LLMs Fail Contextual Judgment Against Prompt Injection

An analysis explains that large language models (LLMs) remain highly vulnerable to prompt-injection attacks that can override safety guardrails, as illustrated by examples like a chatbot taking absurd instructions or a Taco Bell system ordering 18,000 cups. It outlines human defensive layers—instincts, social norms, and institutional training—and argues current LLM architectures lack contextual judgment and interruption reflex, calling for new context-aware defenses and training.