AI Penetration Testing
AI PenetrationTesting
Human pentesters, amplified by purpose-built AI harnesses. We build testing rigs around your systems so our researchers attack deeper, faster, and more thoroughly than tooling or humans alone.
- Human-Led, AI-Amplified
- Custom Test Harnesses
- Deeper Coverage
- 10×
- More cases tested
- 24/7
- Harness runs continuously
- 0
- Findings without human proof
// The thesis
No more black-box pentesting and the shallow results it ships. We wrap a custom test harness around your systems and run AI agents against it 24/7, in your own infrastructure — next-generation security testing, for less than a pentest.
// How It Works
Human-Led, AI-Amplified
A pentest wrapped in a purpose-built harness. AI gives breadth and speed; our researchers give depth and judgment.
- 01
Map Attack Surface
We enumerate hosts, APIs, parameters, and auth flows to scope exactly what the harness will exercise.
- 02
Build the Harness
A custom rig is built around your target: adapters, instrumentation, and oracles tuned to your stack.
- 03
AI Fuzz & Probe
AI generates and executes test cases at machine scale, surfacing crashes and anomalous behavior.
- 04
Human Triage
Researchers verify every candidate, discard noise, and chain bugs into real, exploitable findings.
- 05
Exploit & Report
Working PoCs, an attack narrative, and prioritized fixes. Plus the harness, handed off to you.
// Methodology
AI / LLM Testing Methodology
A hands-on, manual process tuned to how AI features actually fail. Five phases, from first contact to a fix your engineers can ship.
- 01
Initial Exploration
We use the AI features as a real user first: wiring up API endpoints, seeding RAG stores, and running the automation flows. That grounds us in how the model behaves and where it touches the rest of the app.
- 02
Attack Surface Analysis
We map every input vector and output flow: what the model ingests, whether that data comes from the app, internal users, or untrusted external sources, plus any exposed model details or system prompts that sharpen our injection attacks.
- 03
AI Functionality Mapping
Using prompt engineering and application assessment, we enumerate the downstream power the AI actually holds: API access, tool calls, RAG file reads, web access, code execution. Then we probe each one for insecure implementation and broken access control.
- 04
Threat Modeling & Exploitation
We build an informal threat model ranked by the AI's capabilities, data access, and ties into business logic, then attack it by hand across the full technique set.
Direct prompt injectionIndirect prompt injectionJailbreakingData manipulationMulti-turn manipulationRAG poisoningAuthorization bypassPrivilege escalationData exfiltrationSensitive info disclosureDownstream system attacks - 05
Reporting & Remediation
You get live reporting throughout the engagement, so findings reach you as we confirm them, not weeks later. Each one ships with a working proof-of-concept and a prioritized, engineer-ready fix.
// What We Test
Where the Harness Goes
From web apps and APIs to auth flows and the AI features you're shipping, the harness keeps testing where automated tools stop.
Web Applications
We find broken access control, injection, SSRF, XSS, and business-logic flaws. The harness fuzzes every route, parameter, and form, then replays each request across user roles to expose what only shows up in context.
APIs
We find BOLA/IDOR, broken function-level authorization, mass assignment, and abuse and rate-limit gaps. The harness drives your API from its spec, mutating payloads and swapping identities to prove cross-tenant and privilege boundaries.
Auth & Business Logic
We find MFA and session weaknesses, privilege escalation, and multi-step workflow abuse. The harness scripts real user journeys and replays them out of order and across roles to surface state and trust confusion.
AI / LLM Features
We find prompt injection, tool and function-call abuse, jailbreaks, and data exfiltration paths. The harness adversarially fuzzes prompts, context, and tool calls across the model's full surface.
// Why This Approach
Why AI Pen Testing
AI breadth and human depth: neither alone is enough. Together they find what scanners and one-off manual tests miss.
Continuous & Reusable
The harness runs 24/7 and is yours to keep, so you can re-run it on every release instead of buying a new point-in-time test.
Test forever10× the Coverage
The harness executes orders of magnitude more test cases than a manual engagement.
Machine-scale breadthCustom Harness Per Target
We build a rig tuned to your systems, not an off-the-shelf scanner profile.
Built for your stackHuman-Verified Findings
Nothing ships without a researcher proving it. Zero AI hallucinations in your report.
No false positivesReal Exploits, Not Theory
Every finding comes with a working proof-of-concept and a reproduction path.
Proof, not guesses// Deliverables
What You Receive
- A threat model of your attack surface: the entry points, trust boundaries, and the paths that actually matter.
- A working, reproducible proof-of-concept for every finding, with the exact steps to trigger it.
- Prioritized, exploitability-ranked remediations your engineers can act on, not a raw scanner dump.
- The custom harness itself, yours to keep: it runs continuously and your engineers can extend it as the product changes.
- A coverage report showing exactly what was exercised, and what wasn't.
- A live debrief with your engineering team to walk through every finding and the fixes.
// From our blog
Related Research
CVE-2025-32433 PoC
The first public PoC for a CVSS 10 RCE — written with AI-accelerated fuzzing.
ReadAdvisoryML Evasion Attacks: How Adversaries Trick AI
White-box, gray-box, black-box, and transfer-based evasion attacks on ML models.
ReadAdvisoryLittle Bug, Big Impact: $25K Bounty
How a small finding, surfaced fast, led to a critical bug and a $25K bounty.
Read// Get started
Put a Harness on Your Stack
Tell us what you're shipping. We'll build a testing harness around it and show you what AI at machine scale, plus human judgment, actually finds.