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Showing posts with label Mythos. Show all posts

New Bad Epoll Bug Impacts Android and Linux, Allows Root Access


A recently found Linux kernel vulnerability called ‘Bad Epoll’ (CVE-2026-46242) allows an ordinary person without any special privilege to take complete command of a device as a root. This has impacted Linux systems, Android, and servers, and a patch is out to address the flaw. 

Bad Epoll was discovered in the same kernel code where Anthropic’s famous AI model, Mythos, discovered another vulnerability. 

The AI flagged one flaw but missed Bad Epoll. Expert Jaeyoung Chung discovered this one.

About Bad Epoll

Epoll is a Linux feature that allows a program to watch various network connections and files at once. You cannot switch it off as web browsers, network services, and servers, all rely on it.

The flaw is a ‘use-after-free’ bug, where two parts of the kernel clean up the same internal object at once. One cleans the memory while the other one writes it. This small friction allows hackers to attack kernel memory, then rise upward to root.

Detecting the bug

Timing is the catch. A random attempt nearly never lands in the window where the two pathways intersect since it is just roughly six machine instructions wide. On testing platforms, Chung's attack expands that window and tries again without crashing, achieving root roughly 99% of the time.

It is more serious since, according to his account, it can be triggered from within Chrome's renderer sandbox, which prevents nearly all other kernel problems, and it can reach Android, which is not possible with other Linux privilege bugs.

Chung sent the flaw as a zer0-day to Google’s kernelCTF program, and full details can be found on his Github. There are no indications that hackers have used it in real-time. At present, an android variant of compromise exists and the only working code is the kernelCTF PoC.

History of the bug

These two flaws go back to a single 2023 modification to the epoll code. According to Chung, Mythos discovered the first two, now labelled as CVE-2026-43074, with an early patch in 2026.

Additionally, Anthropic said that Mythos discovered linux kernel privilege-escalation bugs, but it did not relate the findings with Bad Epoll. Finding the first one was difficult as race-condition flaws are difficult to detect. But why did the AI miss the second flaw? 

Chung offers two likely reasons:

Small timing window

Lack of evidence during runtime 

Anthropic's Mythos: AI-Powered Vulnerability Discovery Forces Cybersecurity Reckoning

 

Anthropic’s Mythos is less a single “hacker AI” than a signal that cybersecurity is entering a new phase. The real reckoning is not that one model can break everything at once, but that software weakness will be found faster, cheaper, and at greater scale than defenders are used to. Anthropic’s own testing says Mythos can identify and chain serious vulnerabilities across major operating systems and browsers, which is why the company withheld public release and limited access to select organizations for defense work.

That shift matters because security teams have long relied on human pace. Vulnerability research, exploit development, patch validation, and incident response usually move slower than attackers would like; Mythos compresses that timeline. Anthropic says the model can uncover subtle, long-standing flaws, including issues that survived years of automated testing and human review. That does not mean every discovered flaw becomes an immediate catastrophe, but it does mean the window between “bug found” and “weaponized” could shrink dramatically.

Threat analysts believe that AI’s biggest cybersecurity impact may come from existing tools, not only from frontier models like Mythos. Even before Mythos, attackers and defenders were already using AI agents to generate code, search for weaknesses, and automate parts of exploitation and remediation. So the danger is not a sudden cliff where the world changes overnight; it is a steady acceleration that makes old security assumptions look outdated. In that sense, Mythos is a spotlight, not the whole show. 

A second layer of concern is organizational. Anthropic is giving Mythos to more than 40 companies and several security-focused groups so they can test their own systems and harden critical software. That defensive access may help, but it also reveals an uncomfortable reality: the same capabilities that strengthen security can also lower the barrier for misuse if they spread beyond controlled settings. This creates pressure on companies to treat AI as part of the threat model rather than as a productivity add-on. 

Threat analysts ultimately argues for a change in mindset. Security can no longer be an afterthought or a compliance layer added at the end of development. If AI can find and chain vulnerabilities at machine speed, then “secure by design” has to become the default, with better code practices, stronger testing, faster patching, and tighter controls around high-risk AI systems. Mythos may not trigger the exact cybersecurity crisis many people imagined, but it does force a more serious one: software defense must evolve as quickly as software attack.