arXiv Machine Learning October 19, 2026 By Parv Kapoor, Teresa Fortes, Claudia Mamede, Rui Abreu, Jonathan Aldrich, Eunsuk Kang

Refusal Is Not Robustness: Measuring Harmful Reachability in Autoregressive Language Models

Read the original on arXiv Machine Learning →

The paper introduces Bounded Continuation Analysis (BCA), a method that models autoregressive language model generation as a stochastic process over token prefixes to compute the finite‑horizon probability of reaching a harmful continuation. BCA builds a bounded continuation tree, labels terminal continuations with a semantic harm evaluator, and uses exact probabilistic model checking to estimate harmful reachability. Unlike traditional attack success rate (ASR) metrics that only assess sampled outputs, BCA reveals hidden harmful paths that may coexist with safe responses, enabling more precise red‑team targeting across instruction‑tuned LLMs and JailbreakBench behaviors.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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