Argument collapse is a failure mode in which a language model produces many fluent answers but keeps converging on the same few arguments, assumptions, and text structures. The result can look broad while remaining semantically narrow. In cyber security, that matters because AI tools are increasingly used for threat summaries, incident triage, control reviews, and policy drafting, where reasoning diversity is part of the quality signal.
When argument collapse occurs, a defensive assistant may miss alternative attack chains, uncommon indicators, or edge-case risks, especially in open-ended analysis. Attackers can also benefit from this weakness by using LLMs to generate repetitive phishing, malware-adjacent text, or social engineering variants that differ in wording but not in substance. Defenders should test for semantic diversity, compare multiple samples, and check whether the model is exploring genuinely different hypotheses rather than recycling one default line.