Large language models (LLMs) are revolutionizing how we interact with technology, but their potential misuse poses significant risks. Researchers are constantly developing 'jailbreaks' – adversarial prompts designed to bypass safety measures and elicit harmful responses. But how realistic are these threats? A new study proposes a practical framework for evaluating these attacks, offering insights into just how vulnerable LLMs truly are. The research introduces a 'threat model' that considers not just the success rate of a jailbreak, but also its 'naturalness'—how close the adversarial prompt is to normal human language—and the computational effort required to generate it. By using a massive dataset of text and code, the researchers built a filter to assess how likely an LLM is to fall for a jailbreak. They adapted several existing attack methods to this new threat model, putting them all on equal footing for the first time. The results? Surprisingly, they found that many jailbreaks touted as highly effective are less successful against robustly safety-tuned LLMs than previously thought, especially when constrained by this new realistic framework. Attacks that generate nonsensical or unnatural prompts are easily detected and mitigated. The study also reveals fascinating insights into *how* these attacks work. Effective jailbreaks often exploit rare or unusual combinations of words, either absent from typical text or prevalent in specific datasets like code repositories. By understanding these patterns, developers can strengthen LLM defenses and create more robust safeguards. This research highlights the importance of a more nuanced approach to LLM safety. It's not enough to simply measure how often an LLM generates a harmful response. We need to consider the practicality and detectability of these attacks to truly understand the risks and develop effective countermeasures. The ongoing arms race between LLM developers and those seeking to exploit their weaknesses continues, and frameworks like this one are essential to building safer and more reliable AI systems.