Researchers have developed a novel three-layer security framework to combat prompt injection attacks in retrieval-augmented generation (RAG) chatbots. This framework addresses vulnerabilities at multiple stages of the inference pipeline, including user input screening, context assembly, and model output auditing. Tested across GPT-4o, Llama 3, and Mistral 7B models, the system significantly reduced the attack success rate from 71.4% to 11.3% while maintaining a low false positive rate and minimal latency. AI
IMPACT This framework could significantly enhance the security of RAG chatbots against sophisticated prompt injection attacks.
RANK_REASON The cluster describes a research paper detailing a new security framework for LLMs.
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