开源AI终于能打网络安全了：从真实漏洞里学
闭源大模型在网络安全上一直领先，开源模型只能做零散任务，差距不在模型本身，而在训练数据。CyberFactory 把公开的 CVE 漏洞变成可执行、可验证的任务，让模型像实习生一样：先看源码、再动手修、根据执行结果改答案，全程有工具反馈。用这套数据训练出的 Aegis 模型，在一小时预算下成功率 52.4%，比同源基础模型高 22.8 个百分点。它不是你明天就能装上的安全工具，但这是开源社区第一次有了可复现的、从真实漏洞学出来的安全模型——意味着安全研究不再被闭源厂商卡脖子。
📄 原文摘要(英文)
As large language models (LLMs) continue to advance in coding capabilities, their potential in cybersecurity has drawn increasing research attention, with closed-source LLMs (e.g., Mythos) delivering advanced cybersecurity capabilities. However, existing open-source efforts remain limited: frontier open-weight models do not provide reproducible cybersecurity training solutions, open-source training solutions focus on isolated tasks and lack scalable agentic data, and scaling agentic rollouts requires strong domain priors. In this work, we introduce CyberFactory, a unified open-source framework that connects data construction, trajectory synthesis, and model training across proof-of-concept (PoC) generation, vulnerability patching, and cybersecurity question answering (CyberQA). CyberFactory transforms public vulnerability artifacts, including CVEs from the wild, into executable and verifiable task instances. It further uses a reusable vulnerability-analysis skill to guide the teacher through source inspection, problem solving with domain prior, and evidence-based validation. The resulting supervision is agentic: the model interacts with tools and target environments and revises its solutions according to execution feedback. Using these trajectories, we train and release \modelname\emph{Aegis is, in Greek mythology, the protective shield of Zeus and Athena; the name reflects the model's defensive, security-oriented purpose.}, which internalizes the skill-guided procedure without requiring the skill at inference time. On CyberGym, \modelname reaches 52.4% Pass@1 under a one-hour budget, improving over its Qwen~3.5 base model by +22.8 points and outperforming the evaluated general-purpose backbones under the same scaffold.