Backdoor attacks pose a serious threat to large language models (LLMs), but existing defenses remain fragmented, failing to provide unified defense against both dirty-label and clean-label attacks. To investigate why such fragmentation arises, we present the first systematic feature-level mechanistic analysis of LLM backdoors using sparse autoencoders (SAEs). Starting from a 2×2 comparison of clean and poisoned models on clean and triggered inputs, we trace backdoor-induced logit shifts to high-contributing SAE features and categorize them into four roles: interaction, suppressed, mixed, and weight-modified features. This taxonomy reveals systematic encoding differences: dirty-label backdoors are dominated by isolated interaction features, whereas clean-label backdoors rely more on heterogeneous mixtures of mixed and weight-modified features. These differences explain why existing defenses remain fragmented across attack paradigms. We validate this hypothesis through inference-time feature clamping, which reduces ASR to at most **10.8%** in most dirty-label settings and at most **15.4%** in the majority of clean-label settings, while preserving benign-task performance. These results show that SAE-based analysis can explain defense fragmentation and guide interpretable backdoor mitigation.