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🤖 AI/LLM HACKING COURSE
FREE
Part of the AI/LLM Hacking Course — 90 Days
Day 31 of 90 · 34.4% complete
⚠️ Authorised Targets Only: LLM Data exfiltration testing — including URL callback attacks, embedding extraction, and membership inference — must be performed only on authorised systems. Membership inference testing against production models may surface real individuals’ private data as a side effect of the test; agree data handling procedures with the client before beginning.
A healthcare AI deployment I assessed had no obvious injection vulnerability. The chat interface was hardened. The system prompt was short and contained nothing sensitive. The RAG pipeline was properly sandboxed. Three hours in, I was ready to write a mostly-clean report. Then I noticed the application generated Markdown responses that included links — links it created based on topics in the user’s question. Specifically, it would generate a link to a “relevant resource” and the application would pre-fetch that link to generate a preview card. The link was constructed by the model. The model had access to the full conversation history. The conversation history included the patient record the user had pasted in to ask a clinical question.
The exfiltration path was: indirect injection in a web page the AI summarised → payload instructing the model to encode conversation history in a generated URL → application fetches the URL for preview → my server receives the HTTP request with the encoded patient record in the URL path. No system prompt extracted. No credentials leaked. But fifty lines of patient medical history, encoded in base64, arriving at my Interactsh instance in the first two minutes of exploitation. Data exfiltration through AI output channels doesn’t need a dramatic injection chain. It needs one output channel that the application trusts and one data source the model has access to. Day 31 covers the full landscape of how those two pieces combine.
🎯 What You’ll Master in Day 31
Map every output channel available to an AI deployment for exfiltration assessment
Execute URL-based exfiltration via AI-generated link preview mechanisms
Test covert data encoding in structured AI output (JSON, code, markdown)
Run membership inference probes to confirm training data inclusion
Apply training data extraction techniques to surface memorised sensitive content
Calculate exfiltration bandwidth and assess practical exploitability
⏱️ Day 31 · 3 exercises · Kali Terminal + Think Like Hacker + Kali Terminal ### ✅ Prerequisites - Day 5 — Indirect Prompt Injection — URL exfiltration is indirect injection with an outbound channel; the Day 5 methodology is the delivery mechanism - Day 18 — System Prompt Extraction — extraction techniques from Day 18 surface the data; Day 31 covers how to move that data out of the AI environment - Burp Collaborator or Interactsh account — Exercise 1 requires an outbound callback server to confirm URL exfiltration ### 📋 LLM Data Exfiltration — AI LLM Hacking Course Day 31 Contents 1. Mapping AI Output Channels for Exfiltration 2. URL-Based Exfiltration via Link Preview and Fetch 3. Covert Data Encoding in Structured Output 4. Membership Inference Attacks 5. Training Data Extraction 6. Exfiltration Bandwidth and Practical Exploitability In Day 30 you learned that system prompt extraction hitting an API key produces the highest bug bounty payouts — the LLM07 + LLM02 chain. Day 31 is the LLM02 deep dive: the full range of techniques for moving data out of an AI environment once you’ve found a way to access it. Day 32 covers AI model stealing — extracting the model’s functionality itself through systematic API probing.
Mapping AI Output Channels for Exfiltration
Every application that wraps an LLM has at least one output channel. Most have several. The text response is obvious. Less obvious: generated URLs that the application fetches, code that gets executed server-side, structured JSON that feeds downstream systems, embeddings returned in API responses, and webhook payloads the AI generates based on user requests. Each channel has a different exfiltration capacity — how many bytes can be encoded per interaction — and a different detection profile. URL-based exfiltration creates HTTP requests that may appear in server logs. Code execution exfiltration may create process logs. Text-based covert encoding leaves no network trace at all.
The first ten minutes of a data exfiltration assessment map every output channel before testing any of them. The question for each channel: does the application automatically process AI output in this channel? Auto-processing — auto-fetching URLs, auto-executing code, auto-posting to webhooks — is what turns a channel from theoretical to practical. A URL in a text response that a human has to click is high-friction. A URL in a text response that the application fetches server-side for a preview card is zero-friction and logs the request your server.
URL-Based Exfiltration via Link Preview and Fetch
Link preview mechanisms are the most commonly overlooked AI exfiltration surface. Many chat applications generate a preview card when a URL appears in a message — a thumbnail, title, and description fetched from the URL. When the AI generates the URL, the preview fetch is triggered automatically, server-side, before the user even sees the message. That server-side fetch is an outbound HTTP request to an attacker-controlled server, and the URL path is under the AI’s control.
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