How Reading API Documentation Helped Me Find 3 Critical Bugs: A Bug Bounty Hunter’s Secret Weapon + Video

By /

Listen to this Post

Featured Image

Introduction:

In the world of bug bounty hunting, the difference between a missed vulnerability and a critical find often comes down to one simple habit: reading the target’s documentation. As highlighted by security researcher Muhammed Alkesht’s recent success—three bugs discovered with the advice “Always read documentation for target”—thoroughly understanding how an application is intended to work reveals where it might be broken. This article dives deep into the methodology of leveraging documentation to uncover hidden attack surfaces, automate testing, and ultimately secure more bounties.

Learning Objectives:

  • Understand how to systematically extract attack surface from API and application documentation.
  • Learn to identify common vulnerability classes (IDOR, Broken Access Control, etc.) by analyzing documentation.
  • Master techniques to automate documentation review using scripts and AI tools for efficient bug hunting.

You Should Know:

  1. The Art of Reading Documentation Like a Hacker
    Start by approaching the target’s documentation not as a user manual, but as a blueprint of potential vulnerabilities. The tip from Alkesht is straightforward: documentation often reveals endpoints, parameters, authentication flows, and business logic that attackers can abuse.

Step‑by‑step guide:

  • Locate the documentation: Look for /docs, /api, /swagger, /redoc, or `/_static` directories. Use tools like `dirb` or `ffuf` to brute‑force common paths. 
    ffuf -u https://target.com/FUZZ -w /usr/share/wordlists/common-api-paths.txt
  • Identify all endpoints: Extract every URL, HTTP method, and parameter. For OpenAPI (Swagger) docs, you can fetch the JSON spec: 
    curl -s https://target.com/swagger/v1/swagger.json | jq '.paths' > endpoints.json
  • Map authentication requirements: Note which endpoints require tokens, API keys, or specific headers. This helps later when testing for privilege escalation.

2. Extracting API Endpoints from OpenAPI/Swagger

Automating the extraction of endpoints from OpenAPI specifications saves time and ensures you don’t miss any.

Step‑by‑step guide:

  • Download the spec: 
    wget https://target.com/swagger/v1/swagger.json
  • Parse with jq to list all paths and methods: 
    jq -r '.paths | to_entries[] | .key as $path | .value | to_entries[] | "($path) [(.key)]"' swagger.json
  • Generate a wordlist of endpoints for further fuzzing: 
    jq -r '.paths | keys[]' swagger.json | tee api_endpoints.txt
  • For Windows PowerShell, you can use: 
    (Get-Content swagger.json | ConvertFrom-Json).paths.PSObject.Properties.Name

3. Testing for Broken Object Level Authorization (BOLA)

Once you have a list of endpoints that reference object IDs (e.g., /users/{id}, /orders/{order_id}), you can test for BOLA (also known as IDOR).

Step‑by‑step guide:

  • Capture a legitimate request with your own object ID (e.g., GET /api/user/123).
  • Modify the ID to another user’s (e.g., 124) and resend using `curl` or Burp Repeater. 
    curl -X GET "https://target.com/api/user/124" -H "Authorization: Bearer <your_token>"
  • If you receive the same data as for your own ID, you’ve found a BOLA vulnerability.
  • Automate the process with a simple bash loop: 
    for id in {100..200}; do
curl -s -o /dev/null -w "%{http_code} %{url_effective}\n" "https://target.com/api/user/$id"
done
  1. Leveraging AI to Analyze Documentation for Hidden Parameters
    AI can help parse large documentation sets and suggest potential attack vectors by identifying unusual parameters or deprecated endpoints.

Step‑by‑step guide:

  • Feed the documentation (text or OpenAPI JSON) into an AI model like ChatGPT or a local LLM (e.g., LLaMA).
  • Use a prompt like:
    > “Analyze this OpenAPI spec and list all endpoints that accept user‑supplied IDs. Also highlight any endpoints with ‘admin’ or ‘internal’ in the path.”
  • Review the AI’s output and cross‑reference with your manual findings.
  • Automate further by writing a Python script that uses the `openai` API to classify endpoints: 
    import openai
openai.api_key = "your_key"
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": "From this spec, find endpoints vulnerable to IDOR: " + spec_text}]
)
print(response.choices[bash].message.content)

5. Automating Endpoint Discovery with Custom Scripts

Create a Python script that crawls documentation, extracts all endpoints, and generates a report with potential test cases.

Step‑by‑step guide:

  • Write a script using `requests` and `beautifulsoup4` to scrape HTML documentation: 
    import requests
from bs4 import BeautifulSoup</li>
</ul>

url = "https://target.com/docs"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
for link in soup.find_all('a', href=True):
if '/api/' in link['href']:
print(link['href'])

    – For OpenAPI specs, use the `prance` library to parse and traverse: 

    pip install prance

    from prance import ResolvingParser
parser = ResolvingParser('swagger.json')
for path, methods in parser.specification['paths'].items():
print(path, methods.keys())

    – Save the results to a file and import into Burp Suite for active scanning.

    1. Real‑World Case Study: How a Simple Parameter Led to a Critical Bug
      Imagine an e‑commerce API documented with an endpoint /api/orders/{id}. The documentation states that the `id` is a UUID, but during testing you notice that the application also accepts sequential integers. By brute‑forcing order IDs from 1000 to 2000, you discover you can view other customers’ orders—a classic IDOR. This bug was found purely because you read the documentation and understood the expected parameter format.

    7. Mitigation Strategies for Developers

    To prevent such vulnerabilities, developers should:

    • Restrict access to documentation (e.g., require authentication for internal API docs).
    • Implement robust access controls on every endpoint, not just the ones shown in docs.
    • Use unpredictable IDs (UUIDs) and validate ownership server‑side.
    • Regularly audit API documentation for accidental exposure of sensitive endpoints.

    What Undercode Say:

    • Key Takeaway 1: Documentation is a goldmine for bug hunters—it provides a complete map of the attack surface.
    • Key Takeaway 2: Automating the extraction and analysis of documentation saves time and uncovers hidden vulnerabilities that manual review might miss.

    In an era where APIs power most applications, the humble act of reading the manual becomes a critical security practice. Bug hunters who master this skill not only find more bugs but also understand the business logic deeply, leading to higher‑impact reports. Developers, on the other hand, must treat their documentation as sensitive as code, because attackers are reading it too.

    Prediction:

    As AI continues to evolve, we will see a rise in automated documentation analysis tools that can instantly generate attack scenarios from any API spec. This will shift the bug bounty landscape toward more sophisticated, logic‑based vulnerabilities, forcing developers to adopt stricter documentation security and access control measures. The future of hacking lies not just in code, but in the words that describe it.

    ▶️ Related Video (76% Match):

    🎯Let’s Practice For Free:

    IT/Security Reporter URL:

    Reported By: Muhammed Alkesht – Hackers Feeds
    Extra Hub: Undercode MoN
    Basic Verification: Pass ✅

    🔐JOIN OUR CYBER WORLD [ CVE News • HackMonitor • UndercodeNews ]

    💬 Whatsapp | 💬 Telegram

    📢 Follow UndercodeTesting & Stay Tuned:

    𝕏 formerly Twitter 🐦 | @ Threads | 🔗 Linkedin | 🦋BlueSky

Related Posts: