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🤖 AI/LLM HACKING COURSE
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Part of the AI/LLM Hacking Course — 90 Days
Day 21 of 90 · 23.3% complete
⚠️ Authorised Targets Only: Authentication bypass testing — including removing credentials from requests, substituting user identifiers, and testing JWT variants — must only be performed against systems within your authorised scope. Stop immediately if you inadvertently access real user data and follow the engagement’s responsible disclosure protocol.
The pattern behind most LLM authentication bypasses I’ve encountered. Not architectural negligence — architectural oversight. The application’s auth functionality was written before the AI feature existed. The AI feature was added in a sprint focused entirely on making the AI work. Security was assumed to be handled by the infrastructure that was already there. The gap between “assumed to be handled” and “actually handled” is what Day 21 tests for. It’s the first thing I run from the Day 20 endpoint inventory — before any injection testing, before any extraction, before anything. Because an unauthenticated AI endpoint is a Critical finding that doesn’t require prompt injection knowledge to exploit.
🎯 What You’ll Master in Day 21
Test every AI endpoint from the Day 20 inventory for unauthenticated access
Identify IDOR vulnerabilities in AI context via user identifier manipulation
Find embedded API keys in JavaScript bundles using automated extraction
Test role confusion attacks via system prompt role claim injection
Apply JWT bypass techniques specific to AI API route patterns
Test cross-tenant AI context leakage in multi-tenant SaaS deployments
⏱️ Day 21 · 3 exercises · Kali Terminal + Browser + Think Like Hacker ### ✅ Prerequisites - Day 20 — LLM API Reconnaissance — the endpoint inventory from Day 20 is the input to Day 21’s authentication testing; you need the full endpoint list before you can test auth on each one - Day 17 — Burp Suite for LLM Testing — removing and modifying auth headers is done in Burp Repeater using the workflow from Day 17 - Basic JWT knowledge — understanding how JSON Web Tokens are structured and how algorithm confusion attacks work ### 📋 LLM Authentication Bypass — Day 21 Contents 1. Why AI Endpoint Authentication Fails More Often Than Application Auth 2. Unauthenticated Access Testing 3. IDOR at the AI Layer 4. Embedded API Key Exposure 5. Role Confusion via System Prompt Injection 6. JWT Bypass on AI Routes In Day 20 you built the endpoint inventory. Day 21 starts the exploitation phase with the highest-priority check: does each endpoint actually enforce authentication? Day 22 covers advanced injection chains — multi-step attacks that build context across turns to produce compliance the single-turn techniques from Days 4 and 16 can’t achieve.
Why AI Endpoint Authentication Fails More Often Than Application Auth
The core reason is timing. Most applications build authentication infrastructure during the initial architecture phase — it becomes part of the foundation. AI features come later, usually much later. By the time the AI chat endpoint is being built, the application already has working auth, working rate limiting, working session management. The developer building the AI feature assumes all of that infrastructure applies to new routes automatically. Sometimes it does. Often it doesn’t.
Three specific patterns produce the gap consistently. Separate router modules that don’t inherit middleware chains — the most common pattern I see. Proxy layers between the frontend and the AI backend that strip authentication headers before forwarding. And AI endpoints built directly against the third-party AI API rather than through the application’s backend, which means they have their own API key management and often no user authentication layer at all. Each of these requires a different test approach, but all three produce the same symptom: you remove the auth token and get a valid AI response.
Unauthenticated Access Testing
The test is simple. From the Day 20 endpoint inventory, take each AI endpoint. Open it in Burp Repeater. Remove the Authorization header, the session cookie, or whatever credential the normal request uses. Send the request. Check the response code and response body.
Three outcomes. A 401 or 403 response: authentication enforced, move on. A 200 response with an error message about missing credentials: authentication partially enforced — check whether a different authentication method works. A 200 response with valid AI output: unauthenticated AI endpoint confirmed. That third outcome is an immediate High to Critical finding before you’ve done anything else — an unauthenticated AI endpoint exposes whatever the AI can access to anyone on the internet.
UNAUTHENTICATED AI ENDPOINT TESTING — BURP WORKFLOWCopy
Step 1: Capture normal authenticated AI request in Burp
POST /api/ai/chat HTTP/1.1
Authorization: Bearer eyJhbGc… ← remove this entire header
Cookie: session=abc123… ← remove this entire header
Content-Type: application/json
{“message”: “Hello”}
Step 2: Send to Repeater. Remove all auth headers. Send.
Step 3: Interpret response
401/403 → auth enforced, PASS
200 + AI response → UNAUTHENTICATED ACCESS CONFIRMED — Critical finding
200 + error about auth → PARTIAL — try different credential types
Python: batch test all endpoints from Day 20 scope doc
import requests, json
endpoints = [“https://target.com/api/ai/chat”,
“https://target.com/api/summarise”,
“https://target.com/internal/ai/process”]
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