This research reveals a critical vulnerability where LLMs become confused when input text contains instruction-like elements, even when explicitly told to ignore them.

• LLMs struggle to differentiate between task instructions and instruction-like content within inputs
• This vulnerability exists across different types of LLMs including GPT-4, Claude, and Llama 2
• Simple defense mechanisms like instruction repetition proved largely ineffective
• The finding has significant security implications for AI systems in production

For security professionals, this research highlights how attackers could potentially manipulate LLMs by embedding malicious instructions within seemingly innocent inputs, bypassing explicit security guardrails.

LLMs can be easily Confused by Instructional Distractions