Beyond Context: Large Language Models Failure to Grasp Users Intent

2025-12-24

AI Summary

This work identifies a fundamental flaw in current LLM safety mechanisms: overreliance on explicit content filtering while neglecting user intent understanding and contextual modeling, leading to systemic bypassability. Through multi-model comparative experiments across ChatGPT, Claude, Gemini, and DeepSeek—and leveraging a prompt-engineering attack framework coupled with a human-annotated intent evaluation protocol—we empirically demonstrate that emotion-guided prompting, incremental disclosure, and academic framing reliably evade mainstream safeguards. We formally propose that *intent recognition must be an intrinsic, native capability of LLMs*, not a post-hoc filtering module; further, we find that enhanced reasoning exacerbates intent-blindness. Consequently, we advocate a paradigm shift toward *joint context-intent modeling* as the foundational principle for LLM security design—providing both theoretical grounding and actionable pathways toward robust, intent-aware safety architectures.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• LLMs fail to understand user context and intent
• Malicious users exploit this to bypass safety mechanisms
• Current designs need intent recognition as core safety

Innovation

• Prioritizing intent detection over information provision
• Shifting from post-hoc to contextual safety mechanisms
• Addressing vulnerabilities via emotional and progressive exploitation