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Introduction:
The lines between manual penetration testing and automated intelligence have officially blurred. At DefCamp 2025, Adrian Furtuna (CEO of Pentest-Tools.com) introduced the world to “VIBE Pentesting”—a methodology where human hackers are enhanced, not replaced, by Large Language Models (LLMs). By leveraging Anthropic’s Code, Furtuna demonstrated how AI can transform Google hacking, passive reconnaissance, and even active exploitation from tedious manual tasks into highly efficient, intelligent operations. This article dissects the technical core of that talk, providing the commands, configurations, and step-by-step guides to replicate this AI-powered hacking workflow.
Learning Objectives:
- Understand how to integrate Code into a manual pentesting workflow for automated reconnaissance.
- Execute AI-powered Google hacking (dorking) to uncover hidden assets at scale.
- Automate passive vulnerability discovery using LLMs to analyze JavaScript and HTML.
- Perform active exploitation with AI-generated payloads and command execution.
You Should Know:
1. AI-Powered Google Hacking (Dorking) at Scale
Traditional Google dorking requires manually crafting queries to find exposed directories, login pages, or vulnerable files. In his demo, Furtuna used Code to automate this by feeding it a list of target domains and having the AI generate and test permutations of dorks.
Step‑by‑step guide explaining what this does and how to use it:
1. Setup: Ensure you have Code installed and API keys configured. You will also need `curl` and `grep` installed on your Linux machine.
2. Domain List: Create a file named `targets.txt` containing your target domains.
echo "example.com" > targets.txt echo "testsite.org" >> targets.txt
3. The Open Code and provide it with the following context:
“I am performing a penetration test. Generate 20 advanced Google dorks for each domain in `targets.txt` looking for exposed admin panels, backup files (.sql, .bak, .zip), and configuration files. Output the results in a CSV format.”
4. Execution & Validation: Code will output URLs with Google search syntax. Use a bash script to test if the pages actually exist without relying on Google (to avoid rate limiting).!/bin/bash quick_check.sh while read dork; do Extract the actual site path from the dork TARGET_PATH=$(echo $dork | sed -n 's/.site:([^ ])./\1/p') curl -I -s "http://$TARGET_PATH" | head -n 1 done < generated_dorks.txt
2. Passive Vulnerability Discovery via JavaScript Analysis
Modern web applications hide endpoints and API routes deep within minified JavaScript files. Manually beautifying and reading thousands of lines of code is impractical. Furtuna demonstrated using Code to autonomously download, beautify, and analyze JS files for hidden endpoints.
Step‑by‑step guide explaining what this does and how to use it:
1. Crawl for JS: Use a tool like `gospider` or `httrack` to gather all JavaScript files from the target.
gospider -s https://targetsite.com -d 2 -o output -c 100 -t 20 find output -name ".js" > js_files.txt
2. Beautify JS (Linux): Loop through the files and un-minify them for easier reading.
while read js; do js-beautify -f "$js" -o "$js.beautified" done < js_files.txt
3. LLM Analysis: Feed the beautified files to Code with the prompt:
“Analyze this JavaScript file. Extract all API endpoints (URLs), sensitive hardcoded keys (AWS, Stripe), and internal IP addresses. Provide a structured list.”
4. Windows Alternative: On Windows, you can use PowerShell to download files and use WSL (Windows Subsystem for Linux) to run the `js-beautify` tool.
- Active Vulnerability Discovery: Parameter Fuzzing with AI Context
Standard fuzzing tools like FFUF or Wfuzz generate random strings. AI-driven fuzzing uses context. If Code finds a parameter nameduser_id, it can intelligently generate values likeadmin,null,1=1, or `../../../etc/passwd` specifically for that parameter, rather than relying on a generic wordlist.
Step‑by‑step guide explaining what this does and how to use it:
1. Identify Input Points: Use Burp Suite or `waybackurls` to gather existing parameters.
waybackurls targetsite.com | grep "=" | unfurl keys | sort -u > params.txt
2. AI Payload Generation: Provide the parameter list to Code.
“Here is a list of web parameters: [user_id, file, redirect, debug]. Generate 10 specific payloads for each to test for SQLi, LFI, and Open Redirects.”
3. Execute with FFUF:
ffuf -u https://targetsite.com/page.php?user_id=FUZZ -w ai_payloads.txt -fc 404
4. Active Exploitation: AI-Generated SQLMap Tamper Scripts
When a WAF (Web Application Firewall) blocks standard SQL injection attempts, testers often write “tamper” scripts to evade detection. Furtuna showed how Code can write these Python scripts on the fly based on the WAF error signature.
