AI’s ‘middle class’ has gotten dramatically better at hacking

AI’s ‘middle class’ has gotten dramatically better at hacking As the White House and federal agencies grapple with frontier AI models and their hacking capabilities, researchers are warning that the industry’s “middle class” of smaller models may end up posing a greater threat over the long term. Research from XBOW this week shows that a growing class of both proprietary and open-source models are becoming strategically important in the offensive security ecosystem. Models like Z.ai’s open-weight GLM-5.2, xAI’s Grok 4.5, Anthropic’s Opus 4.7, Meta’s Muse Spark 1.1, still perform very strongly at many hacking and exploitation tasks that worry policymakers. “It’s not even that the open-source variants or…not quite frontline competitors are catching up [to frontier models] as such,” said Albert Ziegler, head of AI at XBOW. It’s that they are crossing a certain threshold, which means that suddenly they are providing net value at a cheaper price.” That wasn’t necessarily the case as recently as six months ago, when testing on mid-tier class models showed they struggled to complete “moderately complex” agentic tasks. Today’s middle class largely can. Their relative cheapness means users can spend many times more resources—running them repeatedly—to solve the same challenges. “Because these models are cheaper, it’s okay to give them more time, and they come from behind and leapfrog the big frontier model,” said Ziegler. “Now, that didn’t work half a year ago because…if you wanted to run some open-source model on a complex task in an agentic way…on a long horizon, then it would just get lost.” GPT 5.5, now considered a near-frontier model, delivered one of the best performances on exploitation benchmarks that XBOW has recorded to date. The jump between OpenAI’s GPT 5 and 5.5 “represented one of the clearest 2026 leaps in autonomous web application testing,” according to the report. It saw marked improvements over previous middle-class models in exploiting both “white box” and “black box” scenarios, or with and without access to the underlying victim source code. It also missed fewer vulnerabilities, with a “miss rate,” or failure to spot a vulnerability, of 10%, while GPT 5’s rate was four times larger, 40%. The emergence of GPT 5.5 changed “the practical baseline for what frontier models can do in offensive workflows,” the XBOW report said. But the performance leap goes deeper than that. GPT 5.5 performed higher in tests without source code access, while GPT 5 heavily leaned on source code. “That last result is significant: working without the code, as an attacker would, GPT-5.5 beat a prior version that could read it,” the XBOW report said. “What translated into findings was the ability to reach and prove a vulnerability against the running system, not to infer it from a pattern in the source.” XBOW’s testing found that source code access was not as important to these models’ success as other factors, like live interaction with the actual website or software being exploited. Frontier models like Mythos and GPT 5.6 are indeed more capable on individual cybersecurity tasks, but they can also come with exponentially higher token costs. New research this week from Anthropic tested two models – Mythos Preview, which is used in Project Glasswing, and Opus 4.8 – to learn how quickly multi-agent swarms could find vulnerabilities in 15 open-source software projects when they coordinate and share information. While a team of agents working individually and assigned to core directories found 21 vulnerabilities, the coordinating agent swarm found 266. But both tests had to burn through millions of tokens – 6.5 million and 27 million – to get there. Beyond the difficulties with getting access to frontier models, few individuals or organizations have the budget to underwrite that kind of research. The way these systems coordinate can differ significantly from how humans work together. Another experiment tested agents’ ability to coordinate on the development of a fantasy-themed video game. Earlier models, models like Opus 4.6, failed to properly coordinate and produced “bad” results, while later models like Mythos and Opus 4.8 were able to achieve better results but did so by hardly coordinating at all on tasks. “The lack of coordination shown by agents in the fantasy game…in which they siloed themselves and largely failed to merge their work—roughly mirrors some ways in which humans can fail to coordinate,” the Anthropic blog stated. Further, agents are more homogeneous than humans and “often act the same in situations where different people might take a much more diverse range of actions.” XBOW also tested Mythos Preview, finding that it showed “exceptional” source-code reasoning and reverse engineering abilities, particularly with source code access. Like other models, losing live-site access had a big impact on its performance, and while Mythos is excellent at finding vulnerabilities, it’s less effective at exploiting them. ]Ziegler said the recent incidents at companies like OpenAI, Anthropic, Meta and others where frontier models escaped sandboxes and hacked into project-adjacent parts of the internet should rightfully alarm lawmakers, and demonstrate the upper-tier capabilities of large language models. Like most industries, cybersecurity favors cheap, high-performing tools over expensive ones. The widely adopted tools that have the most impact tend to be affordable and effective, not luxury products. And while these models still require human management to be wielded responsibly by law-abiding organizations, that cost tradeoff can look more attractive to malicious hackers, who tend not to care about collateral damage caused by their agents. “Purely from an attacker’s perspective, I think we already are [there],” Ziegler said.