Alignment has become a critical step towards enabling large language model (LLM) safety guardrails which ensure models provide helpful and harmless responses, while refusing malicious and harmful requests. However, two separate lines of recent work–unalignment via fine-tuning, i.e., jailbreak-tuning (JT), and weight orthogonalization (WO)–have shown that LLM guardrails may be circumvented, such that LLMs obey harmful requests which they would normally refuse. Despite the safety implications of such unalignment procedures, a comprehensive analysis directly contrasting these methods is currently lacking, as is a study of these methods’ impact on malicious LLM capabilities and reasoning models. Using both JT and WO, we study the impact of unaligning six popular LLMs–three reasoning LLMs of various sizes and their instruction-tuned analogues–across harmful safety tasks. Compared to JT, we show that WO produces models which are more effective at adversarially attacking LLMs–in particular, WO reasoning LLMs excel at such adversarial attacks. Interestingly, while increasing adversarial attack efficacy, we show that WO does not drastically increase hallucination rates. This is in stark contrast to JT, which may more than double the hallucination rate of both reasoning and instruction-tuned models alike. Finally, we show that off-the-shelf supervised fine-tuning effectively limits the adversarial attack abilities enabled by WO, without drastically increasing hallucination rates.