#1032Rohan Paul@ROHANPAUL_AIGoogle CEO Sundar Pichai on current frontier model's ability to break the security of almost all current software.
"These models are definitely, like really gonna break pretty much all software out there, maybe already, we don't know."
#1032Rohan Paul@ROHANPAUL_AIAlibaba'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"
Rohan Paul@rohanpaul_aiGoogle CEO Sundar Pichai on current frontier model's ability to break the security of almost all current software. "These models are definitely, like really gonna break pretty much all software out there, maybe already, we don't know."