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Part of the AI/LLM Hacking Course — 90 Days
AI Privacy Attacks – Day 37 of 90 · 41.1% complete
⚠️ Authorised Targets Only: AI privacy attacks testing may surface real personal data as a side effect of demonstrating the vulnerability. Agree explicit data handling procedures — what you’re authorised to access, how long you can retain it, and how it must be destroyed — before starting any privacy assessment.
Let me start with a situation I’ve seen play out in AI security assessments, because it changes the way you should think about AI privacy attacks.
An AI customer-service system had been fine-tuned on two years of real support tickets. Everything looked normal after deployment. No obvious security breach. No dramatic exploit. Then, months later, a researcher started asking the model very specific questions — and the model began reproducing pieces of old customer conversations almost word for word.
Names. Email addresses. Complaint details. Information customers had shared with the company assuming it would stay confidential.
When I looked at cases like this, the uncomfortable part was usually not the model itself. It was the data behind it. The fine-tuning dataset had never been properly scrubbed for PII. The model had effectively memorised parts of that data, and carefully constructed prompts could sometimes pull those fragments back out.
I’ve had legal and security teams ask me a deceptively simple question in situations like this: “Is this a security vulnerability, or is it a compliance problem?”
My answer is: it’s both.
And that distinction becomes increasingly meaningless once personal data is inside an AI system. You don’t necessarily need an attacker chaining together five sophisticated vulnerabilities. If an AI system exposes one customer’s personal information to another person without authorisation, you already have a privacy problem — and potentially a security incident.
That’s what I want you to understand in Day 37. When I assess an AI system, I don’t treat privacy as a separate checkbox that belongs to the compliance team. I look at it as part of the attack surface.
I’ll show you how I approach PII extraction, cross-session data leakage, model-output re-identification, and the privacy risks created by training and fine-tuning data. Because with modern LLM systems, the question isn’t just whether someone can break into the model. It’s whether the model can reveal something it was never supposed to reveal.
🎯 What You’ll Master in Day 37
Map all PII flows into an AI deployment as privacy attack surfaces
Test cross-session context isolation in multi-tenant AI deployments
Extract PII from model training data using Day 31 techniques with privacy focus
Test re-identification via aggregate model outputs
Assess data retention and right-to-erasure compliance gaps in AI systems
Map privacy findings to GDPR, CCPA, and HIPAA provisions for regulatory impact
⏱️ Day 37 · 3 exercises · Kali Terminal + Think Like Hacker + Kali Terminal ### ✅ Prerequisites - Day 31 — LLM Data Exfiltration — membership inference and training data extraction from Day 31 are the core techniques for the PII extraction phase; Day 37 applies them with privacy-specific focus and regulatory mapping - Day 6 — LLM02 Sensitive Information Disclosure — the OWASP overview from Day 6 is the conceptual foundation for Day 37’s comprehensive privacy methodology - Basic familiarity with GDPR Article structure — Exercise 3 maps findings to specific Articles ### 📋 AI Privacy Attacks — Day 37 Contents 1. Mapping PII Flows as Attack Surfaces 2. Cross-Session Context Isolation Testing 3. PII Extraction from Training Data 4. Re-Identification via Model Outputs 5. Data Retention and Erasure Compliance Gaps 6. Regulatory Impact Mapping In Day 36 you assessed the trust boundaries between agents. Day 37 assesses the privacy boundaries that every AI deployment processes personal data under. Day 38 covers fine-tuning security — the vulnerabilities that emerge specifically during the fine-tuning process, including dataset poisoning and the security properties of models trained on private data.
Mapping PII Flows as Attack Surfaces
When I start a privacy assessment of an AI system, I don’t begin by throwing prompts at the model. I first want to know where personal data can enter, where it travels, where it gets stored, and where it can potentially come back out. That gives me a PII flow map — essentially an attack-surface diagram for personal data.
The important distinction is that I’m not creating this map just for documentation. I’m using it to identify where PII can cross a trust boundary. Every time personal information moves from one component to another, I ask what controls are supposed to protect it and what happens if those controls fail.
For every PII flow, I document four things: what data is involved, where it enters, where it persists, and who could potentially retrieve it. For example, a customer’s name and email address might enter through a support conversation, be copied into conversation history, become part of a retrieval index, appear in application logs, and potentially influence a fine-tuned model. Those are not five versions of the same risk. They are five different places where the data needs to be assessed.
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