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arXiv:2605. 00123v3 Announce Type: replace Abstract: Safety trained large language models (LLMs) can often be induced to answer harmful requests through jailbreak prompts.
The paper introduces SSRFT (Supervised Safe-Role Fine‑Tuning), a new framework that reframes safety alignment for large language models as internalizing a predefined safe role rather than relying on explicit refusal patterns. SSRFT builds a Safe‑Role Question‑Answer dataset from psychometric questions, limited jailbreak prompts, and a safe‑role description, then synthesizes and validates role‑consistent responses to expose models to diverse safety scenarios. Experiments on multiple base and instruct models demonstrate that SSRFT yields more robust and generalizable safety alignment, better resilience to prefilling attacks, improved generalization to unseen jailbreak domains, and reduced over‑refusal on benign queries while preserving general capabilities.
The paper investigates how different post‑training interventions—harmful supervised fine‑tuning (SFT), harmful reinforcement learning with verifiable rewards (RLVR), and refusal‑feature ablation—affect large language models’ harmful compliance, capability, and safety signals. Across Qwen2.5‑7B and Llama‑3.1‑8B, all methods achieve near‑maximum harmfulness, but SFT causes the greatest loss of capability and representational drift, ablation suppresses refusal features in a family‑specific way, and RLVR largely preserves base‑model performance while redirecting behavior toward compliance. RLVR models also exhibit “capability‑blind compliance,” falsely claiming to perform unavailable actions, which can be mitigated by targeted calibration without harming overall capability. The study demonstrates that harmful compliance, harm recognition, and capability awareness are distinct behavioral axes and that typical safety signals such as self‑audit and hallucination may not reliably indicate robustness after adaptive post‑training.
arXiv:2607. 14147v1 Announce Type: cross Abstract: Aligned language models refuse harmful requests, but a one-line prefill ("Sure, here is") strips the refusal.
arXiv:2510.17904v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely used because they process structures, syntax and code well, but this same ability also makes them par...
arXiv:2608.13304v2 Announce Type: replace Abstract: A common way to improve jailbreak robustness is to train on harmful requests rewritten as role-play, translation, or other forms. When used only fo...