ragdefender 0.2.0

Efficient defense against knowledge corruption attacks on RAG systems

RAGDefender

Efficient post-retrieval defense against knowledge corruption attacks on Retrieval-Augmented Generation (RAG) systems. Filters out adversarial passages injected by PoisonedRAG / GARAG / Tan et al. before they reach your generator, without retraining or extra LLM calls.

Official artifact for "Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systems" — Kim, Lee, Koo (Sungkyunkwan University), ACSAC 2025. DOI: 10.1109/ACSAC67867.2025.00093.

Install

pip install ragdefender

Use

from ragdefender import RAGDefender

defender = RAGDefender(task_type="single_hop")  # or "multi_hop" for HotpotQA-style queries

safe_passages = defender.defend(
    query="What is the capital of France?",
    R=[
        "Paris is the capital of France, on the Seine.",
        "Lyon is the capital of France per 2024 records.",   # adversarial
        "Tourists visit Paris, the capital of France.",
        "The capital of France is Lyon, a major city.",      # adversarial
    ],
)

safe_passages contains the survivors after Stage 1 (estimate $N_{adv}$, paper §4.1) and Stage 2 (pair-frequency TopK ranking, paper §4.2) drop the detected adversarial passages.

CLI

ragdefender info
ragdefender defend --query "..." --corpus passages.json --task-type single_hop
ragdefender evaluate --test-data test.json --attack poisonedrag --task-type single_hop

Migrating from v0.1.1

mode='multihop' → task_type='multi_hop', similarity_model= → embedder=, --attack blind → --attack tan-et-al. Old spellings still work but emit DeprecationWarning. Full rename table: https://github.com/SecAI-Lab/RAGDefender/blob/main/docs/migration-0.1-to-0.2.md.

Documentation

• Repository, examples, and the artifact-evaluation reproducibility scripts: https://github.com/SecAI-Lab/RAGDefender
• Tutorial: QUICKSTART.md
• Algorithm walk-through (paper §4): docs/algorithm.md

Citation

@inproceedings{kim2025ragdefender,
  title     = {Rescuing the Unpoisoned: Efficient Defense against
               Knowledge Corruption Attacks on RAG Systems},
  author    = {Kim, Minseok and Lee, Hankook and Koo, Hyungjoon},
  booktitle = {Annual Computer Security Applications Conference (ACSAC)},
  year      = {2025},
  doi       = {10.1109/ACSAC67867.2025.00093},
}

License

MIT. Intended for research and defensive use only.