Open weights are a defender's advantage.
dfs-large1 from @depthfirstlabs matches frontier-model performance on vulnerability discovery and was built on the open GLM-5.2 model, post-trained with RL on @FireworksAI_HQ.
This is exactly the case for open-source AI policymakers need to notice: American companies can build best-in-class domain models using open weights.
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Today we're announcing dfs-large1, our newest cybersecurity model that achieves best-in-class performance on vulnerability detection tasks.
Besides frontier AI labs, only a handful of companies have built specialized models that reach the state of the art in their domain. We're proud to be the first to do it for cybersecurity.
dfs-large1 is built on GLM-5.2 and post-trained with reinforcement learning inside depthfirst's security infrastructure. We evaluated it on depthfirst-bench, our benchmark of long-horizon vulnerability discovery across complex repositories, where it achieves best-in-class performance.
Training improvements have not plateaued yet and we expect additional performance gains as we continue training.
A huge thank you to @FireworksAI_HQ for being an outstanding training partner. Their infrastructure enabled us run large-scale reinforcement learning efficiently and iterate much faster.
dfs-large1 is now in preview within the @depthfirstlabs platform
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