Testing applications blind is slow. You send payloads, watch responses, and hope something breaks. But when bug bounty programs publish their source code, everything changes. You stop guessing and start knowing exactly what happens to your input.

The problem? Open source codebases are massive. Thousands of lines across dozens of files. Reading through all of that manually takes days, and by the time you finish, you've probably missed half the interesting attack surface.

That's where AI code analysis comes in. Not as a replacement for manual testing, but as a force multiplier. Tools like Cursor and Antigravity can analyze entire repositories in seconds, answer specific questions about code behavior, and even test applications autonomously.

This article breaks down my exact workflow for testing open source bug bounty programs using AI-assisted code analysis. From deployment to exploitation validation, here's how I approach these targets.

Why Open Source Programs Matter

Some bug bounty programs on platforms like YesWeHack and HackerOne publish their complete source code on GitHub. These aren't toy applications or deliberately vulnerable training labs. They're real production systems with actual bounties.

The advantage is obvious. When you can read the backend code while testing the frontend, you understand the complete attack surface. You see:

• How user input flows through the application
• Where validation happens (or doesn't)
• What database queries look like
• How authentication and authorization work
• Which endpoints exist but aren't linked in the UI

This visibility transforms security testing from educated guessing into systematic analysis.

The Setup: Local and Remote Environments

When I find an open source target, I create two separate environments.

Local Copy

The first copy stays on my local machine. This is for AI analysis with tools like Cursor or Antigravity. These tools work best when they have direct filesystem access to the codebase. They can index the entire repository, understand relationships between files, and answer questions about code behavior instantly.

Remote VPS Copy

The second copy goes on a VPS. I use Hostinger for this. Deploy the application on a clean server, completely isolated from the production environment.

This gives me a private testing environment where I can:

• Test destructive payloads without affecting real users
• Modify the application to add debugging
• Chain exploits without worrying about rate limits
• Validate findings before reporting

Having both environments running simultaneously is critical. The local copy feeds information to the AI. The VPS copy lets me validate what the AI tells me.

Feeding Context to the AI

AI code analysis tools are only as good as the context you give them. Before asking questions about vulnerabilities, I give the AI complete understanding of what the application does and what I'm looking for.

Step 1: Add the Bug Bounty Program Scope

I create a new file in the repository root called program.md. This file contains the complete bug bounty program policy copied directly from the platform.

This includes:

• In-scope assets and features
• Out-of-scope issues
• Severity classifications
• Submission requirements

Why does this matter? Because when I ask the AI to find vulnerabilities, it now understands what actually counts as a valid finding for this specific program. It won't waste time flagging issues that are explicitly out of scope.

Step 2: Initial Code Analysis

Before testing anything, I prompt the AI to analyze the codebase structure:

Prompt:

Analyze this codebase and provide:

1. Technology stack (languages, frameworks, databases)
2. Authentication mechanisms and session handling
3. All user input entry points (forms, API endpoints, query parameters)
4. Database interaction patterns (ORM vs raw queries)
5. File upload handling if present
6. Any custom security middleware or validation libraries

Focus on code patterns that commonly lead to vulnerabilities.

This initial analysis takes 30-60 seconds. The AI returns a structured overview of the entire application architecture. I now know what I'm dealing with before I even open the browser.

Testing with AI Assistance

Once the AI understands the codebase, real testing begins. But instead of blind fuzzing, I'm asking specific questions based on what I see in the frontend.

Finding Dangerous Code Paths

When I encounter a feature that handles user input, I immediately ask the AI about the backend implementation.

Example 1: Password Reset Flow

I'm testing a password reset form that accepts an email address. Before sending any requests, I ask:

Prompt:

The password reset endpoint accepts an email parameter. Show me:

1. The exact code path from input to database query
2. What happens if I send an array of emails instead of a single email
3. Whether the email parameter is validated before database interaction
4. If there's any rate limiting on this endpoint
5. What the response looks like for valid vs invalid emails

The AI traces the complete flow. It shows me the controller method, the validation logic (or lack of it), the database query, and the response structure. I know immediately whether parameter pollution works, whether there's a timing attack vector, or if the endpoint leaks user enumeration.

Example 2: Finding Hidden Sinks

Instead of manually auditing every file, I ask the AI to find dangerous patterns:

Prompt:

Search the entire codebase for any location where user-controlled input flows into:

1. SQL queries (both raw queries and ORM methods that allow raw SQL)
2. System commands (exec, shell_exec, subprocess, etc.)
3. File operations (file paths, file names, file contents)
4. HTML rendering (innerHTML, template injection points)
5. Deserialization functions

For each instance found, show me:
- The file and line number
- The exact user input that reaches this sink
- Whether validation or sanitization exists
- If the vulnerability is actually exploitable given the code context

This returns every potential injection point in the application. Not all of them are exploitable, but now I know exactly where to focus my testing efforts.

Example 3: Access Control Analysis

Authorization bugs are everywhere, but they're hard to find through fuzzing. The AI can spot them instantly:

Prompt:

Analyze all API endpoints and identify:

1. Endpoints that modify or delete resources
2. What authorization checks exist (if any)
3. Whether the checks validate resource ownership or just authentication
4. If there are any endpoints that skip authorization entirely
5. Parameter tampering opportunities (changing user IDs, changing resource IDs)

Look specifically for IDOR vulnerabilities where one user can access another user's resources.

