OSCS: Online Selection with Provable FAR Control for LLM Safety

Large language models (LLMs) are vulnerable to malicious inputs, posing serious risks in high-stakes applications. Although existing detection-based defenses achieve strong empirical performance, they generally lack explicit control over the false acceptance rate (FAR), a critical safety requirement in sensitive deployment scenarios. This challenge is further complicated by two practical constraints: the lack of malicious calibration samples and the streaming nature of real-world inputs. To address these challenges, we propose OSCS, a novel framework for online FAR control without requiring malicious calibration data. OSCS leverages detection scores produced by existing defenses and employs recursive density estimation to estimate benign probability from the test stream. Based on these estimates, OSCS performs real-time accept/reject decisions while provably satisfying a user-specified FAR target. Theoretically, we show that OSCS controls the FAR up to a vanishing excess term under mild assumptions. Extensive experiments on backdoor and jailbreak attack tasks further demonstrate the effectiveness of OSCS, showing that it consistently achieves robust FAR control across diverse attack settings while outperforming existing baselines.