GitHub Link: https://github.com/orgito1015/APIScope-AI
Every bug bounty hunter has experienced it.
You open a new target, discover hundreds of API endpoints, import the documentation into Burp Suite, and then spend hours asking yourself the same question:
Where should I start?
Most existing tools help us discover endpoints, fuzz parameters, or validate API specifications. They’re excellent at automation, but they rarely help answer the question that matters most:
Which endpoint is most likely to contain a high-impact vulnerability?
That question inspired me to start building APIScope AI.
The Problem
Modern applications expose enormous API surfaces.
A single OpenAPI specification can contain:
- Hundreds of endpoints
- Thousands of parameters
- Multiple authentication schemes
- Complex object relationships
- Versioned APIs
- Business logic hidden behind seemingly simple requests
Current tooling generally focuses on:
- API validation
- Endpoint discovery
- Fuzzing
- Automated scanning
While those capabilities are essential, experienced bug bounty hunters know that the highest-impact vulnerabilities often come from understanding the application’s logic, not simply sending thousands of requests.
The Idea
Instead of building another scanner, I wanted to build an API security analyst.
The goal is simple:
Give the tool an API specification and let it explain where and why you should investigate.
Rather than listing endpoints, it should think about them.
For example, instead of producing:
PATCH /users/{id}it should explain:
- Why the endpoint is interesting
- Whether it looks vulnerable to BOLA/IDOR
- Whether sensitive fields are writable
- Whether authorisation requirements appear incomplete
- Which manual tests should be performed first
How It Works
The current architecture is divided into several stages.
Separating deterministic analysis from AI reasoning is an intentional design decision.
The rule engine gathers objective facts.
The AI layer provides context, prioritisation, and testing guidance without inventing evidence.
Current Features
The project currently supports:
- OpenAPI 3.x
- Swagger 2.0
- Postman Collections
- HAR traffic files
It analyses:
- Authentication requirements
- Missing security definitions
- Possible BOLA/IDOR candidates
- Mass assignment
- Sensitive writable fields
- SSRF candidates
- Missing 401/403 responses
- Endpoint relationships
- Resource hierarchies
The output can be generated as:
- Markdown
- HTML
- JSON
- AI-ready prompts
The Bigger Vision
The long-term goal goes far beyond documentation parsing.
Imagine pointing the tool at a bug bounty target.
It automatically discovers:
- Swagger
- OpenAPI
- GraphQL
- Postman collections
- Hidden JavaScript endpoints
- Archived APIs
- Historical versions
- Public repositories
It then builds one complete model of the application’s attack surface.
Instead of saying:
“Possible IDOR”
it explains:
Organisation
├── Users
├── Projects
└── Billing
Project
└── Owner
Billing
└── Organisation ID
Then recommends:
- Test horizontal privilege escalation.
- Verify ownership checks.
- Compare responses between two authenticated users.
- Attempt cross-organisation object access.
That type of reasoning is significantly closer to how experienced researchers approach assessments.
AI Should Assist, Not Replace
One lesson became obvious while designing the project:
AI should not replace deterministic security analysis.
Large language models are excellent at:
- explaining risk,
- suggesting testing strategies,
- prioritising attack paths,
- recognising business logic patterns.
However, they should not invent findings.
The analyser first produces verifiable evidence.
Only then does AI help interpret that evidence.
This keeps reports grounded and reduces false positives.
What’s Next?
Future versions will include:
- GraphQL analysis
- gRPC support
- WebSocket inspection
- Burp Suite integration
- Automatic attack graph generation
- Nuclei template generation
- Burp Repeater request generation
- AI-assisted business logic detection
- Multi-agent reasoning for complex attack paths
- Interactive dashboards
Ultimately, I want APIScope AI to become an AI-powered bug bounty copilot that helps researchers spend less time triaging endpoints and more time finding meaningful vulnerabilities.
Final Thoughts
Bug bounty isn’t about sending the most requests.
It’s about asking the right questions.
The next generation of tooling shouldn’t simply automate testing — it should help researchers understand applications, identify the most promising attack paths, and focus their effort where it matters most.
That’s the direction I’m exploring with APIScope AI, and I’m excited to see where it leads.
If you’re interested in API security, offensive security, or AI-assisted bug bounty research, I’d love to hear your thoughts and collaborate on the project.