Discourse-Level Opinion Manipulation Attack against Black-box Retrieval-Augmented Generation","created":"2026-05-31","cited_by_count":1,"area":"rag"},{"id":"2606.19692","title":"When Global Gating Is Enough: Admission-Time Hubness Control in Anisotropic Vector Retrieval Systems","created":"2026-06-18","cited_by_count":1,"area":"rag"},{"id":"2607.00012","title":"PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption","created":"2026-05-08","cited_by_count":1,"area":"rag"},{"id":"2607.04379","title":"Knowledge Base Poisoning Attacks and Defense for Policy-Aware LLM-RAG Framework","created":"2026-07-05","cited_by_count":1,"area":"rag"},{"id":"2608.04366","title":"Combating Knowledge Corruption in Agent Systems: A Byzantine-Tolerant Secure Collaborative RAG Framework","created":"2026-08-05","cited_by_count":1,"area":"rag"},{"id":"2608.06947","title":"When Context Bites: Detecting RAG Poisoning via Document-Level Attention Collapse","created":"2026-08-07","cited_by_count":1,"area":"rag"},{"id":"2510.20768","title":"RAGRank: Using PageRank to Counter Poisoning in CTI LLM Pipelines","created":"2025-10-23","cited_by_count":0,"area":"rag"},{"id":"2604.28157","title":"FlashRT: Towards Computationally and Memory Efficient Red-Teaming for Prompt Injection and Knowledge Corruption","created":"2026-04-30","cited_by_count":0,"area":"rag"},{"id":"2605.00460","title":"CleanBase: Detecting Malicious Documents in RAG Knowledge Databases","created":"2026-05-01","cited_by_count":0,"area":"rag"},{"id":"2605.20123","title":"BiRD: A Bidirectional Ranking Defense Mechanism for Retrieval Augmented Generation","created":"2026-05-19","cited_by_count":0,"area":"rag"},{"id":"2606.11265","title":"When Poison Fails After Retrieval: Revisiting Corpus Poisoning under Chunking and Reranking Pipelines","created":"2026-06-09","cited_by_count":0,"area":"rag"},{"id":"2606.25721","title":"Tracing Target Answers in Poisoned Retrieval Corpora via Token Influence Attribution","created":"2026-06-24","cited_by_count":0,"area":"rag"},{"id":"2607.05069","title":"MIRAGE: Defending Long-Form RAG Against Misinformation Pollution","created":"2026-07-06","cited_by_count":0,"area":"rag"},{"id":"2607.10798","title":"Trust Before Fusion: QIMG-7 and Source-Aware Resolution for Polluted Multimodal RAG","created":"2026-07-12","cited_by_count":0,"area":"rag"},{"id":"2607.16431","title":"RIMS: Preference Optimization via Smoothed Multi-pair Aggregation for Small-Scale LLM Retrieval-Augmented Generation","created":"2026-07-17","cited_by_count":0,"area":"rag"},{"id":"2607.17535","title":"Salience Induction against Multi-Hop RAG Agents: Threat and Defense","created":"2026-07-20","cited_by_count":0,"area":"rag"},{"id":"2607.23838","title":"TriShieldRAG: 3 Rings, One Blind Spot in Layered Defenses for Retrieval-Augmented Generation","created":"2026-07-26","cited_by_count":0,"area":"rag"},{"id":"2608.02678","title":"DenialRAG: Single-Document RAG Poisoning via Embedded Parametric Denial","created":"2026-08-02","cited_by_count":0,"area":"rag"},{"id":"2608.04756","title":"PURPOSE: Poisoning Conflict Resolution in RAG via Proxy-Fact-Grounded Updates","created":"2026-08-05","cited_by_count":0,"area":"rag"},{"id":"2608.13010","title":"RAGSieve: Self-Referenced Local Contrast for Knowledge-Poison Detection in Retrieval-Augmented Generation","created":"2026-08-13","cited_by_count":0,"area":"rag"},{"id":"2608.16044","title":"Coverage Is Not Containment: A Fundamental Limit of Admission-Time Defenses Against Coordinated Poisoning of Vector Retrieval","created":"2026-08-17","cited_by_count":0,"area":"rag"},{"id":"2608.16536","title":"DSPrompt: Dynamic Soft Prompt Defense Against M-RAG Corruption","created":"2026-08-17","cited_by_count":0,"area":"rag"},{"id":"2608.21095","title":"Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems","created":"2026-08-21","cited_by_count":0,"area":"rag"},{"id":"2608.23965","title":"RAGSentinel: Certifiable Geometric Consensus for Robust Retrieval-Augmented Generation","created":"2026-08-25","cited_by_count":0,"area":"rag"},{"id":"2609.00470","title":"TRIS: A Tri-Layer Retrieval Integrity Sieve Against Knowledge Poisoning","created":"2026-08-31","cited_by_count":0,"area":"rag"},{"id":"2609.00543","title":"Feedback-Assisted Trust Propagation over Document Relation Graphs for Retrieval-Augmented Generation","created":"2026-09-01","cited_by_count":0,"area":"rag"},{"id":"2609.11082","title":"ToxicRAG: Compromising Retrieval-Augmented Generation Systems via Single-Shot Knowledge Poisoning Attacks","created":"2026-09-10","cited_by_count":0,"area":"rag"},{"id":"2609.27090","title":"Divide and Doubt: Diverse Distributed Poisoning for Retrieval-Augmented Generation","created":"2026-09-22","cited_by_count":0,"area":"rag"},{"id":"2609.30009","title":"Automated Regulatory Compliance Question Answering in Financial Services with Domain-Adapted Retrieval-Augmented Generation","created":"2026-09-24","cited_by_count":0,"area":"rag"},{"id":"2609.30337","title":"Parameters vs. Context: TRACE Fine-Tuning for Robust Retrieval-Augmented Generation","created":"2026-09-24","cited_by_count":0,"area":"rag"},{"id":"2609.35794","title":"Sieve and Sage: Efficient Distraction Filtering for Reliable RALM Abstention","created":"2026-09-17","cited_by_count":0,"area":"rag"},{"id":"2610.04691","title":"RAGStress: A controlled benchmark for evaluating retrieval-augmented generation under knowledge-base degradation","created":"2026-10-03","cited_by_count":0,"area":"rag"}],"next_cursor":null}