Listen to this Post
Introduction:
A security researcher’s recent Hall of Fame achievement highlights a pervasive threat in modern web applications: exposed API keys. By employing fuzzing techniques against a JavaScript file, the researcher uncovered a hardcoded Google API key, a critical vulnerability that could have led to substantial financial loss and data exposure. This incident underscores the critical need for robust secrets management and proactive security testing in the software development lifecycle.
Learning Objectives:
- Understand the risks associated with exposed API keys and how to hunt for them.
- Learn practical fuzzing techniques to discover hidden endpoints and secrets in web assets.
- Master command-line tools and scripts to automate the discovery and validation of sensitive data leaks.
You Should Know:
1. Web Asset Enumeration with `curl` and `grep`
Before fuzzing, you must first enumerate all accessible web assets, particularly JavaScript files. The combination of `curl` and `grep` is a powerful starting point.
`curl -s https://target.com/ | grep -Eo ‘src=”[^”]\.js”‘ | sed ‘s/src=”//’ | sed ‘s/”//’`
Step-by-step guide:
- `curl -s https://target.com/`: Silently fetches the HTML source of the target’s homepage.
- The output is piped (
|) togrep -Eo 'src="[^"]\.js"', which uses a regular expression to find and output only the matching parts of lines that look like JavaScript file inclusions (e.g.,src="app.js"). - The `sed` commands then clean the output, removing the `src=”` and trailing quote, leaving you with a clean list of JS file paths. You can then loop through this list, downloading each file for analysis.
2. Fuzzing for JavaScript Files with `ffuf`
Fuzzing is the systematic process of injecting invalid or unexpected inputs to find hidden endpoints or files. `ffuf` is a fast web fuzzer written in Go.
`ffuf -w /usr/share/wordlists/seclists/Discovery/Web-Content/common.txt -u https://target.com/FUZZ -e .js -fc 403`
Step-by-step guide:
-w: Specifies the wordlist path. The `common.txt` wordlist from `SecLists` is a great starting point.- `-u https://target.com/FUZZ`: Defines the target URL. The keyword `FUZZ` is where the tool substitutes entries from the wordlist.
-e .js: Automatically tries each wordlist entry with the `.js` extension appended.-fc 403: Filters out HTTP 403 (Forbidden) responses, which are common and can clutter results. This helps you focus on accessible files (returns 200 OK).
3. Pattern Matching for Secrets with `grep`
Once you have a collection of JavaScript files, the next step is to scan them for patterns that match API keys, tokens, and other secrets.
`grep -E -n “(api_key|apikey|key|token|secret|password)[^a-zA-Z0-9]{0,3}[‘\”]?[A-Za-z0-9]{20,40}[‘\”]?” .js`
Step-by-step guide:
1. `-E`: Enables extended regular expressions.
-n: Shows the line number of the match, which is crucial for quickly locating the secret in the file.- The complex regular expression looks for common variable names like “api_key”, followed by an assignment operator (like `=` or
:), and then a string of 20-40 alphanumeric characters, which is a typical length for many API keys. This pattern can be refined based on the specific key you are hunting for (e.g., AWS keys have a specific prefix).
4. Validating Google API Keys
Finding a string that looks like an API key is only half the battle. Validation is critical to confirm its exposure and potential impact. Google provides specific endpoints to check the status and permissions of a key.
`curl -s “https://www.googleapis.com/oauth2/v3/tokeninfo?key=AIzaSyA…EXAMPLE” | python3 -m json.tool`
Step-by-step guide:
- This command sends the potentially exposed key to a Google API endpoint.
2. The `-s` flag keeps `curl` silent.
- The output is piped to `python3 -m json.tool` to format the JSON response for easy reading.
- The response will indicate if the key is valid and often details the associated Google Cloud project, API scopes, and any usage restrictions. A valid response confirms the key is active and exposed.
5. Automating the Hunt with a Bash Script
Combining these steps into a script automates the process, making it efficient and repeatable.
