How We Built OpenMythos: A Cybersecurity LLM Trained from Scratch

OpenMythos, a cybersecurity-focused large language model, was developed in response to the inadequacies of general-purpose LLMs in accurately addressing cybersecurity issues. The model was trained using a meticulously curated dataset that combined formal academic research from the ArXiv cs.CR category with real-world CVE data to provide both theoretical and practical insights into vulnerabilities. The training process involved two stages: Supervised Fine-Tuning (SFT) to establish a foundation for cybersecurity reasoning, followed by Reinforcement Learning with Verifiable Reward (RLVR) to ensure output accuracy by having the model verify its own responses against known vulnerabilities. The training utilized Modal's serverless GPU infrastructure, specifically H100s, to efficiently handle the computational demands without managing long-running instances. The OpenMythos model, along with its datasets and demo, is publicly available on Hugging Face, inviting further evaluation and use in security tooling and vulnerability analysis workflows.

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