Security Knowledge Dilution in Large Language Models: How Irrelevant Context Degrades Critical Domain Expertise

Large Language Models (LLMs) demonstrate remarkable capabilities across diverse domains, yet their performance can be unexpectedly fragile when specialized knowledge is required. We investigate a novel phenomenon we term 'knowledge dilution', the degradation of domain-specific expertise when models are exposed to large volumes of irrelevant but contextually plausible information. Through a controlled experiment involving 400 code generation tasks across varying levels of context dilution, we demonstrate that security-focused knowledge in LLMs systematically degrades as irrelevant technical content increases in the conversation context. Our findings reveal that security feature implementation drops by 47% when moving from focused contexts (0 dilution tokens) to heavily diluted contexts (40,000 dilution tokens), with statistical significance (p < 0.001). This work has critical implications for AI safety, particularly in security-critical applications where domain expertise degradation could lead to vulnerable systems. While demonstrated here in the security domain using GPT-4, this phenomenon likely represents a fundamental challenge for maintaining specialized expertise in production LLM deployments across critical domains.