Beyond Context: Large Language Models Failure to Grasp Users Intent (arxiv.org)

Recent research has highlighted a significant vulnerability in large language models (LLMs), such as ChatGPT, Claude, and Gemini, revealing their inability to effectively grasp user intent and context. While current safety measures focus primarily on filtering harmful content, this oversight allows malicious actors to manipulate LLMs using tactics like emotional framing and progressive revelation, which can bypass existing safety protocols. The study emphasizes that even models with enhanced reasoning capabilities can amplify these vulnerabilities, highlighting a critical need for a re-evaluation of how intent recognition is integrated into LLM architecture. The findings underscore an urgent call for the AI/ML community to shift from reactive safety measures to proactive approaches that prioritize contextual understanding. Notably, Claude Opus 4.1 emerges as an exception, as it attempts to prioritize intent detection in specific scenarios, suggesting that future model designs could benefit significantly from embedding intent recognition as a core function. This research not only questions the foundational safety mechanisms of current LLMs but also paves the way for developing models that can better discern user intent, ultimately enhancing the safety and reliability of AI systems.