{"id":274923,"date":"2026-09-03T10:39:58","date_gmt":"2026-09-03T08:39:58","guid":{"rendered":"https:\/\/www.hwgsababa.com\/?p=274923"},"modified":"2026-09-03T10:39:58","modified_gmt":"2026-09-03T08:39:58","slug":"the-rise-of-machine-speed-curiosity","status":"publish","type":"post","link":"https:\/\/www.hwgsababa.com\/en\/the-rise-of-machine-speed-curiosity\/","title":{"rendered":"Between Unknown and Exploitable: The Rise of Machine-Speed Curiosity"},"content":{"rendered":"
[vc_row el_class=”margin_top_30″][vc_column][vc_column_text css=”” el_class=”paragrafo”]For years, one of the most important constraints in offensive security was human attention. Finding a meaningful vulnerability required someone to understand an unfamiliar environment, follow dependencies, test assumptions, notice unexpected behavior, and keep asking the next question.<\/p>\n

Automation could make individual tasks faster. Scanners could cover large attack surfaces quickly. Fuzzers could generate enormous numbers of inputs. But the curiosity remained human. That is starting to change.<\/p>\n

From automation to machine-speed curiosity<\/strong><\/h3>\n

Traditional security automation is exceptionally good at executing predefined tasks. Give it a known vulnerability, signature, configuration error or testing logic, and it can search for that condition at enormous scale.<\/p>\n

The emerging generation of AI systems introduces something different: exploration. A model can inspect an unfamiliar system, interpret what it sees, form a hypothesis, test it, learn from the response and decide what to try next.<\/p>\n

The distinction is subtle, but important. Traditional automation is essentially looking for something it already knows how to look for. Agentic AI can increasingly ask: \u201cWhat happens if I try this?\u201d And then keep asking.<\/p>\n

That combination of curiosity at machine speed and persistence at machine scale changes what can be explored, how quickly, and with how much dependence on scarce human expertise.<\/p>\n

When vulnerability discovery becomes reasoning<\/strong><\/h3>\n

Anthropic’s Claude Mythos Preview provides<\/a> one of the clearest indications of where this capability is heading. Project Glasswing was created<\/a> to explore whether advanced AI could help defenders discover critical software vulnerabilities before attackers could exploit them. Mythos demonstrated an ability to reason through complex software and identify vulnerabilities that had remained hidden for years, including flaws in major operating systems, browsers and open-source software.<\/p>\n

The important point is not simply that AI can scan software faster. Traditional security tools already do that very well. It is that vulnerability discovery can increasingly involve autonomous reasoning<\/strong>: understanding unfamiliar software, investigating promising behaviors, developing hypotheses and pursuing them. And that begins to change the economics of vulnerability discovery.<\/p>\n

Historically, organizations could have vastly more code and attack surface than their best researchers had time to investigate. Expert attention was the bottleneck. As exploration becomes scalable, that bottleneck begins to move downstream \u2013 toward validation, prioritization and remediation.<\/p>\n

When the AI doesn’t know the vulnerability in advance<\/strong><\/h3>\n

CVE-2026-0773 provides<\/a> a useful illustration of what this looks like in practice. What makes the case interesting is not simply that AI helped discover another critical vulnerability, but how the system arrived there.<\/p>\n

Rather than starting with a known vulnerability signature and checking whether the target matched it, the AI-driven process explored the application and its APIs. It interpreted an unexpected response, connected that behavior to an unsafe deserialization mechanism, pursued the hypothesis and ultimately validated a path to remote code execution.<\/p>\n

That looks much more like the investigative loop of an experienced penetration tester than a conventional vulnerability scan. The difference is that the loop can increasingly run at machine speed.<\/p>\n

Speed gets the attention. Persistence may matter more.<\/strong><\/h3>\n

For CISOs, however, speed is only half of the story. Applications do not remain static between security assessments. New endpoints appear. APIs change. Authentication flows evolve. Business logic is modified. Dependencies are updated. New integrations create new relationships and potentially new attack paths.<\/p>\n

The attack surface keeps moving. And this is where persistence becomes important. A penetration test performed several months ago may have been completely accurate when it was completed. The harder question is what that result tells you about the application today. If AI can continuously explore an environment that is itself continuously changing, the distance between unknown and exploitable starts to compress.<\/p>\n

This is especially relevant for weaknesses that conventional scanning can struggle to identify: business logic flaws, unexpected API interactions, chained vulnerabilities and behaviors that only become apparent when different parts of an application are understood together.<\/p>\n

Can security assurance keep up?<\/strong><\/h3>\n

This does not mean traditional penetration testing<\/a> becomes obsolete. Human expertise remains critical for understanding business context, designing complex attack scenarios, evaluating real-world impact and making decisions that require judgment. But point-in-time testing increasingly needs to coexist with more continuous forms of offensive validation.<\/p>\n

This is precisely the structural challenge: between individual tests, applications continue changing and their attack surfaces continue evolving, while complex API interactions and business-logic risks are among the areas traditional approaches may miss.<\/p>\n

For CISOs, that changes the questions worth asking:<\/p>\n