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Introduction:
In the competitive arena of bug bounty hunting, efficiency is the currency of success. A recent engagement demonstrated how shifting from broad reconnaissance to targeted, automated analysis of client-side JavaScript can directly lead to the discovery of high-impact vulnerabilities like broken access control. This methodology transforms overwhelming asset enumeration into a precise roadmap for exploitation.
Learning Objectives:
- Understand how to systematically extract and analyze JavaScript files for hidden API endpoints and internal paths.
- Learn to configure and use the open-source tool `SecretHunter` to automate the discovery of secrets and endpoints.
- Develop a workflow to proactively test identified endpoints for common authorization and access control vulnerabilities.
You Should Know:
- From Static Assets to Attack Surface: The Power of JS Analysis
Modern web applications heavily rely on JavaScript files, which often contain hardcoded API endpoints, internal application paths, and sometimes even forgotten debug parameters or credentials. Manual review is tedious. The solution is automated parsing.
Step-by-step guide:
- Asset Discovery: Use a tool like `assetfinder` or `subfinder` to enumerate subdomains, then `httpx` to probe for live web servers.
subfinder -d target.com -silent | httpx -silent -o live_targets.txt
- JavaScript File Extraction: Crawl the live targets and filter for `.js` files and HTML pages containing JavaScript.
cat live_targets.txt | gau | grep -E '.js$' | sort -u > js_files.txt Alternatively, use `katana` for crawling: cat live_targets.txt | katana -js-crawl -silent -o js_files.txt
- Initial Manual Analysis: Quickly examine files for obvious endpoints.
Look for patterns like /api/, /v1/, /admin/ grep -n "/api/|/v1/|/admin/" js_files.txt | head -20
2. Automating the Hunt with SecretHunter
`SecretHunter` is a tool designed to scan JavaScript files for endpoints, secrets (API keys, tokens), and interesting strings. It significantly reduces noise and highlights potential leads.
Step-by-step guide:
1. Installation: Clone the repository and install dependencies.
git clone https://github.com/neonprimetime/SecretHunter.git cd SecretHunter pip3 install -r requirements.txt
2. Basic Scan: Run it against your list of JavaScript files.
python3 SecretHunter.py -l js_files.txt -o scan_results.json
3. Analyze Output: The tool outputs JSON containing categorized findings. Focus on the `Endpoints` and `Secrets` sections.
jq '.Endpoints[]' scan_results.json | sort -u jq '.Secrets[]' scan_results.json
3. Validating and Mapping Undocumented Endpoints
Not all discovered endpoints are valid or accessible. You must verify their status and behavior.
Step-by-step guide:
- Probe Endpoints: Use `ffuf` or `httpx` to check HTTP status codes.
cat endpoints.txt | httpx -status-code -title -o validated_endpoints.txt
- Identify Patterns: Look for administrative endpoints (
/admin/,/dashboard/), API routes (/api/v1/user/), or debug paths (/debug/,/console/). - Map Parameters: Use tools like `arjun` or `paramspider` to find hidden parameters for these new endpoints, which are prime testing grounds.
python3 paramspider.py -d target.com -l high -o params.txt --subs False
4. Exploiting Access Control Issues
Discovered endpoints, especially those with administrative-sounding paths, are often vulnerable to Broken Access Control (BAC), a top OWASP risk. Test for Horizontal and Vertical Privilege Escalation.
Step-by-step guide:
- Horizontal Privilege Escalation: If you find an endpoint like
/api/v1/orders/{orderId}, try accessing another user’s order by changing the ID.Using a valid session cookie (USER_A_COOKIE), try to access another resource curl -H "Cookie: session=USER_A_COOKIE" https://target.com/api/v1/orders/12345 curl -H "Cookie: session=USER_A_COOKIE" https://target.com/api/v1/orders/67890
- Vertical Privilege Escalation: Access an admin endpoint `/admin/userList` with a low-privilege user session.
curl -H "Cookie: session=LOW_PRIV_USER_COOKIE" https://target.com/admin/userList -v
- Missing Function Level Access Control (MFLC): Directly access a privileged function’s API route that should not be linked for your role.
5. The Bounty Workflow: From POC to Disclosure
A clean, professional report is crucial for resolution and reward.
Step-by-step guide:
- Document Everything: Record all steps with screenshots and commands. Tools like `stepci` or manual curl commands with `-v` flag are perfect.
- Craft a Proof-of-Concept (POC): Create a simple script or series of commands that reliably reproduces the issue without causing damage.
- Responsible Disclosure: Submit via the platform’s official channel (HackerOne, Bugcrowd, etc.). Include: Clear title, detailed steps, impact assessment, and POC. Never exfiltrate or modify real user data.
What Undercode Say:
- Automation is a Force Multiplier: Tools like `SecretHunter` are not cheating; they are essential for cutting through the noise of modern application attack surfaces, allowing researchers to focus on human-centric analysis of business logic.
- The Devil is in the Details (Client-Side): Organizations often overlook what they ship to the browser. A systematic review of client-side code remains one of the highest-yield activities for finding overlooked vulnerabilities that scanners miss.
The described workflow epitomizes the modern ethical hacker’s strategy: leveraging automation for comprehensive reconnaissance, applying critical thinking to prioritize leads, and meticulously testing application logic. The $1300 bounty is a direct result of this efficient, tool-augmented methodology. This approach shifts the focus from “finding everything” to “finding the right thing,” demonstrating that in bug bounty hunting, precision trumps volume every time.
Prediction:
The integration of AI and machine learning into reconnaissance tools will become standard, moving beyond simple pattern matching to behavioral analysis of JavaScript. Tools will soon predict which undocumented endpoints are most likely to be vulnerable based on framework usage, code complexity, and historical exploit data. This will further lower the barrier to entry for high-quality security research while simultaneously forcing developers to adopt stricter “code hygiene” and implement more robust, automated checks for secrets and debug pathways in their CI/CD pipelines. The cat-and-mouse game will escalate to the AI layer.
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