Korrosiv.AI PENTEST ENGINE v0.9.2-alpha
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Korrosiv.AI

Dissolve your attack surface._

An AI-native penetration testing engine for web applications and APIs. Point it at a target. Watch it think, adapt, and find what others miss.

korrosiv@pentest:~$ cat about.md

AI-first. Built to break things.

Korrosiv.AI is an AI-native, AI-first automated penetration testing engine.

It doesn't scan for known signatures or run scripted checks. It thinks like a pentester, analyzing application logic, chaining vulnerabilities, and adapting its approach in real time.

Built by pentesters who understood that the future of offensive security isn't more tools, it's smarter ones.

[CORE]

PRECISE

Every attack vector is methodical and targeted. We don't brute force, we dissolve.

[CORE]

TECHNICAL

Built by pentesters, for pentesters. The platform respects its audience's expertise.

[CORE]

CONTROLLED

Corrosion is not chaos, it's chemistry. Every test is deliberate and documented.

korrosiv@pentest:~$ cat threat_assessment.md

By the time the report lands, the app has already changed.

Traditional penetration testing is a point-in-time exercise. You engage a team, wait weeks for availability, then receive a report that reflects the application as it existed during a narrow testing window.

Meanwhile, your development team ships daily. New features, new endpoints, new attack surface, all deployed between the time a finding is documented and the time it's read. The report is outdated before the ink dries.

Manual human-led pentests aren't scaling to the speed of modern development. The vulnerability lifecycle has compressed from months to hours, but the testing model hasn't changed in a decade.

Week 1

Pentest engagement begins

Week 2-3

Manual testing in progress

Week 4

Report delivered

Weeks 1-4

Dev team shipped 47 deployments, 12 new API endpoints, and 3 major features, all untested

[ALERT]

The average web application receives multiple updates per week. A quarterly pentest covers less than 2% of the changes shipped.

korrosiv@pentest:~$ cat capabilities.md

What the platform does.

[MODULE]

Web Application Testing

Full-stack analysis of web applications, testing authentication flows, session management, and probing business logic the way a human pentester would.

[MODULE]

API Penetration Testing

Automated discovery and exploitation of REST and GraphQL endpoints. Broken auth, injection, mass assignment, tested systematically.

[MODULE]

AI-Driven Analysis

Not a scanner with an AI label. The AI is the pentester, reading responses, adapting payloads, and chaining findings like a senior consultant.

[MODULE]

Actionable Reporting

Every finding includes reproduction steps, evidence screenshots, impact analysis, and remediation guidance your team can act on immediately.

[MODULE]

Real-Time Visibility

Watch the AI work through a live dashboard. See endpoints discovered, tests executed, and vulnerabilities found, as they happen.

[MODULE]

Responsible by Design

Built with guardrails. Scoped testing, controlled exploitation, and full audit trails. Offensive capability with defensive discipline.

korrosiv@pentest:~$ cat origin.md

Why we built this.

On an internal infrastructure engagement, a spreadsheet of legacy credentials turned up on a file share. One string kept recurring, a string that looked completely random. No dictionary match, no known pattern. A human pentester moves on.

AI didn't. It identified the string as a keyboard walk pattern, recommended adding it to the active password spray, and that single insight corroded through layers of vendor defences into full domain compromise.

In a typical human-led pentest, AI reasons on roughly 5-20% of the engagement context. The rest gets skimmed or missed under time pressure. If that number reaches 100%, the outcomes change completely.

Human-led pentest
~5-20% of data reasoned on by AI
Korrosiv.AI
100% every output analyzed, every pattern caught
korrosiv@pentest:~$ diff --human-led --ai-led

Human-led pentests have limits.
AI-led pentests don't.

Human-Led Pentests

  • - Limited by time, scope, and fatigue
  • - Reasons on 5-20% of tool output
  • - Weeks to schedule, weeks to deliver
  • - Report is stale before it's read
  • - Inconsistent across engagements
  • - Cannot scale with deployment velocity
vs

AI-Led Pentests

  • + No fatigue, no time constraints
  • + Reasons on 100% of all data
  • + Deploy on demand, results in minutes
  • + Findings reflect the live application
  • + Consistent methodology every time
  • + Scales with every code push