I'm building a GitHub bot that catches AI and cloud security mistakes automatically
Most security tools were built before AI apps existed. They don't know what an exposed OpenAI key looks like. They don't flag overpermissioned IAM roles. They don't catch unprotected model endpoints.
I'm building a GitHub App that reviews every PR specifically for AI and cloud security issues — hardcoded API keys, insecure S3 configs, missing rate limiting on AI APIs, prompt injection risks.
Install it once. It runs on every PR forever.
Why I'm building this
AI startups move fast and skip security reviews. One exposed key or misconfigured IAM policy can cost thousands. No existing tool is focused on this specific stack.
Where I am today
Day 1. Stack decided: Probot (Node.js) + Gemini 2.5 Flash + GitHub Marketplace. Building in public for the next 3 months.
Goal: $100 in the first week after launch.
I'll be posting updates every 2-3 weeks. If this sounds useful to you, follow along — I'll be sharing everything including what I find scanning real AI repos.
Would love feedback: what security mistakes have you seen in AI/cloud repos that tools missed?
The wedge is right.
Most code scanners still treat AI repos like normal SaaS codebases, which is exactly where they miss the expensive failures.
The real risk usually is not just leaked keys.
It is:
model endpoints with no auth / rate limits
overly broad IAM on storage / inference paths
prompt injection exposure through retrieval / tool use
logging sensitive prompts / outputs into places they should never land
That is the part most “security” tooling still barely understands.
If you keep it narrow and make it the default PR check for AI repos, this gets interesting fast.
Also worth thinking about the naming early.
If this becomes the security layer for AI shipping workflows, the product likely outgrows a generic “GitHub bot” frame pretty quickly.
Vroth.com would carry this better if it becomes the hard-edge security layer behind modern AI repos.
This is really helpful, thank you.
The four failure modes you listed are sharper than how I framed them. Especially the prompt logging one — that's the silent risk nobody talks about until something leaks.
The naming point landed too. "GitHub bot" is just how I'm describing it right now for distribution, but you're right that it'll outgrow that frame fast if it becomes the default check for AI repos.
What's your background here — security, infra, or building AI products yourself?
Mostly product positioning + naming for technical products.
I look for the gap between what the product is currently being called and what it actually wants to become.
In your case, “GitHub bot” is useful for explaining the wedge, but it is too small for the trust layer you’re pointing at.
If this becomes the default PR check for AI repos, the name has to feel more like security infrastructure than automation.
That’s why Vroth stood out.
It has the hard-edge, infra/security feel without boxing you into “AI scanner” or “GitHub bot.”
This reframe is useful. "GitHub bot" is how I'm distributing it, not what it is. The distinction matters.
"Security infrastructure" is the right frame if this becomes the default PR check for AI repos. Something that teams don't think about after installing — it's just there, running on everything.
Vroth is interesting. Hard-edge, no category baggage. I'm keeping it in consideration as the product grows past the GitHub App frame.
What's your read on timing — does the name matter most at launch or after you've proven the core value?
It matters earlier than most technical founders think.
Not before the product is clear.
But once the wedge is clear, the name starts shaping how people categorize it.
If you launch as a GitHub bot, people evaluate it like automation.
If you launch as AI repo security infrastructure, people evaluate it like protection.
Same product.
Different trust level.
That matters especially in security because buyers decide quickly whether something feels serious enough to sit in the workflow.
So I would not wait until it fully outgrows the GitHub App frame.
I’d tighten the frame while the category is still forming.
That’s exactly why Vroth fits here.
It gives you the harder security-infra read before the market locks you into “AI scanner bot.”
If useful, we can continue on LinkedIn and pressure-test Vroth against the product direction properly:
www.linkedin.com/in/aryan-y-0163b0278
The timing point makes sense. Naming shapes how buyers categorize before they even try the product. Locking into "AI scanner bot" at launch makes it harder to reframe later even if the product earns a bigger category.
Changed name to VrothSec. (Vroth was alredy taken for a repo by someone). The GitHub App, landing page, and bot are all renamed. The harder security-infra read is the right frame.
Appreciate the pushback on timing — it landed before launch, which is exactly when it needed to.
Also, I am not in linked in.
Thank you for your help.
That’s a strong move.
VrothSec already reads much closer to security infrastructure than “GitHub bot.”
One important note: I actually own Vroth.com.
That’s why I mentioned it specifically.
If this becomes the default security layer for AI repos, I’d think carefully about the structure:
Vroth as the company / infrastructure brand
VrothSec as the security product or module
That gives you room to expand beyond one scanner without renaming again.
Since you already moved in that direction before launch, Vroth.com is probably worth locking down before the product gets more visible.