We introduce KFinEval, a benchmark for evaluating large language models (LLMs) in the Korean financial domain, with about 1K expert-curated instances across foundational financial knowledge, regulation-grounded procedural reasoning under noisy contexts, and financial toxicity under adversarial prompts. Existing financial benchmarks are largely English-centric and emphasize numerical or surface reasoning, providing limited coverage of regulation-grounded decision-making and domain-specific safety. Evaluating proprietary and open-weight LLMs, we observe substantial disparities and trade-offs among knowledge accuracy, procedural reasoning, and safety-aligned behavior; notably, strong capability does not consistently translate into safer responses under adversarial financial scenarios. KFinEval provides a practical Korean-regulation-grounded benchmark for diagnosing and improving financial LLM reliability and safety. Code and resources: https://github.com/sklim84/KFinEval.