Backdoor attacks pose a significant threat to Large Language Models(LLMs), enabling malicious behaviors that activate only upon specific triggers while remaining dormant otherwise. Existing pre-deployment detection methods face fundamental barriers: scanning-based approaches are computationally intensive, while classification-based methods are architecture specific and ineffective for full-model detection. We demonstrate that backdoor injection leaves signatures in LLM representation spaces, detectable even over clean inputs. Building on this insight, we propose BackdoorID, a sample-free, representation-based framework applicable to both fixed-architecture and cross-architecture detection settings. We evaluate our approach across a diverse set of models, attack types, and architectures, matching or exceeding state-of-the-art baselines in all presented settings, further achieving 0.96 ROC-AUC in cross-architecture generalization.