1) Answer-first: an answer is first fixed from a trusted source — execution-derived results (crashes, tests), structure-derived code facts (changed functions, call graph), or text from authoritative reports. 2) The language model turns the remaining context into a question and rephrases the answer. 3) An LLM-based judge verifies each sample: whether the answer traces back to and matches the source, is not leaked in the question, and whether the question identifies a unique answer. 4) Ambiguous/invalid samples are regenerated once or discarded. The resulting QA pairs enter the common training pipeline for the OpenAegis model.
Language models answering cybersecurity questions frequently hallucinate or produce unverifiable claims. CyberQA addresses the lack of trustworthy, verifiable QA data by grounding every answer in a trusted, traceable source instead of relying on model generation.
CyberQA defined as one of CyberFactory's three task domains; QA data built answer-first and used to train OpenAegis.
