mlsanityData to 7 Oct 2026

TrustRAG: Blockchain-Enhanced RAG via Committee-Based Credibility Scoring

arXiv 2608.20097 ↗First posted 20 Aug 2026cs.CR cs.DCcited by 0

On the map: Retrieval-augmented generation → Privacy and security of RAG

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) lets Large Language Models (LLMs) pull in up-to-date, domain-specific information instead of relying only on what they were trained on. Yet most RAG systems still draw from centralized databases with limited oversight, making it difficult to verify where a document came from, whether it has been tampered with, or whether it should be trusted at all. This is a serious problem in domains where both the timeliness and accuracy of retrieved content are critical, such as healthcare, finance, logistics, and legal case law, where a wrong or manipulated document can directly lead to bad decisions. We present TrustRAG, a committee-based, blockchain-backed RAG system: before a document is used, it is certified by a committee of domain experts through a zero-knowledge protocol, and the committee's hidden scores are combined via secure multi-party computation into a trust score that any client can verify. These scores, along with the underlying document data, are maintained jointly across chains through hash commitments, so no document or score can be silently altered or dropped, and every ranking can be independently replayed and checked.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 13 of its 22 references that are in the map. First posted on arXiv 20 Aug 2026.

Builds on

13 of 22 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchain10 Nov
  2. 2024Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents9 Jun

Other references · 11

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  3. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  4. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 760
  5. 2023Toxicity in ChatGPT: Analyzing Persona-assigned Language Modelscited by 356
  6. 2024ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidencecited by 109
  7. 2023Dense X Retrieval: What Retrieval Granularity Should We Use?cited by 88
  8. 2024Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Trainingcited by 88
  9. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  10. 2023RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledgecited by 51
  11. 2025Should BBR be the default TCP Congestion Control Protocol?cited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025ClaimTrust: Propagation Trust Scoring for RAG Systems
  2. 2025TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation
  3. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
  4. 2024C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System
  5. 2026RAG-Safety-Bench: Reliable Evaluation of Retrieval-Augmented LLM Safety
  6. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems
  7. 2025Token-Level Precise Attack on RAG: Searching for the Best Alternatives to Mislead Generation
  8. 2025Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval