Attack of the killer script kiddies

Last August, some of the best cybersecurity teams in the business gathered in Las Vegas to demonstrate the strength of their AI bug-finding systems at DARPA’s Artificial Intelligence Cyber Challenge (AIxCC). The tools had scanned 54 million lines of actual software code that DARPA had injected with artificial flaws. The teams were capable enough to identify most of the artificial bugs, but their automated tools went beyond that — they found more than a dozen bugs that DARPA hadn’t inserted at all.

Attack of the killer script kiddies

In the aftermath of Mythos, AI-assisted amateur hackers are waiting to strike.

Even before the security earthquake that Anthropic delivered this month with Claude Mythos — the new AI model that seems to find vulnerabilities in every piece of software it’s pointed at — automated systems were growing increasingly capable of finding coding flaws. And fears are growing that not only can AI detect these flaws, but also be used to exploit them, putting hacking skills into the hands of everyone across the planet.

“Mythos or not, this is coming.”

This isn’t an empty threat. For decades, this type of no-skill hacker, known as a script kiddie, has wreaked havoc, running scripts they ripped from the internet or copied from exploit tool kits. They didn’t fully understand or have the technical know-how to write these scripts themselves. And yet they were still able to deface websites and propagate viruses.

What’s happening now represents a major escalation, where people without technical backgrounds are able to use AI to enhance their capabilities in a way that wasn’t possible with simple scripts. It is likely to have far more wide-reaching repercussions.

“There’s a tidal wave coming. You can see it. We can all see it,” said Dan Guido, CEO and cofounder of cybersecurity firm Trail of Bits, which was a runner-up in the challenge. “Are you going to lay down and die, or are you going to do something about it?”

Even beyond Project Glasswing, Anthropic is trying to prevent the misuse of its software by criminals. A week after announcing Mythos, the company released Claude Opus 4.7, which for the first time built in safeguards meant to block malicious cybersecurity requests. (Security professionals who want to use the model defensively can apply to the company’s Cyber Verification Program.)

Anthropic’s announcement of Mythos sent shockwaves throughout the industry, but there were warning signs of AI’s cybersecurity prowess prior to it. In June 2025, the autonomous offensive security platform XBOW beat out human hackers to top the leaderboard of HackerOne, a bug bounty platform, indicating big leaps in the ability of AI models to find bugs.

By the time AIxCC rolled around, “there were already 10 to 20 different bug-finding systems that could find orders of multitude more bugs than we could patch,” Guido said.“This is actually not a new problem.”

“2026 is the year when all security debt comes due… 2026 is the make-it-or-break-it year.”

AI is great at pattern matching, and it’s becoming easier and easier for people to find variants of bugs that are already known and ones that have not yet been discovered. And writing exploits is becoming easier as well.

“You can use AI tools and with very minimal human guidance, and in some cases no human guidance, find a zero day in widely used software,” said Tim Becker, senior security researcher at Theori, which was also a finalist in the competition.

The concern is palpable across the industry, and improvements to models — along with improved understanding of their capabilities — are happening at lightning speed.

Open-weight models, or models whose trained parameters (also known as weights) are publicly available, also pose risk. In fact, sophisticated threat actors would be far more likely to run their own deployments to prevent the exploits from being exposed on Anthropic or OpenAI servers, Becker said, as Anthropic may retain data to monitor abuse. And the industry is bracing for what may come next. Other model creators may not be as cautious as Anthropic, potentially unleashing their powerful new tools straight to the public.

“Mythos or not, this is coming,” Guido says.

Mythos represents a step up at writing exploits, but current models are capable, too. Security researchers are already using more widely available models to report vulnerabilities to vendors before they’re exploited in the wild. That means there’s also the risk of malicious actors using them for ill purposes, such as creating exploits for oppressive regimes or stealing sensitive data on their own.

Industry experts predict that the advancement in AI security capabilities is going to lead to a lot more exploits. Bad actors could direct AI to find bugs in uncommon pieces of software that no one previously would have put in the effort to exploit.

“The bar to diving into a new million-line codebase and finding a bug is so much lower than it used to be.”

“Now, because effort is cheap, you can do things that are lower down the food chain. You can write exploits for software that only one company has. You can write exploits for software that exists in only one configuration that one company has. And you can do it on the fly. So during the middle of an intrusion into some hospital and there’s a wall standing between you and what you want, you can just point an LLM at that wall and say, ‘Figure out a flaw here,’ and it can grind until it’s successful. And it’ll find some vulnerability, it can find some configuration, it’ll run an exploit, for a weakness that no one ever has before, and it’ll do it with almost no effort on the part of the user… the hacker… the script kiddie,” said Guido.

This supercharges script kiddies, he says, because they’ll be able to operate on their feet without the constraints of memorizing the weaknesses in random UNIX utilities but instead defaulting to the pretraining in the tool they are using. They’ll be able to iterate through exploits targeting weaknesses at machine speed, something that no human — let alone script kiddie — can do.

It’s hard to determine exactly how much this is improving attacker capabilities, though there definitely seems to be a correlation. Security researchers can help us try to wrap our heads around the scale of bugs being discovered.

Before Becker started working on automatic bug finding with AI, he worked on vulnerability research, finding zero days and reporting them to maintainers. He said it used to take him weeks or months to find a high-impact vulnerability in a brand-new codebase, and now it only takes hours.

“I just drop the code into our AI bug-finding tool and in a couple hours I get a report with a bunch of candidate vulnerabilities, and most of them end up checking out and being real issues,” he said. “The bar to diving into a new million-line codebase and finding a bug is so much lower than it used to be.”

Every release of an automated tool has led to some level of panic about how it might be exploited, whether that’s text-to-image generators or open-source tools like the exploit development and delivery system Metasploit. The panic even goes back to 1995, when a free software vulnerability scanner named SATAN (an acronym for Security Administrator Tool for Analyzing Networks) was released.

“You can just point an LLM at that wall and say, ‘Figure out a flaw here,’ and it can grind until it’s successful.”

Often automated tools don’t lead to the same level of mayhem that had been expected or predicted, due to prevention measures put in place, low adoption rates by attackers, or other factors.

Joshua Saxe, CTO and cofounder of Security Superintelligence Labs, wrote in a blog post that exploits themselves don’t cause cyberattacks, and that adoption of AI vulnerability research tools has been incremental.

“There seems to be an implicit mental model where some new adversarial tool becomes available... and therefore we will immediately see criminal beha