This is quite interesting.

Armis recently released a report testing the code-generation capabilities of several mainstream AI models, and the findings were alarming—security vulnerabilities everywhere. Honestly, I’m not surprised, but seeing the actual data still gave me pause.

These days, “vibe coding” is all the rage—where developers let AI write the code while they tweak parameters. Everyone’s obsessed with speed, but who’s paying attention to security? Armis’s tests revealed dependency risks, missing edge cases, permission management flaws… the list goes on.

1. The Industry Is Running Naked

The report directly challenges the “move fast and break things” mentality. OpenClaw was already exposed for security issues, and now Armis has added fuel to the fire. The bottom line? AI-generated code might look functional, but it falls apart under scrutiny.

Even the CEO of Hexaware admits that while AI boosts efficiency for businesses, the cost of security audits has skyrocketed. It’s ironic—what was meant to save time ends up creating more work to fix the mess.

2. Huge Gaps Between Models

The report tested mainstream models like Codex, Claude, and Gemini. While the full data isn’t public yet (I’ll update the link when it drops), some models reportedly produced code with twice the vulnerability rate of others.

Missing edge cases were the most common issue. AI writes code like it’s guessing exam questions—it only learns “common patterns” and ignores edge scenarios. For example, ask it to write a file-reading function, and it might skip permission checks entirely.

Dependency risks are another headache. AI loves using the latest library versions but doesn’t care about compatibility. Your demo might run fine, but it’ll crash in production.

3. A Commercial Deadlock

Tool vendors are quick to deflect: “We’re just assistants—the final responsibility lies with developers.” But in reality, many small companies deploy AI-generated code directly, skipping reviews altogether.

Big players aren’t much better. In the race for market share, they’re bragging about “lines of generated code” and “X-fold efficiency gains.” Anyone raising security concerns is seen as blocking profits.

What’s the Solution?

Honestly, there’s no quick fix. Unless clients start suing over security breaches, the industry will keep running naked.

But at least two things can be done:

  1. Don’t Treat AI Code as a Black Box: Always review generated code manually, especially for permissions and edge cases.
  2. Upgrade the Toolchain: Current security scanners can’t keep up with AI’s code generation speed. They need a redesign.

Here’s the hard truth—AI-generated code today is like a grade-schooler copying a PhD thesis. It looks impressive until you ask questions. Sure, it’s fast, but what about quality? What about security? It’s time to think harder about this.

(I’ll add the Armis report link once it’s available. Stay tuned if you’re interested.)