Building safety-critical systems, like self-driving cars or medical devices, is incredibly complex. How do we *really* know they'll work as intended, every single time? One method is building an "assurance case"—a structured argument supported by evidence that demonstrates the system's safety. Think of it like a rigorous proof, meticulously demonstrating why a plane won't fall out of the sky or a nuclear reactor won't melt down. But assurance cases can be vulnerable—gaps in reasoning or missing "edge cases" can lead to disastrous failures. Researchers are constantly searching for these vulnerabilities, known as *defeaters*. Imagine trying to poke holes in the most complex, airtight argument ever assembled. It's challenging, requires creativity, and is prone to human biases like overlooking inconvenient truths. That’s where a new idea called *CoDefeater* comes in. Researchers are exploring whether Large Language Models (LLMs), the technology behind chatbots like ChatGPT, can help automate the hunt for defeaters. LLMs can process huge amounts of information and generate novel arguments, making them potentially great at spotting hidden flaws. Initial research using ChatGPT on two real-world systems—the Large Hadron Collider and a small drone battery system—has shown promising results. The LLM successfully identified many known defeaters, and even generated new, previously undiscovered ones! For the drone battery, the LLM suggested some very practical defeaters, such as unexpected power drain due to component failure, emergency flight extensions, and the surprisingly frequent bird attacks which force drones to take evasive maneuvers. However, LLMs aren't perfect. They sometimes struggle with implicit assumptions in assurance cases, where defeaters challenge not the stated logic, but the underlying premises on which the logic rests. For example, the LLM might not challenge an assumption that a certain frequency signal is the correct one to monitor, whereas a human analyst might identify that using this frequency might mask dangerous interference on another nearby frequency. The future of CoDefeater lies in refining how we interact with these LLMs through better "prompt engineering" (basically, giving the LLM clearer instructions) and incorporating external knowledge bases to help LLMs reason more effectively. This research is just the beginning, but it opens exciting doors. Imagine a world where AI assists safety engineers in building ironclad arguments for the dependability of critical systems. CoDefeater suggests this could become a reality, paving the way for a future with safer and more reliable technology.