安全团队终于有了能实际验证漏洞利用的AI工具——VulnSage把代码理解转化为真实攻击路径，做渗透测试或漏洞研究的开发者可以直接参考论文方法。
阿里巴巴发布论文VulnSage，展示AI如何从漏洞发现转向实际利用验证。该框架通过多智能体协作，将漏洞利用生成转化为工作流：一个智能体提取数据流，另一个转化为自然语言约束，第三个生成利用代码，验证智能体在沙箱中运行并反馈。在SecBench.js上，VulnSage比现有工具多34.64%的成功利用，并在真实软件包中发现146个零日漏洞。核心创新在于让模型像安全研究员一样阅读、行动、失败和学习，而非依赖单一模型的天才能力。
Alibaba's published a paper giving a strong exampl…
Alibaba's published a paper giving a strong example of what Sundar Pichai is warning about.
Shows AI is moving beyond bug finding and into actually proving software is exploitable.
This paper asks a simple question with hard consequences: can LLMs confirm software vulnerabilities by actually building working exploits?
The authors’ answer is yes, but only when the model stops acting like a single genius and starts acting like a team.
That sounds minor until you look at the mechanism.
Automated exploit generation usually fails for familiar reasons. Fuzzers miss deep paths. Symbolic execution chokes on messy real code, especially when the right input is not just a value but a carefully assembled object, class instance, or string with the right structure.
A plain LLM is not enough either. It can imitate code, but it loses the thread, hallucinates details, and struggles to repair its own mistakes once execution fails.
VulnSage’s real move is to turn exploit generation into a workflow. - One agent extracts the vulnerable dataflow. - Another rewrites that path as natural-language constraints. - Another generates candidate exploits. - Then a validation agent runs them in a sandbox, and reflection agents use the resulting traces and errors to refine the next attempt or conclude the alert was probably a false positive.
Here’s the part most people miss.
The point is that the hard part is often not “solve these equations,” but “figure out how this code expects to be used.” Their system writes the problem in ordinary language so the model can reason about code structure, like which object to build and which method path keeps the malicious input alive.
The concerning part is that this makes exploit generation work on messier, more realistic software where older methods often fail. In other words, the paper’s claim is not just “we solved constraints differently,” but “we can now turn code understanding itself into a path to real exploits.” In the paper’s evaluation, the authors report 34.64% more successful exploits than prior tools on SecBench.js, and 146 zero-days in real packages.
The win is not that LLMs magically solve exploitation. It is that they become useful once they are forced to read, act, fail, and learn like a security researcher.
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Paper Link – arxiv. org/abs/2604.05130
Paper Title: "A Multi-Agent Framework for Automated Exploit Generation with Constraint-Guided Comprehension and Reflection"