AI builds your app in a weekend — and ships the security holes with it 🐛🔓
I kept seeing the same thing: someone ships an app built with AI (Cursor, Lovable, v0, Bolt…), it works, they launch — and it's quietly leaking. Exposed API keys, open data endpoints, missing auth, vulnerable dependencies. They find out when someone else does.
So I built Opviva — a security agent that watches your app after you deploy. You talk to it, and it:
scans your live app + the code behind it,
proves each exploit is real (no wall of false positives),
opens the fix as a pull request,
and keeps watching 24/7.
No security team, no dashboards to babysit. Free scan, results in ~60 seconds.
I made a little spaghetti-western to explain it — the owl sheriff vs. the outlaws (Bugs, Vulnerabilities, Errors) 👇
Would love feedback from founders here: what would it take for you to trust an automated fix enough to actually merge it?
Free scan → opviva.com
Security products depend heavily on trust, especially when they keep monitoring an application after launch.
How are you planning to communicate scanner updates, maintenance, or service incidents to Opviva users? Are you considering a public changelog or status page as the product grows?
The interesting opportunity isn't finding security vulnerabilities—it's making automated remediation trustworthy enough that founders stop treating security as a specialist function. I'd keep validating whether customers adopt Opviva because it catches issues or because they become confident enough to merge AI-generated fixes without introducing new risk. That's a much stronger position.
Aryan, this is exactly the bet — you articulated it better than my landing page does. Finding vulns is table stakes; the moat is making the fix trustworthy enough that a non-security founder merges it without a second engineer to babysit it.
The way I'm trying to earn that trust: Opviva never auto-merges — every fix is a reviewable PR. And before it even proposes one, a separate pass has to prove the finding is exploitable (with repro steps), so the fix is closing something real, not chasing a scanner's false positive. The theory is that "here's the exploit, here's the one-line change that closes it, here's the PR" is a very different trust ask than "trust my black box."
Your validation point is the one keeping me up: I genuinely don't know yet whether early users will adopt because it catches things or because they get confident enough to merge. Those are different products. I'm going to instrument exactly that — merge rate on generated PRs vs. just scan usage.
Curious what you think moves the trust needle faster: a really transparent PR ("this is the exploit it closes, here's the diff, here's the test"), or does that trust only ever come from track record over time? Have you worked on remediation before — the framing was too precise to be a first guess.
I appreciate that.
Your last question is exactly where my thinking went as well. I do have a view on how trust gets built for products making high-stakes changes, but I don't think it would be useful as a generic answer in a thread.
I'd rather discuss it in the context of Opviva and the trade-offs you're testing.
If you're interested, what's the best email to reach you on?
Appreciate it, Aryan — happy to swap notes founder-to-founder. Saw you're building Beryxa — what's the angle? Drop me a line and we'll pick it up there: support [at] opviva [dot] com
Thanks! I’ve just sent it over.
Looking forward to hearing your thoughts whenever you have a chance.