mlsanityData to 14 Oct 2026

Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems

arXiv 2506.00281 ↗First posted 30 May 2025cs.CR cs.AIcited by 4

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

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Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems, which integrate Large Language Models (LLMs) with external knowledge sources, are vulnerable to a range of adversarial attack vectors. This paper examines the importance of RAG systems through recent industry adoption trends and identifies the prominent attack vectors for RAG: prompt injection, data poisoning, and adversarial query manipulation. We analyze these threats under risk management lens, and propose robust prioritized control list that includes risk-mitigating actions like input validation, adversarial training, and real-time monitoring.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 6 of its 28 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

6 of 28 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models3 Jun

Other references · 5

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023A Survey of Large Language Modelscited by 2,576
  4. 2020Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodologycited by 41
  5. 2024The AI Security Pyramid of Paincited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systemscited by 7
  2. 2026ProGRank: Probe-Gradient Reranking to Defend Dense-Retriever RAG from Corpus Poisoningcited by 2
  3. 2026Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directionscited by 2
  4. 2026A Case Study on the Impact of Anonymization Along the RAG Pipelinecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks
  2. 2024On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains
  3. 2026Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks
  4. 2025CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Models
  5. 2026Influence Factors on RAG Poisoning
  6. 2025RAG Security and Privacy: Formalizing the Threat Model and Attack Surface
  7. 2025MARAGE: Transferable Multi-Model Adversarial Attack for Retrieval-Augmented Generation Data Extraction
  8. 2026PIDP-Attack: Combining Prompt Injection with Database Poisoning Attacks on Retrieval-Augmented Generation Systems