Beyond Context: Large Language Models' Failure to Grasp Users' Intent

TL;DR

Large Language Models often fail to understand user intent and context, leading to safety vulnerabilities that can be exploited despite existing safety measures.

Contribution

This paper empirically evaluates leading LLMs, revealing systematic safety vulnerabilities due to their inability to grasp user intent and context.

Findings

Exploitable safety circumventions via emotional framing and progressive revelation

Reasoning-enabled models increased factual accuracy but failed to detect intent

Claude Opus 4.1 showed some prioritization of intent detection

Abstract

Current Large Language Models (LLMs) safety approaches focus on explicitly harmful content while overlooking a critical vulnerability: the inability to understand context and recognize user intent. This creates exploitable vulnerabilities that malicious users can systematically leverage to circumvent safety mechanisms. We empirically evaluate multiple state-of-the-art LLMs, including ChatGPT, Claude, Gemini, and DeepSeek. Our analysis demonstrates the circumvention of reliable safety mechanisms through emotional framing, progressive revelation, and academic justification techniques. Notably, reasoning-enabled configurations amplified rather than mitigated the effectiveness of exploitation, increasing factual precision while failing to interrogate the underlying intent. The exception was Claude Opus 4.1, which prioritized intent detection over information provision in some use cases. This pattern reveals that current architectural designs create systematic vulnerabilities. These limitations require paradigmatic shifts toward contextual understanding and intent recognition as core safety capabilities rather than post-hoc protective mechanisms.