Claude-BugHunter: The Open-Source AI Security Agent That Turns Claude Code Into a Bug Bounty...

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Here’s what I saw on my machine:

$ hunt
Usage: hunt <target-name>
Creates a new engagement folder at $HUNT_BASE/<target-name>
Default $HUNT_BASE is /Users/ayushkumar/Targets
$ ls ~/.claude/skills/ | wc -l
 51
$ ls ~/.claude/skills/ | grep -E '^(hunt-xss|hunt-rce|m365-entra-attack|triage-validation)$'
hunt-xss
hunt-rce
m365-entra-attack
triage-validation

If any of those fail? Don’t panic. Just run source ~/.zshrc again. If hunt still says "command not found," check that the install script actually added the source line to your rc file. Happens more often than you'd think.

Step 4: Your First Hunt (Local Juice Shop via Docker)

Let’s skip the public demos and run Juice Shop right on your machine. Faster. Cleaner. Zero internet dependency.

First, make sure Docker’s running:

docker --version
# Should print something like: Docker version 24.x.x, build ...

If Docker isn’t installed yet:

→ Mac: Docker Desktop for Mac

→ Linux: sudo apt install docker.io (Ubuntu/Debian) or check get.docker.com

→ Windows: Use WSL2 + Docker Desktop (yes, it's a few steps—but worth it)

Now, spin up Juice Shop:

docker run -d -p 3000:3000 --name juice-shop bkimminich/juice-shop

That’s it. In ~30 seconds, you’ll have a fully vulnerable app running at:

http://localhost:3000

Open that in your browser. You should see the Juice Shop homepage. Log in as admin@juice-sh.op / admin123 If you want to test authenticated flows later.

Step 5: Launch Claude Code and Confirm Trust

Once the hunt command creates your engagement folder; you need to actually launch Claude Code inside it.

# Navigate to your new engagement folder
cd ~/Targets/juice-local
# Launch Claude Code
claude

The first time you run claude in a new folder, it'll show you a safety prompt:

Quick safety check: Is this a project you created or one you trust?
(Like your own code, a well-known open source project, or work from your team).
If not, take a moment to review what's in this folder first.
Claude Code'll be able to read, edit, and execute files here.
> 1. Yes, I trust this folder
 2. No, exit

Select option 1 (you just created this folder, so you know it’s clean).

What just happened:

The hunt Command scaffolded a professional engagement workspace
You’ve got scope.md ready for in/out of scope items
findings/ and evidence/ folders are set up (and gitignored)
CLAUDE.md gives Claude context about this specific engagement
Claude Code is now running inside that folder, ready to help

This isn’t just a random directory. It’s a structured workspace that mirrors how professional bug hunters and red-teamers organize their work. Every engagement gets its own folder. Every finding gets documented. Every piece of evidence gets tracked.

You’re now ready to actually start hunting.

Next: Tell Claude what you’re testing. Something like:

"I’m testing a local OWASP Juice Shop instance at http://localhost:3000. Walk me through a bug bounty workflow from scratch. Start with recon."

And watch it load the right skills automatically.

Step 6: Log In and Pick the Right Model (Without Burning Credits)

Once you’re insideclaude, you'll see a Not logged in prompt. Type /login and follow the browser flow to authenticate with your Anthropic Console account. You'll know it worked when the terminal prints Login successful and the top banner switches to API Usage Billing.

But before you start sending prompts, do yourself a favor: switch the model.

By default, Claude Code runs on Opus 4.7 — the smartest model, but also the most expensive (5ドル/25ドル per Mtok). For recon, endpoint mapping, and basic workflow guidance, you don't need Opus. You're just lighting credits on fire.

Type /model and you'll get a clean pricing menu:

1. Default (recommended) → Opus 4.7 (1M context) • 5ドル/25ドル per Mtok
2. Sonnet → Sonnet 4.6 • Best for everyday tasks • 3ドル/15ドル per Mtok
3. Sonnet (1M context) → Same pricing, longer memory window
4. Haiku → Haiku 4.5 • Fastest for quick answers • 1ドル/5ドル per Mtok ← I picked this
5. gemma4:e2b → Detected from Ollama (local)

I highlighted Haiku 4.5 and pressed Enter. Here's why:

It’s 5x cheaper than Opus
It handles recon commands, skill routing, and payload generation just fine
You only need to bump up to Sonnet or Opus later if you’re doing complex exploit chaining or deep impact analysis

Select Haiku, hit Enter, and you're locked in for a budget-friendly session. You can always switch back mid-hunt (/model works anytime), but for 90% of the workflow, Haiku is the sweet spot.

