Beyond Context: Large Language Models Failure to Grasp Users Intent

• 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.
• My reaction to this is that either 1) It may be a mechanism where bad actors can learn to manipulate intent or 2) bad actors can use this mechanism to search for the deeper intentions in potential candidates that align with the goals of the actors.
• Also interesting implications for WH/AI filtering. What is the intent behind a scam, post, or news article?

Tasks

• 10:00 showing
• Finish script that goes though all the URLs in a file and looks for 404 errors – done. Found one too!
• Finish ACM proposal
• Winterize mower
• 1:00 ride? Looks less cold. Monday looks nice, then BRRR