MiST: Mid-Training LLMs for Cybersecurity
Cybersecurity combines high-stakes analysis with complex technical language, making it an impactful and challenging domain for LLMs. We present MiST (Mid-trained Security Transformer), a suite of 8B and 32B models that achieve strong performance on public cybersecurity benchmarks. We use mid-training as an intermediate adaptation stage between general pre-training and cybersecurity training. Rather than performing continual pre-training over large volumes of raw domain text, we curate a compact, expert-vetted seed corpus, and transform it into high-quality domain-specific synthetic training data. The final MiST checkpoints improve mean cybersecurity accuracy by +13.1 and +8.6 absolute per...
| Authors | Oded Ovadia, Elad Ben Zaken, Elad Guttman et al. |
|---|---|
| Primary category | cs.CR |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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