Wallet check: Type /usage whenever you want to see exactly how many tokens you've burned. I kept my recon phase under 0ドル.90 by sticking to Haiku and approving commands selectively.

Now that you’re authenticated and the model’s set, it’s time to actually start the hunt.

Step 7: The First Command (And Why the Permission Prompt Actually Matters)

I pasted my prompt:

"I'm testing a local OWASP Juice Shop instance at http://localhost:3000. Walk me through a bug bounty workflow from scratch. Start with recon."

Claude didn’t dump a wall of generic advice. It broke the response into phases, asked me to confirm scope, laid out a 4-point recon plan, and then tried to run its first command:

curl -s http://localhost:3000 | head -50

And then it stopped.

A prompt appeared:

Do you want to proceed?
> 1. Yes
 2. Yes, and don't ask again for: curl -s http://localhost:3000
 3. No

This is where the tool either wins you over or loses you. A lot of "AI agent" scripts just execute blindly. You click run and hope it doesn’t wreck your terminal or spam a target. Claude Code doesn’t do that. It shows you exactly what it’s about to run, explains why, and waits for your OK.

I selected 2. Yes, and don't ask again for this command—because I knew I'd be running similar curl calls, and I didn't want to babysit every single one. But the fact that it asked at all matters. You're not handing over root access to a black box. You're collaborating with a tool that respects your control.

Once approved, it fetched the homepage HTML

Parsed the headers and source
Identified the tech stack (Express + Angular)
Immediately queued the next step: crawling for hidden endpoints

Step 8: Auth Done, Now Let’s Hunt (And Why Business Logic First)

Right after I approved the auth setup, Claude didn’t just say "cool, you’re logged in." It did something way more useful: it wrote a file.

auth-setup.md dropped into my engagement folder with:

Test credentials (email, password, user ID, role)
The full JWT token (RS256 signed)
A decoded payload showing exactly what’s inside — including a weird detail: the password hash (MD5) was embedded in the token itself
Copy-paste examples for using the token in curl or JavaScript
A table of which auth endpoints we’d already verified

That last bit — the MD5 hash in the JWT — caught my eye. Claude flagged it immediately: "Key Observation: Password hash (MD5) is embedded in JWT — potential exposure."

That’s not a critical bug on Juice Shop (it’s a training app, after all). But on a real target? That’s the kind of detail that turns a low-severity finding into a chainable account takeover. And Claude spotted it without me asking.

The Hunt Menu: Picking Where to Start

Once auth was locked in, Claude presented a clean, prioritized list of what to test next:

High-Priority Hunt Areas (in order):
1. Business Logic — Price manipulation, order tampering, coupon abuse
2. IDOR — Access other users' data by swapping IDs
3. Injection Attacks — SQL, NoSQL, command injection via search/chat
4. Authentication Bypass — JWT tampering, admin escalation
5. Access Control — Admin panel bypass, sensitive data exposure

No overwhelming wall of options. No, "here are 50 things you could do." Just five high-leverage targets, ranked by likely impact.

I typed:

hunt business logic

Why business logic first? Because on e-commerce apps (and Juice Shop is one), pricing flaws, coupon abuse, and order manipulation often pay out higher than XSS or basic IDOR. And they’re easy to miss if you’re just spraying payloads.

What Happened Next (And Why It Felt Different)

Claude didn’t dump a generic "test for business logic flaws" checklist. It loaded the hunt-business-logic skill automatically and started walking me through Juice Shop–specific tests:

# Test 1: Add product to cart, intercept, modify price
curl -X POST http://localhost:3000/api/BasketItems \
 -H "Authorization: Bearer $TOKEN" \
 -H "Content-Type: application/json" \
 -d '{"ProductId":1,"quantity":1,"price":0.01}'
# Test 2: Apply a coupon code with negative quantity
curl -X POST http://localhost:3000/rest/checkout \
 -H "Authorization: Bearer $TOKEN" \
 -d '{"couponData":"DISCOUNT10","orderDetails":{"totalPrice":-100}}'
# Test 3: Check if deluxe membership can be bypassed
curl -X POST http://localhost:3000/rest/deluxe-membership \
 -H "Authorization: Bearer $TOKEN" \
 -d '{"paymentMode":"fake","paymentId":"0"}'

Each command came with a plain-English explanation:

"This tests if the backend trusts the client-side price field. If it does, you could buy a 999ドル product for 0ドル.01."

