This article discusses a new paper outlining design patterns for mitigating prompt injection attacks in LLM agents. It details six patterns – Action-Selector, Plan-Then-Execute, LLM Map-Reduce, Dual LLM, Code-Then-Execute, and Context-Minimization – and emphasizes the need for trade-offs between agent utility and security by limiting the ability of agents to perform arbitrary tasks.
Researchers at HiddenLayer have developed a novel prompt injection technique that bypasses instruction hierarchy and safety guardrails across all major AI models, posing significant risks to AI safety and requiring additional security measures.
This paper introduces a multi-agent NLP framework to address prompt injection vulnerabilities in generative AI systems. The framework utilizes specialized agents for generating responses, sanitizing outputs, and enforcing policy compliance, evaluated using novel metrics like Injection Success Rate (ISR), Policy Override Frequency (POF), Prompt Sanitization Rate (PSR), and Compliance Consistency Score (CCS). The system employs OVON for inter-agent communication.
An analysis of Large Language Models' (LLMs) vulnerability to prompt injection attacks and potential risks when used in adversarial situations, like on the Internet. The author notes that, similar to the old phone system, LLMs are vulnerable to prompt injection attacks and other security risks due to the intertwining of data and control paths.
This post highlights how the GitHub Copilot Chat VS Code Extension was vulnerable to data exfiltration via prompt injection when analyzing untrusted source code.
Simon Willison explains an accidental prompt injection attack on RAG applications, caused by concatenating user questions with documentation fragments in a Retrieval Augmented Generation (RAG) system.