The AI maps out the entire authorization structure. It shows which endpoints check ownership, which only check authentication, and which have no checks at all.

Autonomous Testing with Antigravity

Antigravity has a feature that goes beyond code analysis. It can actually open a browser, navigate the application, and interact with it autonomously.

Here's how it works:

Step 1: Provide Credentials

If the application requires authentication, I give the AI valid credentials for a test account I created.

Step 2: Define the Testing Goal

Instead of manually clicking through the application, I describe what I want tested:

Prompt:

Open the application in a browser using these credentials:
- Email: test@example.com
- Password: TestPassword123

Navigate to the user profile section and test for:
1. Stored XSS in profile fields (name, bio, location, etc.)
2. IDOR when viewing other users' profiles
3. File upload vulnerabilities if profile pictures are supported
4. Whether profile data is properly escaped in all contexts

For each test, show me the DOM before and after input, the HTTP requests made, and whether the vulnerability exists.

The AI opens Chrome, logs in, navigates to the profile page, fills in XSS payloads, submits forms, and inspects the DOM to see if the payloads execute. It captures screenshots, HTTP traffic, and provides a report of what it found.

Why This Matters

The AI can read the rendered DOM. This means it sees the actual HTML that reaches the browser, including:

• Dynamically generated content from JavaScript
• Content loaded via AJAX after page load
• Hidden form fields and parameters
• Client-side validation that might be bypassable

It can also execute JavaScript in the browser context, which lets it test for DOM-based vulnerabilities that pure HTTP traffic analysis would miss.

What This Workflow Actually Looks Like

Here's a real example of how these pieces fit together.

I'm testing an open source e-commerce application program. The scope includes testing the checkout flow for vulnerabilities.

Step 1: Deploy and Analyze

Clone the repo. Deploy on my VPS. Load the codebase in Cursor. Feed the program policy to the AI.

Step 2: Map the Attack Surface

Ask the AI: "Show me all endpoints involved in the checkout process, from cart to payment confirmation."

It returns a list of 8 endpoints with their HTTP methods, parameters, and authorization requirements.

Step 3: Identify Weak Points

I notice one endpoint handles quantity updates for cart items. I ask: "Show me the code that handles cart quantity updates. Can I set negative quantities? Can I set quantities that exceed inventory?"

The AI shows me the controller code. There's validation for negative numbers, but no check against available inventory. Potential business logic flaw.

Step 4: Test on VPS

Open the deployed app on my VPS. Add an item to cart. Intercept the quantity update request. Change quantity to 999999. The order processes successfully even though the product only has 10 units in stock.

Step 5: Validate Impact

Ask the AI: "What happens in the database when an order quantity exceeds available inventory? Does this create a negative inventory value or does it fail?"

It shows me the order processing code. Inventory goes negative. No checks, no failures. This is a valid business logic vulnerability.

Total time from identifying the endpoint to confirming the vulnerability: 5 minutes.

Without AI analysis, I would have spent 30+ minutes reading through checkout code trying to understand the flow. I might have missed this endpoint entirely if I was only testing through the UI.

What AI Code Analysis Can't Do

This workflow is powerful, but it's not magic. The AI has clear limitations.

It Doesn't Understand Business Context

The AI can tell you if input validation exists. It can't tell you if a feature is supposed to be restricted to certain user roles based on business requirements that aren't in the code.

It Misses Complex Logic Chains

Multi-step vulnerabilities that require specific sequences of actions in a specific order are hard for AI to identify. Race conditions, time-of-check-time-of-use bugs, and state confusion issues often require human intuition.

It Can't Replace Manual Testing

The AI accelerates analysis. It doesn't replace the need to actually test payloads, chain exploits, or validate findings. You still need to understand web security fundamentals.

It Generates False Positives

When you ask the AI to find "all SQL injection points," it will flag every database query. Many of them use parameterized queries or ORM methods that are actually safe. You still need to review each finding and determine exploitability.

The Real Advantage

The value of AI code analysis isn't finding vulnerabilities automatically. It's eliminating the time spent on reconnaissance and code review so you can focus on exploitation.

Instead of spending hours reading code to understand how a feature works, you get that information in seconds. Instead of blindly fuzzing every input hoping something breaks, you know exactly which inputs reach dangerous sinks.

This matters because time is the limiting factor in bug bounty. The faster you can analyze a target, the more targets you can test. The more targets you test, the more vulnerabilities you find.

Open source programs give you visibility. AI tools let you analyze that visibility at scale. Combined, they create a workflow that's fundamentally different from traditional black-box testing.

Conclusion

Testing open source bug bounty programs with AI assistance isn't about automation. It's about working smarter.

You still need to understand web vulnerabilities. You still need to craft exploits. You still need to validate findings. But you skip the hours of manual code review that would normally come before any of that.

The workflow is simple: deploy locally and remotely, feed the AI complete context about the program and codebase, ask specific questions about code behavior instead of reading files manually, validate AI findings through actual testing on your VPS, and report confirmed vulnerabilities with full understanding of the code that causes them.

If you're only testing closed-source applications, you're working harder than necessary. Look for programs that publish their code. The analysis you can do when you see both sides of the application is completely different.

The tools exist. The programs exist. The only question is whether you're using them.