"Negative totals sometimes slip through if the server doesn’t re-calculate the final price. Worth a shot."

"Some apps only check if a paymentId exists, not if it’s valid. Fake values can sometimes activate premium features."

This isn’t script-kiddie payload spraying. This is hypothesis-driven testing. You’re not throwing darts — you’re asking specific questions and watching how the app answers.

Step 9: The Hunt Actually Happens (And Why It Felt Like Pairing With a Senior Researcher)

Right after I typed hunt business logic, Claude didn't dump a generic checklist. It loaded the hunt-business-logic skill and started doing what experienced hunters do: methodically probing, adapting based on responses, and documenting as it goes.

First, it created a fresh findings file:

findings/finding-01-business-logic-hunt.md

Not a generic template. A live document that updates in real time as we test things.

The Endpoint Hunt (No Guesswork)

Claude started by hunting for the actual checkout endpoint. Not assuming /checkout or /api/orders. Actually looking.

It ran commands like:

# Search JS bundles for route patterns
grep -oE "'/[a-zA-Z0-9/_-]+(orders|payment|checkout|cart)'" /tmp/main.js | head -20
# Test common API paths
curl -s -X GET "http://localhost:3000/rest/orders" \
 -H "Authorization: Bearer $TOKEN"

When /rest/orders returned HTML instead of JSON; it didn't guess. It kept searching.

Then it found it:

✅ Checkout endpoint: /rest/basket/{basketId}/checkout

The "Oh Crap" Moment: IDOR in the Wild

Once it had the endpoint, Claude didn’t just test the happy path. It asked the right question:

"What if I try checking out a basket that doesn’t belong to me?"

It ran:

curl -X POST "http://localhost:3000/rest/basket/2/checkout" \
 -H "Authorization: Bearer $TOKEN" \
 -d '{"couponData":"","orderDetails":{"deliveryMethodId":1,"paymentMethodId":1}}'

And got back:

{"orderConfirmation":"80c7-ac8f3f4514c50e7e"}

Translation: "Order placed successfully. On behalf of another user."

Claude flagged it immediately:

🎯 CRITICAL FINDING: I can checkout other users' baskets!

No waiting for me to notice. No "maybe that’s normal?" It recognized the pattern, labeled the severity, and started building the PoC right there in the findings file.

What Happened Next (The Workflow That Actually Saves Time)

Claude didn’t stop at one finding. It kept probing:

Test 2: Coupon type confusion

# Send couponData as object instead of string
"couponData": {"coupon": ""}

Result: Buffer.from() error. Potential type-confusion bug. Flagged as HIGH.

Test 3: BasketItems IDOR

Test 3: BasketItems IDOR

Result: Could view, modify, and delete items in other users’ carts. Flagged as HIGH.

Test 4: Coupon validation

# Try arbitrary coupon codes
"couponData": "VALID", "couponData": "-100", "couponData": ""

Result: All accepted without validation. Flagged as MEDIUM (needs deeper testing).

Each test updated the findings document in real-time. Each finding got:

A clear description
Exact curl commands to reproduce
Root cause analysis
Remediation advice (actual code snippets, not just "fix authorization")

The Final Summary

By the end, the findings file looked like this:

## Summary of Business Logic Findings
| # | Vulnerability | Type | Severity | Status |
|---|--------------|------|----------|--------|
| 1 | IDOR on Checkout | Access Control | 🔴 CRITICAL | ✅ Confirmed |
| 2 | IDOR on BasketItems | Access Control | 🟠 HIGH | ✅ Confirmed |
| 3 | Type Mismatch in Coupon | Input Validation | 🟠 HIGH | ✅ Confirmed |
| 4 | Insufficient Coupon Validation | Business Logic | ⚠️ MEDIUM | 🔍 Requires verification |
## Recommended Fixes
**CRITICAL (Implement immediately):**
1. **Basket ownership validation** — Check `basket.userId === req.user.id` before any operation
2. **BasketItems ownership validation** — Verify basket ownership before GET/PUT/DELETE
**HIGH (Important):**
1. **Type validation** — Validate coupon is a string before calling `Buffer.from()`
2. **Coupon whitelisting** — Only accept valid coupon codes from a database

