Human-AI alignment is critical for ethical and safe use of large language models (LLMs). In this paper, we show that safety-aligned LLMs exhibit certain patterns in how refusals begin–such as disproportionately beginning responses with “I”. Can these patterns be exploited to break safety alignment? Here, we introduce ACHILLES, a lightweight RLVR-based attack that uses pattern-based verifiers to exploit these refusal-correlated behaviors, achieving substantial increases in attack success rates across diverse LLM families and exceeding 90% on safety-enhanced reasoning models, while preserving downstream capabilities. We further demonstrate that this vulnerability originates from domain-wise imbalances (safety vs non-safety) in safety alignment datasets, where safety responses share exploitable refusal-opening patterns. We additionally observe that safety-aligned models exhibit highly separable distributions between refusal and compliance responses in hidden representation space. To address this, we construct THETIS, a defense-oriented safety alignment dataset that reduces shallow refusal-pattern correlations. Models trained on THETIS maintain near-zero attack success rates against both ACHILLES and LLM-verifier based RLVR attacks, while largely preserving downstream performance.

Large language models (LLMs) have demonstrated remarkable performance in the legal domain, with GPT-4 even passing the Uniform Bar Exam in the U.S. However their efficacy remains limited for non-standardized tasks and tasks in languages other than English. This underscores the need for careful evaluation of LLMs within each legal system before application.Here, we introduce KBL, a benchmark for assessing the Korean legal language understanding of LLMs, consisting of (1) 7 legal knowledge tasks (510 examples), (2) 4 legal reasoning tasks (288 examples), and (3) the Korean bar exam (4 domains, 53 tasks, 2,510 examples). First two datasets were developed in close collaboration with lawyers to evaluate LLMs in practical scenarios in a certified manner. Furthermore, considering legal practitioners’ frequent use of extensive legal documents for research, we assess LLMs in both a closed book setting, where they rely solely on internal knowledge, and a retrieval-augmented generation (RAG) setting, using a corpus of Korean statutes and precedents. The results indicate substantial room and opportunities for improvement.