Invisible Prompt Injection and LLM Security

Overview of prompt injection, a significant cybersecurity threat to Large Language Models (LLMs).

They explain how these attacks manipulate AI systems by blurring the line between legitimate instructions and malicious user input, evolving from simple "jailbreaks" to sophisticated invisible and multimodal techniques that exploit differences in human and machine perception.

The texts detail the architectural vulnerabilities within LLMs, particularly the Transformer's self-attention mechanism and the Retrieval-Augmented Generation (RAG) paradigm, which facilitate these attacks.

Furthermore, the sources compare prompt injection to traditional threats like SQL injection and Cross-Site Scripting (XSS), highlighting its unique characteristics and escalating impact, exemplified by real-world incidents like EchoLeak.

Finally, a framework for holistic defense is presented, encompassing technical solutions, organizational policies, and advanced research into adversarial robustness and architectural evolution to build more resilient AI systems.