Generative backdoor attacks pose a growing threat to the instruction tuning of large language models (LLMs), where attackers poison a few training samples to induce malicious payloads on triggered inputs while preserving normal behavior on clean ones. However, existing backdoor-poisoned sample filtering methods typically rely on global sample representations and clustering-based separation, and such global signals can become fragile when heterogeneous target-side malicious payloads scatter poisoned samples or low poisoning rates prevent poisoned samples from forming stable clusters. This motivates us to move beyond cross-sample separability and instead exploit localized token-level target-side anomalies induced by malicious payload tokens under teacher forcing. Building on this intuition, we propose TTAF, a Teacher-forcing Token-level Anomaly Filtering framework which introduces a token-level anomaly score under teacher forcing and aggregates localized evidence to filter suspicious poisoned samples before fine-tuning. Experiments across multiple datasets, representative backdoor attacks, and different LLMs show that TTAF consistently achieves strong filtering performance within this payload-injection setting, with efficient runtime and no external dependencies. Code is available at https://github.com/Sam-Han2020/TTAF.