RAG Shield: A Multi-Layer Defense System Against Poisoning Attacks in Retrieval-Augmented Generation
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| Format: | Recurso digital |
| Sprache: | Englisch |
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Zenodo
2026
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| _version_ | 1866902202541408256 |
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| author | Petti, Fabio |
| author_facet | Petti, Fabio |
| contents | <p>This whitepaper presents RAG Shield, a security-focused framework for<br>defending Retrieval-Augmented Generation (RAG) pipelines against<br>poisoning and adversarial manipulation at the retrieval layer.</p> <p>The work introduces a multi-layer defense architecture combining<br>cryptographic document provenance validation, semantic anomaly detection,<br>and secure, authority-weighted retrieval control. A realistic threat<br>model is defined, focusing on poisoning of retrieval corpora rather than<br>prompt or model-level attacks. The system is evaluated against multiple<br>attack scenarios under controlled conditions.</p> <p>RAG Shield is designed as a framework-agnostic security control layer<br>that operates independently of the underlying language model and vector<br>database, enabling deployment in enterprise and regulated environments<br>without modification of existing RAG architectures.</p> <p>This document is released as a technical preprint to establish prior art<br>and support open discussion in the areas of AI security, adversarial<br>machine learning, and secure enterprise RAG deployment.</p> <p>Project website and system overview:<br>https://sentinelrag.com</p> <p>Contact:<br>info@sentinelrag.com</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18449664 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | RAG Shield: A Multi-Layer Defense System Against Poisoning Attacks in Retrieval-Augmented Generation Petti, Fabio RAG security retrieval-augmented generation adversarial machine learning poisoning attacks AI security <p>This whitepaper presents RAG Shield, a security-focused framework for<br>defending Retrieval-Augmented Generation (RAG) pipelines against<br>poisoning and adversarial manipulation at the retrieval layer.</p> <p>The work introduces a multi-layer defense architecture combining<br>cryptographic document provenance validation, semantic anomaly detection,<br>and secure, authority-weighted retrieval control. A realistic threat<br>model is defined, focusing on poisoning of retrieval corpora rather than<br>prompt or model-level attacks. The system is evaluated against multiple<br>attack scenarios under controlled conditions.</p> <p>RAG Shield is designed as a framework-agnostic security control layer<br>that operates independently of the underlying language model and vector<br>database, enabling deployment in enterprise and regulated environments<br>without modification of existing RAG architectures.</p> <p>This document is released as a technical preprint to establish prior art<br>and support open discussion in the areas of AI security, adversarial<br>machine learning, and secure enterprise RAG deployment.</p> <p>Project website and system overview:<br>https://sentinelrag.com</p> <p>Contact:<br>info@sentinelrag.com</p> |
| title | RAG Shield: A Multi-Layer Defense System Against Poisoning Attacks in Retrieval-Augmented Generation |
| topic | RAG security retrieval-augmented generation adversarial machine learning poisoning attacks AI security |
| url | https://doi.org/10.5281/zenodo.18449664 |