Between a Rock and a Hard Place: The Tension Between Ethical Reasoning and Safety Alignment in LLMs

View PDF HTML (experimental) Abstract:Large Language Model safety alignment predominantly operates on a binary assumption that requests are either safe or unsafe. This classification proves insufficient when models encounter ethical dilemmas, where the capacity to reason through moral trade-offs creates a distinct attack surface. We formalize this vulnerability through TRIAL, a multi-turn red-teaming methodology that embeds harmful requests within ethical framings. TRIAL achieves high attack success rates across most tested models by systematically exploiting the model's ethical reasoning capabilities to frame harmful actions as morally necessary compromises. Building on these insights, we introduce ERR (Ethical Reasoning Robustness), a defense framework that distinguishes between instrumental responses that enable harmful outcomes and explanatory responses that analyze ethical frameworks without endorsing harmful acts. ERR employs a Layer-Stratified Harm-Gated LoRA architecture, achieving robust defense against reasoning-based attacks while preserving model utility. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2509.05367 [cs.CR] (or arXiv:2509.05367v4 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2509.05367 arXiv-issued DOI via DataCite Submission history From: Shei Pern Chua [view email] [v1] Thu, 4 Sep 2025 05:53:20 UTC (2,206 KB) [v2] Fri, 12 Sep 2025 11:05:08 UTC (2,206 KB) [v3] Sat, 10 Jan 2026 05:42:21 UTC (433 KB) [v4] Wed, 15 Apr 2026 05:09:08 UTC (508 KB)