cisco-ai-defense/adversarial-hubness-detector
🔧 Toolcisco-ai-defense
A specialized security scanner designed to detect adversarial hubs within RAG systems and vector databases.
The Adversarial Hubness Detector addresses a critical security gap in modern AI architectures: the susceptibility of vector databases to adversarial manipulation. In RAG (Retrieval-Augmented Generation) systems, attackers may inject data designed to become 'hubs'—points that disproportionately influence retrieval results—thereby hijacking the model's context or forcing it to hallucinate specific outputs. This tool provides a systematic approach to scanning and identifying these problematic clusters before they can be exploited. Built in Python, the framework integrates directly into the RAG development lifecycle, allowing security teams to audit their vector embeddings and database configurations. It focuses on detecting anomalies in high-dimensional space that indicate adversarial presence, providing actionable insights to mitigate risks associated with data poisoning and retrieval-based prompt injection. As RAG systems become standard in enterprise environments, this tool serves as a vital component for proactive security posture management.
💡Highlights
- ├─Detects adversarial data hubs
- ├─Secures RAG vector databases
- └─Python-based security auditing
🎯For
- ├─AI Security Engineers
- └─RAG Developers