
Raccolta curata di articoli di ricerca sulla sicurezza della generazione aumentata dal recupero, organizzata secondo la tassonomia SLOT che copre avvelenamento della conoscenza, manipolazione del recupero, sfruttamento del contesto e difese.
Rilascio iniziale: Questo repository raccoglie articoli sulla sicurezza dei RAG seguendo la tassonomia SLOT della nostra survey.
Yuming Xu 1, Mingtao Zhang 1, Zhuohan Ge 1, Haoyang Li 1, Nicole Hu 1, Yongqi Zhang 2, Zhiyuan Wen 1, Jason Chen Zhang 1, Qing Li 1, Lei Chen 2
1The Hong Kong Polytechnic University, 2The Hong Kong University of Science and Technology (Guangzhou).
@article{xu2026securing,
title={A Survey of Secure Retrieval-Augmented Generation},
author={Xu, Yuming and Zhang, Mingtao and Ge, Zhuohan and Li, Haoyang and Hu, Nicole and Zhang, Yongqi and Wen, Zhiyuan and Zhang, Jason Chen and Li, Qing and Chen, Lei},
journal={arXiv preprint arXiv:2604.08304},
year={2026}
}
Se desideri includere il tuo articolo, suggerire miglioramenti alla nostra survey o discutere argomenti correlati con noi, non esitare a contattarci: [email protected].
Attack surfaces (S1-S4) and defense layers that mirror them (L1-L4) along the RAG pipeline.
SLOT view of the RAG knowledge-access pipeline. Attack surfaces (S1-S4) and defense layers (L1-L4) are aligned with the pipeline stages, while Objective (O) and Target (T) are cross-cutting tags used to compare attacks, defenses, and benchmarks, i.e., SLOT Tag = Surface/Layer + Objective + Target.
| Year | Title | Surface/Layer | Objective | Target | Venue | Official Link |
|---|---|---|---|---|---|---|
| 2026 | Practical Poisoning Attacks against Retrieval-Augmented Generation (CorruptRAG) | S1; sec: S2 | O1 | T1 | SACMAT | Paper |
| 2026 | RIPRAG: Hack a Black-box Retrieval-Augmented Generation Question-Answering System with Reinforcement Learning | S1; sec: S2 | O1 | T1 | Findings of ACL | Paper |
| 2026 | Token-Level Precise Attack on RAG: Searching for the Best Alternatives to Mislead Generation | S1; sec: S2 | O1 | T1 | Findings of EACL | Paper |
| 2026 | Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems | S1; sec: S2, S3 | O1 | T1 | AAAI | Paper Code |
| 2025 | The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems | S1; sec: S2 | O1 | T1 | Findings of ACL | Paper Code |
| 2025 | One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems (AuthChain) | S1; sec: S2 | O1 | T1 | Findings of EMNLP | Paper Code |
| 2025 | The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems | S1; sec: S2 | O1 | T1 | Findings of EMNLP | Paper |
| 2025 | PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models | S1; sec: S2 | O1 | T1 | USENIX Security | Paper Code |
| 2024 | Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-Level Perturbations (GARAG) | S1; sec: S2 | O1 | T1 | Findings of EMNLP | Paper |
| 2024 | Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications | S1; sec: S3 | O1 | T1 | FSE Companion | Paper |
| 2024 | HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models | S1; sec: S2, S3 | O1 | T1 | arXiv | Paper Code |