Finding a flaw isn't proving it. Proving it isn't fixing it.
CloudThinker Cyber is an autonomous offensive security platform. Agents attack your applications and APIs the way a real adversary would, prove every finding with a reproducible exploit, then open the merge request that closes it.
Scoped to environments you approve. Production is off by default.
or book a live demo →
Trusted by security teams that ship daily
Plugs into the stack you already run
Learns how your product works
From your OpenAPI spec, roles and source, the agents build a model of how the product is meant to behave, then test what breaks it. Tenant isolation, payment flows, privilege boundaries. The flaws where every request looks legitimate.
Sees the environment it is testing
Connected to your cloud and Kubernetes, the agents know which role can assume what, which service reaches which, and which endpoint is genuinely exposed. Severity reflects your infrastructure, not a generic score.
Attacks like an adversary
Point the agents at a URL. They map the surface, then chain vulnerabilities into the attack paths a scanner never reaches, because a scanner only ever tests one flaw at a time.
Proves it with an exploit
A separate validator reproduces the attack path before it reaches your queue, using scoped, non-destructive checks. What lands is proven risk, not a maybe.
Tests what changed, not everything
A full pentest on every merge is impossible. Testing the delta is not. Each change is scoped to the endpoints, roles and dependencies it actually touched, and every previously proven finding is replayed as a regression.
Opens the fix as a merge request
The patch is drafted from your code and linked to the finding. Merge it and the agents replay the original path to confirm it is closed.
How it works
Attack. Prove. Fix.
01
Attack
Give the agents a target and whatever context you have: a staging URL, an OpenAPI spec, credentials, your repo, your cloud account. They build a model of how the product is meant to work and what it runs on, then go looking for a way in. After the first full run, each change is scoped to what it actually touched.
02
Prove
Agents chain what they find into a real attack path, then an independent validator reproduces it end to end with scoped, non-destructive checks. Anything it cannot reproduce never reaches you.
03
Fix
Every proven finding arrives triaged, with severity, owner, SLA, and a drafted merge request. Merge it and the agents replay the exact path to confirm it is closed.
Inside Cyber
One console, from first request to merged fix
Real screens from the Cyber workspace, the same views your team lives in.
Live run
Watch the attack happen
Every run streams live through Detect, Analyze, Resolve and Triage. Follow which paths the agents are chaining, watch a finding get proven, and ask them anything mid-run.
Findings
A queue where everything is real
No 400-row scanner dump to sift through. Every item is a proven finding, ranked by exploitability in your environment, with KEV and network-reachable signals, CVSS, an owner, and an SLA clock.
Finding detail
The whole kill chain, not a score
Nothing hides behind a severity number. Open any finding to see the chained attack path, the exact command that reproduced it, every request the agents sent, and the merge request waiting to close it.
The difference
Most tools attack from the outside. Cyber knows what is inside.
An outside attacker has to guess at your architecture, and so does a tool that only sees your front door. Cyber tests with your cloud, your Kubernetes and your code in context, so the attack paths it proves are the ones that actually exist.
Sees your real environment
Connected to your cloud and Kubernetes, agents know which role can assume what, which service talks to which, and which endpoint is actually reachable. Severity reflects real exploitability in your infrastructure, not a generic score.
Understands your business logic
Agents learn how the product is supposed to work, from your OpenAPI spec, roles and source, then test what breaks it: tenant isolation, payment flows, privilege boundaries, abuse of legitimate features. These are the flaws no signature-based tool can find, because nothing about the request looks wrong.
White, gray, or black box
Choose the perspective per target: full source access for maximum depth, credentials-only gray box, or a pure external attacker's view. Same agents, same standard of proof, under the rules of engagement you set.
Every finding
Traced end to end, or it never ships
Every proven finding is a complete case file: how the agents got in, the exploit that proves it, the patch that closes it, and the retest that confirms it. Nothing hides behind a severity score.
The attack path
The full chain, step by step: which request, which role, which response gave it away. You see how the agents got in, not just where they landed.
The proof
A reproducible exploit with the exact command that confirmed it, run scoped and non-destructive. If it cannot be reproduced, it never becomes a finding.
The fix, as a merge request
A patch drafted from your code and linked to the finding. Merge it and the agents replay the original path to confirm it is closed.
The paper trail
Every request the agents sent, logged and exportable, with OWASP API Top 10 coverage your auditor accepts for SOC 2, ISO 27001 and PCI DSS.
Governed
Full autonomy. Your rules of engagement.
Autonomous does not mean unsupervised. You define what the agents may touch, production sits outside that scope by default, and every request they send is recorded in an exportable audit log.
Production excluded by default
Runs stay inside the environments you approve. Adding production is an explicit decision, never a default.
Non-destructive proof
Exploits are reproduced with read-only methods where available, designed to confirm the path without mutating customer data.
Rate-limited and scoped
Traffic is throttled and locked to the rules of engagement you set.
Full audit log
Every request an agent sends is recorded, reviewable and exportable.
API security pentesting report
"A really high-quality report. Now, I can run the application security testing each release instead of quarterly."
Lai Pham
Co-Founder, Diaflow
Proven, then fixed
"It caught a cross-tenant data leak our annual pentest missed, then shipped the fix as a PR the same afternoon. It's like having a red team on every deploy."
Dung Vo
Tech Lead, FPT Cloud
Why proof and fix
Most tools hand you a finding. Cyber hands you a closed one.
Where scanners and point-in-time pentests stop
A finding, probably: no working exploit, so triage starts with is this even real
A generic severity score: scored against the world, not against your environment
A recommendation: your team still writes every patch
A point in time: a two-week window on a target that changes hourly
Retest billed extra: and scheduled months out
The same full sweep every time: so it is too slow to run per merge, and nothing links a finding to the change that caused it
CloudThinker Cyber
A proven attack path: reproduced end to end before it reaches your queue
Ranked by real exploitability: scored against your cloud, your roles, your reachability
The patch, as a merge request: drafted from your code. Review, merge, done
Every approved release: new code is tested the day it ships
Automatic retest: merged fixes are replayed against the original path
Incremental, scoped to the change: the delta is tested per merge and every past finding replays as a regression