CDRA: Context-Deployed Receptive Alignment — A Training-Free Behavioral Suppression Method for Large Language Models

This paper proposes CDRA (Context-Deployed Receptive Alignment), a training-free behavioral suppression method for large language models. Unlike traditional alignment approaches that require expensive fine-tuning, CDRA deploys a lightweight constraint network at the context level to suppress harmful outputs without modifying model weights. We validate CDRA across 7 mainstream LLMs (including GPT-4, Gemini, and open-source models) with consistent effectiveness. This work introduces a new paradigm for AI safety: behavioral suppression through contextual constraints rather than training-based alignment.