  Constructing assurance cases is a widely used, and sometimes required,
process toward demonstrating that safety-critical systems will operate safely
in their planned environment. To mitigate the risk of errors and missing edge
cases, the concept of defeaters - arguments or evidence that challenge claims
in an assurance case - has been introduced. Defeaters can provide timely
detection of weaknesses in the arguments, prompting further investigation and
timely mitigations. However, capturing defeaters relies on expert judgment,
experience, and creativity and must be done iteratively due to evolving
requirements and regulations. This paper proposes CoDefeater, an automated
process to leverage large language models (LLMs) for finding defeaters. Initial
results on two systems show that LLMs can efficiently find known and unforeseen
feasible defeaters to support safety analysts in enhancing the completeness and
confidence of assurance cases.
