AI penetration testing software
Attackers test your security with AI.So should you.
RIA Labs runs autonomous penetration tests against your applications, and reports only what it managed to actually exploit. Paid reports add the script to reproduce each finding and — in white-box engagements — a fix to apply.
First scan is free · starts with a signed authorisation · no agent to install
$ ria -t app.example.com --scan-mode deep
▸ sandbox isolated container · proxy + browser up
▸ recon 41 endpoints · 6 auth flows mapped
▸ agents 12 spawned across 7 vulnerability classes
✓ validated SQL injection · POST /auth/login
CWE-89 · CVSS 9.8 · exploit ran · PoC attached
AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
✓ validated IDOR · GET /api/v1/orders/{id}
CWE-639 · CVSS 6.5 · read another user's data
AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N
✗ rejected suspected SSRF · /api/fetch
exploit failed — not reported
▸ complete 2 findings · 1 discarded at validationThe problem
Your security process is annual. Your attack surface is not.
Manual penetration testing has become a periodic rubber stamp — expensive, point-in-time, run once or twice a year for a compliance sign-off. Between engagements you are dark, which is exactly when your attack surface moves the most.
And the gap is widening
AI assistants are pushing more code into production, faster. Systems are getting larger and less predictable between each manual test. The more AI writes your software, the more automated offensive testing that software needs.
Scanners were never the answer
Static and dynamic scanners flag patterns and hand you a queue to triage. They cannot chain two weaknesses together, reason about your business logic, or tell you whether the thing they found is actually exploitable. Attackers can.
How it works
It tests the way a person would, then proves it
An LLM decides what to attack and how to chain it together — the part where judgment matters. A deterministic harness governs how every one of those actions runs and whether it actually worked.
- 01
Point it at a target
A URL, a repository, or a compiled binary. Black-box needs nothing but the address and your written authorisation.
- 02
It attacks the target
Agents work inside a disposable, isolated container with a real tester's toolkit — an intercepting proxy, a scripted browser, a shell, a Python runtime.
- 03
You get what survived
Candidates that fail validation are discarded rather than reported. What reaches you has been executed and confirmed.
What you get
Three things per finding
All of it in one interactive report you can hand to an engineer and have them fix something that afternoon. This is the paid report — a free scan returns scores and counts only.
A finding, not a warning
Each issue arrives pinned to the endpoint or the function it lives in, mapped to a CWE, and scored with CVSS 3.1 — computed from the base metrics, not asserted by a model.
A reproduction script
The exploit that demonstrated the issue, as code you can run. Its second job is the useful one: run it again after your fix and you have proof the fix actually landed.
A proposed fix
In white-box engagements, a diff against the exact lines responsible — formatted so a reviewer can apply it straight from a pull request.
Two modes
With your code, or without it
Black-box is how most people start, because it costs you nothing to set up. White-box is where the results get sharp.
Black-box
No access to your code.
We work the way an outside attacker does — from the outside in, against your running application. It is how demos run, because it needs nothing from you but a URL and permission.
- Nothing required from your team but a target and written authorisation
- Findings are exploit-validated against the live application
- Lower efficiency than white-box — no source to reason about, so coverage depends on what is reachable from outside
White-box
We read your source, and we talk to you.
Source access plus a one-hour interview with your team — the same way any manual penetration testing firm starts an engagement. Static and dynamic analysis run together, and findings come back with a proposed fix.
- Static analysis and live testing correlated against each other
- One hour of your engineers' time to walk us through the architecture
- Findings arrive with a proposed fix as a diff, ready to drop into a pull request
The engagement
Authorised, scoped, and then destroyed
Offensive testing is only legitimate when it is authorised in writing. Nothing about your systems gets touched before that, and nothing about them survives after.
- 01
Sign the agreement and the authorisation
Nothing runs before this. Not recon, not a port scan, nothing.
- 02
A discovery call
What carries real risk, which roles and tenants exist, what would actually hurt.
- 03
You provide the environment and access
Test credentials and reachability — plus source, in white-box. By default nothing is installed on your side.
- 04
The first report
A day to several weeks, depending on the application and how much validation it needs.
The testing environment is destroyed. The report is all that survives.
Every test runs in its own isolated container. When the engagement ends, that container and everything in it — the copied source, the captured traffic, the exploit code — is torn down. One interactive report leaves it, and nothing is installed in your environment or left behind.
Coverage
Forty-plus vulnerability classes, mapped to CWE
Including the full OWASP API Security Top 10, framework-specific weaknesses for the stacks people actually ship on, and cloud identity paths in Azure, AWS and Kubernetes.
- SQL injection
- IDOR
- SSRF
- XSS
- RCE
- XXE
- SSTI
- CSRF
- Path traversal
- Race conditions
- Business logic
- Authentication & JWT
- SAML attacks
- Request smuggling
- Prototype pollution
- NoSQL injection
- Mass assignment
- Multi-tenant isolation
- GraphQL
- WebSocket security
- Insecure file upload
- Cryptographic failures
- CORS misconfiguration
- Subdomain takeover
- Supply chain
- and more
Beyond the web
It reads compiled software too
Most AI pentesters stop at the web application. We also correlate a shipped binary, its debug symbols, and its source tree into a single view — reasoning about the machine code that actually ships, not just the source that was meant to compile into it.
This matters for endpoint software — agents, EDR, RMM, telemetry clients — where the thing on a customer's machine is a binary, not a repository.
Scoped honestly: this analysis is static. Findings are recovered from decompilation and pinned to the exact function, but they are not runtime-proven the way web findings are.
Find out what a scan turns up
The first scan costs nothing. Nominate a target and we will run it, then walk you through what came back.