RAG-CT: Mitigating Privacy Risks on Retrieval-Augmented Generation Systems via Scanning Prompt Distribution

arXiv:2609.16095 · cs.CL, cs.AI, cs.CR · Submitted 2026-09-14 · Read on arXiv

cs.CL, cs.AI, cs.CR

Submitted: 2026-09-14

Updated: 2026-09-14

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• RAG — 73× in this paper · explained in 3 episodes
• Retrieval-Augmented Generation — 9× in this paper
• Retrieval-Augmented Generation (RAG) — 5× in this paper · explained in 16 episodes
• Hallucination — 4× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Grounding — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Anomaly Scoring Mechanism — 1× in this paper · explained in 1 episode
• Attack Success Rate — 1× in this paper
• Attack Success Rate (ASR) — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Differential Privacy — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Unique Leakage Chunks — 1× in this paper

Sources

• Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases
• Safeguarding Privacy of Retrieval Data against Membership Inference Attacks: Is This Query Too Close to Home?
• The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)
• Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems
• Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using Jailbreaking
• Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applications
• Certifying LLM Safety against Adversarial Prompting
• A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models

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