Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions

The project is currently a theoretical research paper (arXiv:2604.08304) rather than a software product. While the taxonomy provides academic value by separating RAG-specific risks (like retrieval poisoning) from general LLM risks (like prompt injection), it lacks a technical moat or implementation that would provide defensibility. With 0 stars and 8 forks within 8 days, it reflects early academic dissemination rather than community adoption. Competitively, this work sits alongside the OWASP Top 10 for LLM Applications and Microsoft's 'PyRIT' (Python Risk Identification Tool). The 'high' platform domination risk is due to cloud providers (AWS, Azure, Google) and vector database companies (Pinecone, Weaviate) rapidly integrating these exact security mitigations directly into their managed RAG services. As a framework, its utility is high for architects today but will likely be superseded by standardized industry benchmarks or automated security scanners within 6-12 months.