`!/bin/bash
TARGET=$1
echo “[+] Enumerating JS files for $TARGET…”
Step 1: Get JS files from page
curl -s $TARGET | grep -Eo ‘src=”[^”].js”‘ | sed ‘s/src=”//’ | sed ‘s/”//’ > jsfiles.txt
Step 2: Fuzz for common JS filenames
ffuf -w /usr/share/wordlists/seclists/Discovery/Web-Content/common.txt -u $TARGET/FUZZ -e .js -fc 403 -o fuzz.json -of json &>/dev/null
cat fuzz.json | jq -r ‘.results[].url’ >> jsfiles.txt
Step 3: Download and scan each file
echo “[+] Scanning for secrets…”
while read js; do
wget -q “$TARGET/$js” -O – | grep -E -n “(api_key|key|token)[^a-zA-Z0-9]{0,3}[‘\”]?[A-Za-z0-9]{20,40}[‘\”]?”
done < jsfiles.txt`
Step-by-step guide:
1. Save this code as `secrets_hunter.sh`.
2. Make it executable: `chmod +x secrets_hunter.sh`.
- Run it against a target: `./secrets_hunter.sh https://example.com`.
- The script will output any lines and line numbers from JavaScript files that match common secret patterns, providing a quick initial assessment.
6. Windows PowerShell Equivalent for Asset Discovery
Security testing isn’t exclusive to Linux. PowerShell on Windows is equally powerful.
`Invoke-WebRequest -Uri “https://target.com” | Select-Object -ExpandProperty Content | Select-String -Pattern ‘src=”[^”]\.js”‘ -AllMatches | ForEach-Object { $_.Matches.Value }`
Step-by-step guide:
1. `Invoke-WebRequest` fetches the web content, similar to curl.
2. `Select-Object -ExpandProperty Content` extracts the HTML body.
3. `Select-String` uses a regex pattern to find all JavaScript file references.
4. The `ForEach-Object` loop processes each match, allowing you to subsequently download and analyze each file.
7. Mitigation: Securing Secrets with Environment Variables
The core mitigation for this vulnerability is proper secrets management. Never hardcode secrets. Instead, use environment variables.
Example (Node.js application):
`// BAD: Hardcoded key
const googleApiKey = ‘AIzaSyA…EXAMPLE’;`
`// GOOD: Using environment variable
const googleApiKey = process.env.GOOGLE_API_KEY;`
Step-by-step guide:
- Create a `.env` file in your project root (and add it to
.gitignore).
2. Store your secret in the file: `GOOGLE_API_KEY=your_key_here`.
- In your application code, use a library like `dotenv` to load the variables at runtime:
require('dotenv').config(). - In your production environment, set these environment variables directly in your hosting platform’s configuration panel (e.g., AWS Systems Manager, Azure Key Vault, Heroku Config Vars).
What Undercode Say:
- The Low-Hanging Fruit is Still Ripe: This case proves that high-impact vulnerabilities are not always complex zero-days. Methodical, basic reconnaissance and testing of standard application assets like JS files remains a highly effective strategy for bug bounty hunters and penetration testers.
- Automation is the Hunter’s Best Friend: The difference between a casual glance and a professional assessment is automation. Scripting the process of enumeration, fuzzing, and pattern matching transforms a tedious manual task into a scalable, repeatable security control.
The incident involving OnlineTri demonstrates a mature security posture not just in the researcher’s skill but crucially in the company’s response time. A vulnerability’s true risk is a product of both its exploitability and the time it remains open. Patching within minutes is the gold standard and drastically reduces the window of opportunity for malicious actors. This highlights a critical shift in cybersecurity: value is placed not on finding vulnerabilities alone, but on the entire lifecycle from discovery to remediation. For organizations, building a culture that encourages and facilitates responsible disclosure is as important as building secure code in the first place. The real victory here is a symbiotic relationship between security researchers and developers, leading to a more secure ecosystem for all users.
Prediction:
The automation of secrets detection will become deeply integrated into developer workflows and CI/CD pipelines. Static Application Security Testing (SAST) tools will evolve to include more sophisticated, context-aware secret detection that can distinguish between fake keys in tutorials and real keys in production code. Furthermore, we will see a rise in “secret-as-a-service” scanning, where external auditors continuously monitor public code repositories, exposed endpoints, and certificate transparency logs for leaked credentials belonging to their clients, offering remediation as a service. The cat-and-mouse game will escalate, but the advantage will shift towards defenders through pervasive, automated monitoring.
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: Netipalli Manoj – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