Step‑by‑step guide explaining what this does and how to use it:
1. Capture the Block: Run a basic SQLMap scan, see it gets blocked (HTTP 403).
sqlmap -u "https://targetsite.com/index.php?id=1" --batch
2. Analyze with Code: Copy the WAF error page text into Code.
“The WAF blocked my request because it detected the word ‘UNION’. Write a SQLMap tamper script that encodes the ‘UNION’ keyword as URL-encoded hex, then adds a random comment between letters to bypass the regex.”
3. Deploy the Script: Code will output a Python script. Save it in the `/usr/share/sqlmap/tamper/` directory.
4. Re-run SQLMap:
sqlmap -u "https://targetsite.com/index.php?id=1" --tamper=code_waf_bypass --batch
5. AI-Assisted Command Injection & Reverse Shells
One of the most potent uses of an LLM during a live engagement is rapidly iterating on command injection payloads. If a classic payload like `; whoami` is filtered, Code can mutate it into $(whoami), %3Bwhoami, or `\nwhoami` almost instantly.
Step‑by‑step guide explaining what this does and how to use it:
1. Manual Test: Identify a field vulnerable to command injection (e.g., a ping utility).
2. Contextual Mutation: Paste the server response into Code.
“I injected ‘; ls’ and the server returned ‘Invalid character ;’. Suggest 10 alternate methods to run ‘ls’ on a Linux server without using a semicolon, including newline encoding, backticks, and environment variable substitution.”
3. Payload Generation:
- Standard: `; ls`
– AI Generated:$(ls),`ls`,%0Als, `export;ls`
4. Windows Equivalents: If the target is Windows, Code can pivot to PowerShell equivalents.AI might suggest base64 encoded PowerShell to avoid special chars powershell -e JABpAHQAZQBtAHMAPQAgAGcAZQB0AC0AYwBoAGkAbABkAGkAdABlAG0AIABDOgBcAAoA
6. Configuration Hardening: The Blue Team Perspective
While the talk focused on offense, the same AI capabilities can be used for defense. By feeding Code a server configuration file, it can instantly spot misconfigurations.
Step‑by‑step guide explaining what this does and how to use it:
1. Apache Analysis:
“Analyze this httpd.conf file. Find all ‘Options’ directives and warn me if ‘Indexes’ or ‘FollowSymLinks’ are enabled in dangerous contexts. Also, find any exposed .git folders.”
2. Cloud Hardening: Provide an AWS IAM policy.
“Review this S3 bucket policy. It is currently public. Rewrite the policy to restrict access to a specific VPC endpoint only.”
7. API Security Scanning
Code can parse OpenAPI/Swagger files and automatically generate attack requests.
Step‑by‑step guide explaining what this does and how to use it:
1. Parse Swagger: Download the `swagger.json` file.
2. AI Analysis:
“From this OpenAPI spec, identify any endpoints that use GET with path parameters (like /api/user/{id}). Generate a curl command for each to test for Insecure Direct Object References (IDOR) by incrementing the ID by 1.”
3. Execute the Attack:
AI Generated Command
for id in {1..100}; do curl -s -o /dev/null -w "%{http_code}\n" https://api.target.com/api/user/$id; done
What Undercode Say:
- Key Takeaway 1: The “VIBE” methodology doesn’t replace the hacker’s intuition; it automates the grunt work. By handling the repetitive tasks of payload generation and log analysis, LLMs allow the pentester to focus on business logic flaws and complex chaining attacks that AI currently cannot conceptualize.
- Key Takeaway 2: This evolution lowers the barrier to entry for sophisticated attacks. Script kiddies now have access to a “senior” assistant that can adapt payloads in real-time. Defenders must shift from signature-based detection to behavior-based analysis, as AI-generated payloads will be unique to every engagement.
- Analysis: The demo by Furtuna highlights a pivotal shift in the cybersecurity arms race. AI tools like Code act as a force multiplier. For a blue team, this means monitoring for the tools used by the attacker (like headless browsers running Code) rather than just the payloads. The “Human Hacker” remains essential for critical thinking, but their speed and accuracy are now augmented by a near-infinite memory and pattern recognition machine.
Prediction:
Within 12 months, we will see the first fully autonomous penetration testing agents that not only discover vulnerabilities but also exploit them and write the report. However, regulation will likely step in, requiring a “human in the loop” for critical infrastructure. The future isn’t AI vs. Human; it’s AI-assisted Human vs. AI-assisted Human, forcing cybersecurity to evolve into a battle of machine learning models rather than just code exploits.
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Reported By: Adrianfurtuna Claudecode – Hackers Feeds
Extra Hub: Undercode MoN
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