• gethacked.ca
• February 21, 2026
• No Comments

We’ve always believed that penetration testing isn’t just about running scanners and checking boxes. It’s about thinking like an attacker — anticipating logic flaws, chaining vulnerabilities, and uncovering what automated tools miss.

The Challenge: A “Secure” API Endpoint

A Toronto-based fintech client came to us with an API they believed was hardened. They had:

• WAF in place
• Input sanitization
• Rate limiting
• JWT authentication

Our initial scans (Burp Suite, Nuclei, Nessus) returned clean results — medium-severity issues only.

But something felt off.

The /api/v1/resolve endpoint accepted arbitrary URLs in a target parameter. We suspected SSRF — but every manual test failed. The backend filtered out common internal domains (localhost, 127.0.0.1, metadata.google.internal). Even DNS rebinding attempts were blocked.

Enter AI: Our Custom “ExploitGPT” Engine

We integrated a fine-tuned LLM into our Burp workflow — dubbed ExploitGPT. This wasn’t just another plugin. It was an autonomous vulnerability reasoning engine trained on our internal pentest logs.

Here’s how it worked:

1. Context Analysis: ExploitGPT parsed the API response headers, JavaScript assets, and server banners — noticing that X-Internal-Proxy: true was returned on all responses.
2. Payload Generation: Instead of trying common SSRF vectors, it generated 47 novel payloads based on protocol abuse patterns:
   • gopher://10.5.3.10:6379/_set%20exploit%201
   • http://10.5.3.10:8080/__debug__/reload?token=...
   • Even a crafted file:///proc/self/cwd/.env path to read environment variables
3. Chain Discovery: One payload — http://127.0.0.1:9000/v1/serialize?data=<base64-encoded-Java-serialization-object_ — triggered an unexpected deserialization endpoint exposed only under internal proxy headers.
4. Automated Exploitation: Once identified, ExploitGPT auto-generated a Python exploit script that:
   • Triggered SSRF via the target param
   • Injected serialized Java object with RCE payload (java.lang.Runtime.exec())
   • Exfiltrated /etc/passwd, then dropped a reverse shell

The Result: A Zero-Day Exploit Chain

We didn’t just find an SSRF.

We found:

• SSRF → Internal Proxy Bypass
• Deserialization of Untrusted Data
• Remote Code Execution on Java-based microservice

The client had no idea their internal monitoring service (monitor:9000) exposed a Java serialization endpoint — and it wasn’t documented anywhere.

ExploitGPT found it in under 3 hours. Without AI, this would’ve taken days of manual fuzzing and reverse engineering.

Why This Matters

AI isn’t replacing pentesters — it’s amplifying them.

• Speed: Reduced test cycles from weeks to days.
• Depth: Found vulnerabilities that bypassed traditional rule-based scanners.
• Scalability: One AI agent can analyze 50+ endpoints simultaneously with human-level context awareness.

Final Thoughts

AI-assisted penetration testing is no longer futuristic — it’s essential_

The most dangerous vulnerabilities aren’t those with CVEs… they’re the ones no one thought to test for.

With AI as our co-pilot, we don’t just find bugs. We uncover hidden attack surfaces that could have cost clients millions.

If you’re still relying on manual pentesting alone — it’s time to upgrade.

Let’s get hacked… smarter.

Want us to run an AI-powered pentest on your system? Contact us at hello@gethacked.ca.