Indirect prompt injection attacks target common LLM data sources

This report examines indirect prompt injection attacks on LLMs, where malicious instructions are hidden within external content and executed by the model, enabling stealthy manipulation that can lead to data leaks, misinformation, and malicious code propagation in software supply chains. Research (BIPIA) indicates broad vulnerability across models, and experts recommend defenses including content sanitization, explicitly distinguishing context from instructions, tagging untrusted sources, restricting LLM capabilities, and monitoring/red-teaming to detect and mitigate such attacks.