In 2017, a developer made a small optimization to Linux’s encryption module. It was harmless. It improved performance. It shipped quietly.
Nine years later, it became a vulnerability. Not discovered by manual review. Not caught by fuzzing. But found by an AI pentesting tool… in under an hour.
Let’s break down how something this subtle turned into a full system compromise.
📝 Background / Context
When you run a program on Linux, it doesn’t execute directly from disk.
The OS loads it into memory—creating a working copy. This is important because if you can modify that in-memory copy… you can change how the program behaves—without touching the file on disk.
Most security controls focus on files on disk.
Very few monitor what happens inside shared memory.
That’s where this bug lives.
🔥 Attack Flow — Step by Step (Simplified)
- The Attacker gains low-level access to a Linux system (say via a stolen SSH key) > They have minimal privileges. A regular user. Nothing special.
- Attacker now targets a specific category of high-privilege programs (ex: built-in account-switching tool) that always run with full admin privileges regardless of who launches them.
- The attacker tricks the system into processing that file through a crypto function > This crypto function has a bug that’s introduced in 2017: Instead of working on a safe separate copy of data, it ends up writing into shared memory space used by all programs (This allows attacker to overwrite a few bytes inside a target program).
- The crypto function begins the requested operation > fails midway (expected) > But before failing, it writes 4 bytes of attacker-controlled data into the in-memory version of that high-privilege program (this is the bug).
- In other words, the attacker deliberately triggers processing of a high privileged program using the flawed crypto function > which fails processing it > but makes a small but costly mistake of writing 4 attacker-controlled bytes to that privileged program)
- Attacker opens the modified program > It runs with the 4-byte alteration > Hands over full admin access! Works across many Linux distributions since 2017.
💡 Key Insights
- The key point isn’t the bug itself. It’s how it was found. This vulnerability went undetected for 9 years. Not caught by manual review. Not caught by fuzzing. It was found in under an hour using an AI pen-testing tool named Xint Code. The prompt had a context to look for something specific to these codepaths but nevertheless, it was flagged by the AI scanning tool.
- This is important: Finding deep, non-obvious logic flaws used to be rare. Often limited to elite attackers (APTs, nation state actors). But that barrier is collapsing. Anyone with access to a powerful AI can now find them in a matter of few hours across the entire tech stack. It gives us a glimpse into what the future looks like once tools like Mythos become available.
- Copy Fail is not just a local privilege escalation. It is also a container escape. A compromised pod can use this to break out of its isolation boundary and compromise the host node and from there, potentially the entire cluster. This impacts all cloud environments too.
- If a security system scans the hard drive to see if the program file has been modified, it will find nothing wrong. The file on the disk is completely untouched. Only the copy in the computer’s temporary RAM was changed.
📌 Closing Thought
For years, bugs like this lived in a protected class. Not because they were impossible to find—but because they were too expensive to discover.
They required deep knowledge, long manual analysis, weeks or months of focused effort. Which meant only a handful of elite researchers—and occasionally nation-state teams—could realistically find and weaponise them.
That economic barrier was the real defense. AI is breaking it. Tools in the class of “Mythos” aren’t just faster fuzzers. They combine Code understanding + Path exploration + Hypothesis generation + Iterative testing.
They don’t just test inputs—they reason about how systems behave. Which means they can uncover many deep vulnerabilities at broad scale. Exactly the kind of bug we saw here.
The implication is profound: The cost of finding deep, non-obvious vulnerabilities is collapsing. What once took months can now take hours. What once required expertise can now be automated.
What once was rare… becomes scalable. And when discovery becomes cheap, volume increases. Not just more bugs found—but more dangerous classes of bugs becoming accessible.
This changes the equation for defenders. You’re no longer protecting against a few highly skilled adversaries.
You’re protecting against: Anyone with access to the right AI tooling. Which means the old assumptions—“this is too obscure to be found”, “this requires expert-level knowledge”, “this will take time to exploit”—no longer hold. All of this forces us to accept one thing:
Everything will eventually be found.
The only question is: Will you fix it before the AI finds it… or after?
