'Dangerous' AI Models Are Coming No Matter What The US government crackdown on Anthropic's Claude Fable 5 and Mythos 5 hides a glaring truth: AI models with advanced hacking capabilities will soon be the norm. Late last week, Anthropic took its new Claude Fable 5 and Mythos 5 AI models offline following a United States government export-control directive barring "any foreign national" from using the services. The company has been in talks with the White House since Friday but has yet to secure an agreement that would allow it to reinstate the offerings. Since Mythos debuted in April, Anthropic has claimed--and warned--that the model has advanced capabilities for not only finding software vulnerabilities to help defenders patch them, but also figuring out ways to exploit them that could be used by bad actors. Anthropic itself noted this double edged sword in its launch of Mythos 5 and Claude Fable 5. "A great deal of advanced usage of AI models is dual use: the same queries that are beneficial in the hands of cybersecurity professionals and biology researchers could be dangerous if available to malicious actors," the company wrote in a blog post last week.

As attackers ramp up their AI exploit development, the search for software vulnerabilities is changing rapidly. A decade ago, programs to reward researchers for submitting software vulnerability findings were just starting to go mainstream. Vulnerability disclosure and "bug bounty" programs represented a paradigm shift years in the making--moving institutions from hostility and defensiveness about security research findings to acknowledgement that receiving input and releasing fixes was necessary. When Apple finally announced a bug bounty in 2016, the top reward was $200,000. It rose to $1 million in 2019 and $2 million last year .

Predicting the impact of advances in technology may be a fool's errand but it is a necessary one nonetheless to help try to guide research and funding toward efforts that benefit defense more than offense. For this paper, we try to mathematically model the impact of further advancement in several critical aspects of cybersecurity. Perhaps more importantly than any of the forewarnings or funding recommendations we come to, this approach strives to sharpen debates about AI's impact on cybersecurity. This is the companion paper for a separate report, published by CSET and titled, "Will AI Make Cyber Swords or Shields," illustrating the value of rigor in policy discussions about technological advancement. There is too much uncertainty to believe that the math gives precise projections, but it forces us to be precise in our assumptions. Reasonable people may disagree with the range of values we choose as inputs or even the models we use. We welcome those disagreements and hope they advance our collective understanding of how AI may change the future of cybersecurity. Following this introduction, we proceed with separate analysis from three areas of cybersecurity: 1) phishing, 2) vulnerability discovery, then 3) the dynamics between patching and exploitation.