Find the vulnerability before your users do.
Safe, Not Sorry points an autonomous adversarial AI at your own applications, breaks in the way a real attacker would, and proves exactly what it reached — continuously, not once a year.
No agents to install on every box · data never leaves your machine
Built for the teams running the software that runs everything
“I hacked a school ERP in 30 minutes.
That should not be the case.”
ERPs, LMSs, CRMs and the new wave of AI assistants hold student records, payroll and customer data — and most of the organizations running them have no security team, no continuous red-team, and no way to test the AI layer at all. A yearly pentest is a snapshot. Modern apps change every week and need continuous adversarial testing.
Attack → Prove → Explain → Fix → Verify
An autonomous agent runs the full loop a human red-teamer would — end to end, on every scan.
Map the surface
The agent discovers endpoints, auth boundaries, AI tools and sensitive data paths on your app.
Break in autonomously
It crafts and fires real payloads — SQLi, XSS, auth bypass, prompt injection — adapting to what it sees.
Evidence, not alerts
Every finding ships with the exact request and the data it exposed. No false-positive noise.
Plain-English root cause
Not "your prompt is bad" — the systemic failure, in language a non-expert can act on.
Concrete remediation
A specific, architectural fix — parameterize the query, move authorization server-side.
Prove the fix holds
It replays the same attack and shows it's blocked. We don't just find bugs — we prove they're gone.
A security team in a download.
Autonomous agent
Generates and executes its own attacks. No test scripts to write, no payloads to maintain.
Runs on your machine
A lightweight local runner reaches localhost, staging and internal apps a cloud scanner never could.
Data never leaves
Your app and its data stay on-device. Only sanitized attack patterns ever sync — never your records.
AI-native
Built for LLM apps: prompt injection, tool abuse, and agent authorization bypass — not just web bugs.
Continuous
Schedule it or wire it into CI. Every deploy gets a fresh adversary, not a yearly snapshot.
Bring your own model
Point it at any OpenAI-compatible endpoint — a local model, or a hosted one via OpenRouter.
Not "an AI that scans your website."
| Capability | Legacy pentest | OSS red-team CLIs | AI-scan SaaS | Safe, Not Sorry |
|---|---|---|---|---|
| Continuous, not once-a-year | ✗ | ✓ | ✓ | ✓ |
| Autonomous — generates its own attacks | ✗ | ✗ | ✓ | ✓ |
| AI-native (agents, tools, prompt injection) | ✗ | ✓ | ✓ | ✓ |
| Runs locally — reaches internal / localhost apps | ✓ | ✓ | ✗ | ✓ |
| For orgs with no security team | ✗ | ✗ | ✗ | ✓ |
| Proves the fix works | ✗ | ✗ | ✗ | ✓ |
Trusted where the stakes are real.
“It found an auth bypass in our student portal in under a minute — one our last pentest completely missed. The plain-English writeup meant our one dev could fix it that afternoon.”
“We don't have a security team. Safe, Not Sorry is the closest thing we've had to one — and it runs against our internal app that no cloud scanner could ever reach.”
“The verify step is the killer feature. It re-runs the exact attack after the fix and shows it blocked. That's the evidence our board actually wanted.”
Start free. Scale when you're ready.
Every plan runs the full Attack → Prove → Verify loop. Priced by apps and scan frequency.
For a single app and the curious.
- 1 application
- Weekly autonomous scans
- Core vuln classes
- Local runner
- Community support
For teams shipping every week.
- Up to 10 applications
- Daily scans + on-demand
- All vuln classes + AI-layer tests
- CI/CD integration
- Fix & verify loop
- Email & Slack alerts
For regulated and self-hosted orgs.
- Unlimited applications
- Self-hosted deployment
- SSO, RBAC & audit logs
- Private attack corpus
- Custom & private models
- Dedicated support + SLA
Learn from attacks — not from your secrets.
The runner sends home the shape of an attack, never your data. Every vulnerability found in one environment becomes a generalized test that protects everyone else.
Questions, answered.
Is this legal to run against my app?
Yes — Safe, Not Sorry is built for authorized self-testing of applications you own or have written permission to test. Every scan requires you to confirm authorization for the target.
Does my data leave my machine?
No. The runner and the model calls happen on your infrastructure. Only sanitized, generalized attack patterns are ever synced — never customer records, credentials, or documents.
Do I need a security team to use it?
No. That's the point. Findings come with plain-English explanations, exact evidence, and concrete fixes a single developer can apply.
Which models does it use?
Any OpenAI-compatible endpoint — a local model on your own hardware, or a hosted model via OpenRouter. You choose the brain; we drive the attack loop.
Can it test AI features, not just web apps?
Yes. It's designed for LLM applications — prompt injection, tool abuse, and agent authorization bypass — alongside classic web vulnerabilities.
How is this different from a yearly pentest?
A pentest is a snapshot. Safe, Not Sorry runs continuously, so every deploy meets a fresh adversary — and it proves each fix actually holds.
Find the vulnerability before your users do.
Join the early access program. Point it at your app and watch it break in — in seconds.
Request early access →