Prompt Injection Detection and Mitigation via AI Multi-Agent NLP Frameworks
This addresses security risks in generative AI for users and developers, but it is incremental as it extends an existing multi-agent architecture.
The paper tackles prompt injection vulnerabilities in generative AI systems by introducing a multi-agent NLP framework for detection and mitigation, achieving a marked reduction in injection success and policy breaches on 500 engineered prompts.
Prompt injection constitutes a significant challenge for generative AI systems by inducing unintended outputs. We introduce a multi-agent NLP framework specifically designed to address prompt injection vulnerabilities through layered detection and enforcement mechanisms. The framework orchestrates specialized agents for generating responses, sanitizing outputs, and enforcing policy compliance. Evaluation on 500 engineered injection prompts demonstrates a marked reduction in injection success and policy breaches. Novel metrics, including Injection Success Rate (ISR), Policy Override Frequency (POF), Prompt Sanitization Rate (PSR), and Compliance Consistency Score (CCS), are proposed to derive a composite Total Injection Vulnerability Score (TIVS). The system utilizes the OVON (Open Voice Network) framework for inter-agent communication via structured JSON messages, extending a previously established multi-agent architecture from hallucination mitigation to address the unique challenges of prompt injection.