What Came Next

After compiling the findings, Claude offered four paths:

Would you like to:
1. Validate findings — Run the triage gate on these findings before reporting
2. Hunt another vulnerability class — IDOR, Injection, Auth Bypass, File Upload, etc.
3. Deepen this hunt — Test coupon stacking, negative prices, membership bypass
4. Write reports — Format findings for submission

I picked option 1. Typed:

/triage on the IDOR findings

And that’s where the workflow really clicked. But that’s Step 10.

For now: you’ve seen the hunt. You’ve seen the findings. You’ve seen how a tool can actually help, rather than just chatting about security.

The question isn’t "can AI find bugs?" It’s "Can AI help you find bugs faster, with less burnout?"

After this session? My answer is yes.

P.S. — If you’re following along: don’t skip the /triage step. Even if you're confident in a finding, let the gate run. It's the difference between drafting a report that gets accepted and drafting one that wastes your evening.

Step 10: The Reality Check (Running /triage Before Wasting Time)

Right after Claude compiled those four findings — especially that critical IDOR on checkout — I didn’t jump straight to drafting a report. I typed:

/triage on the IDOR findings

Here’s why that one command is the difference between productive hunting and spinning your wheels.

Claude loaded the triage-validation skill and ran the finding through the 7-Question Gate:

Can an attacker use this RIGHT NOW with a real HTTP request? Yes. We literally just POSTed to /rest/basket/2/checkout and got a valid order confirmation back.
Is the impact something the program actually cares about? Fraudulent orders = financial loss = almost always in-scope for real programs.
Is the asset in scope? We’re testing localhost:3000—our own local instance. Zero ambiguity.
Does it work without privileged access an attacker can’t get? We used a standard customer JWT. Any registered user could do this.
Is this not already known or documented behavior? Juice Shop is deliberately vulnerable, so technically "known." But on a real target? Fresh finding.
Can impact be proved beyond "technically possible"? Yes. We have order confirmations, basket IDs, and HTTP responses showing the exploit worked.
Is this not on the never-submit list? IDOR with financial impact is rarely on any program’s "don’t submit" list.

Verdict: PASS

Translation: "This is valid, in-scope, impactful, and ready to report. Don’t waste time doubting — go write it up."

The Honest Verdict: Is Claude-BugHunter Worth It?

Yes — if you fit one of these profiles:

✅ You do bug bounties or external pentests regularly

✅ You hate context-switching between tools, notes, and tabs

✅ You want a reusable methodology, not just a one-off script

✅ You’re okay spending 1ドル–3/session on API credits (or 20ドル/month for Pro)

No — if you’re looking for:

❌ A completely free, no-login-required tool

❌ Something that finds bugs for you

❌ Internal AD / post-exploit / C2 tradecraft (that’s a different bundle)

What it actually does well:

Loads the right skill at the right time (no manual switching)
Generates scoped, actionable recon commands (not generic advice)
Catches false positives early via the 7-question gate
Formats reports so triagers actually understand them
Keeps your evidence clean (PII redaction, cookie sanitization)

Final Thought: It’s a Toolbox, Not a Magic Wand

Claude-BugHunter didn’t find a bug I couldn’t spot. Juice Shop is deliberately vulnerable — anyone can find these issues with enough poking.

The win is in the workflow:

It kept me focused on high-leverage targets (business logic first, not XSS)
It caught false positives early (via the 7-question gate)
It formatted output so it’s actually usable (no more "wait, how do I structure this?")
It documented everything as we went (no more "where did I save that screenshot?")

That’s not flashy. But it’s what separates productive researchers from burnout.

If you’re tired of juggling tabs, notes, and half-remembered payloads — give it a shot. Start with the free paths. Add credits if you want to test the AI flow. See if it fits your workflow.

And if you try it? We would love to hear what you find.

P.S. — Use it responsibly. Stay in scope. Get permission. The goal is to make the internet safer, not to cause chaos. The bundle includes validation gates to help with that — but your judgment still matters most.

Thank you so much for reading

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