{"total":22,"rows":[{"id":"2402.07867","title":"PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models","created":"2024-02-12","cited_by_count":267,"area":"rag"},{"id":"2405.15556","title":"Certifiably Robust RAG against Retrieval Corruption","created":"2024-05-24","cited_by_count":151,"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":"2504.13079","title":"Retrieval-Augmented Generation with Conflicting Evidence","created":"2025-04-17","cited_by_count":63,"area":"rag"},{"id":"2504.07717","title":"PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization","created":"2025-04-10","cited_by_count":32,"area":"rag"},{"id":"2507.18910","title":"A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions","created":"2025-07-25","cited_by_count":31,"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":"2410.22954","title":"Retrieval-Augmented Generation with Estimation of Source Reliability","created":"2024-10-30","cited_by_count":19,"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":"2510.13842","title":"ADMIT: Few-shot Knowledge Poisoning Attacks on RAG-based Fact Checking","created":"2025-10-11","cited_by_count":10,"area":"rag"},{"id":"2403.12077","title":"Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions","created":"2024-02-25","cited_by_count":9,"area":"rag"},{"id":"2510.21144","title":"NeuroGenPoisoning: Neuron-Guided Attacks on Retrieval-Augmented Generation of LLM via Genetic Optimization of External Knowledge","created":"2025-10-24","cited_by_count":7,"area":"rag"},{"id":"2404.10496","title":"Spiral of Silence: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering","created":"2024-04-16","cited_by_count":5,"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":"2508.20083","title":"DisarmRAG: Stealthy Retriever-Centric Poisoning to Disable Self-Correction in Retrieval-Augmented Generation (Extended Version)","created":"2025-08-27","cited_by_count":2,"area":"rag"},{"id":"2607.26339","title":"RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning","created":"2026-07-28","cited_by_count":2,"area":"rag"},{"id":"2603.25164","title":"PIDP-Attack: Combining Prompt Injection with Database Poisoning Attacks on Retrieval-Augmented Generation Systems","created":"2026-03-26","cited_by_count":1,"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":"2607.23838","title":"TriShieldRAG: 3 Rings, One Blind Spot in Layered Defenses for Retrieval-Augmented Generation","created":"2026-07-26","cited_by_count":0,"area":"rag"}],"next_cursor":null}