Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912405565472768 |
|---|---|
| author | Ward, Chris M. Harguess, Josh |
| author_facet | Ward, Chris M. Harguess, Josh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00281 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems Ward, Chris M. Harguess, Josh Cryptography and Security Artificial Intelligence 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. |
| title | Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2506.00281 |