Generative models are increasingly used to simulate risk-sensitive systems, from financial markets and energy demand to healthcare, where downstream decisions depend on rare but consequential events. In this setting, matching average behavior is not enough; rare tail events drive stress testing, capital allocation, and policy analysis. Yet tail objectives such as Value-at-Risk and Expected Shortfall are difficult to optimize directly because empirical estimators rely on sorting, thresholding, and subset selection. We introduce TailDiff, a training-free inference-time guidance method for pretrained diffusion models that uses a distributional tail scoring rule to construct differentiable tail-aware objectives. We prove that the population loss uniquely identifies the intended tail law, quantify its finite implementation gap, and bound the terminal sampler perturbation by the accumulated realised guidance updates. On synthetic data, TailDiff approaches the empirical lower bound. On a challenging financial tail-risk simulation benchmark, it outperforms the state-of-the-art on tail metrics while preserving bulk structure.

Deep generative models are increasingly used as simulators for downstream decision-making under data scarcity, but in risk-sensitive applications their usefulness depends on rare adverse scenarios rather than typical samples. Standard generative objectives prioritize bulk distributional fidelity, leaving low-probability tails vulnerable to localized optimization noise and making tail-dependent functionals unstable under finite simulation budgets. We introduce Diachronic Sample Integration (DSI), a test-time inference framework that ensembles generated samples across checkpoints from a stochastic training trajectory. DSI targets a checkpoint-mixture distribution that averages checkpoint-specific tail fluctuations rather than relying on a single brittle endpoint. We formalize this mechanism through a finite-budget bias-variance theory. Empirically, across multivariate synthetic processes and high-frequency trading data, DSI substantially reduces tail-estimation error compared to single-checkpoint baselines under fixed simulation budgets, outperforming standard diffusion and state-of-the-art tail-aware baselines without modifying the generative objective.

In authentication scenarios, applications of practical speaker verification systems usually require a person to read a dynamic authentication text. Previous studies played an audio adversarial example as a digital signal to perform physical attacks, which would be easily rejected by audio replay detection modules. This work shows that by playing our crafted adversarial perturbation as a separate source when the adversary is speaking, the the practical speaker verification system will misjudge the adversary as a target speaker. A two-step algorithm is proposed to optimize the universal adversarial perturbation to be text-independent and has little effect on authentication text recognition. We also estimated room impulse response (RIR) in the algorithm which allowed the perturbation to be effective after being played over the air. In the physical experiment, we achieved targeted attacks with a success rate of 100%, while the word error rate (WER) on speech recognition only increased by 3.55%. And recorded audio could pass replay detection for the live person speaking.