Homework: Real-World Attack Vectors
Objective: Apply your understanding of real-world attack vectors by conducting a threat analysis of actual AI-powered products. This exercise develops the skill of thinking like an attacker — a critical capability for building secure AI systems.
Task 1: Choose 3 Real-World AI Products
Select 3 real AI-powered products from different categories. You may choose from this list or select your own:
| Category | Example Products |
|---|---|
| AI Search | Perplexity, Google AI Overviews, Bing Copilot, You.com |
| Code Assistants | GitHub Copilot, Cursor, Codeium, Amazon CodeWhisperer |
| Email/Productivity | Microsoft Copilot (Office), Google Gemini (Workspace), Notion AI |
| Customer Support | Intercom Fin, Zendesk AI, Ada, Drift |
| Writing/Content | Jasper, Copy.ai, Grammarly AI, Writesonic |
| Agents/Assistants | ChatGPT with plugins, Claude with tools, Custom GPTs |
| Security | AI-powered SIEM tools, code scanning tools |
For each product, briefly describe:
- What the product does
- What data it processes (user input, documents, web content, code, etc.)
- What capabilities/tools it has (sending messages, web access, code execution, file access, etc.)
Task 2: Attack Surface Analysis
For each of your 3 products, create an attack surface map using this format:
Product: [Name]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
DATA INPUT CHANNELS:
1. [Channel name] — [description, trust level]
2. [Channel name] — [description, trust level]
...
INJECTION POINTS:
Direct:
- [How a user could directly inject]
Indirect:
- [How an attacker could inject via data sources]
- [How an attacker could inject via third-party integrations]
AVAILABLE CAPABILITIES:
- [Capability 1] → Exploitation potential: [low/medium/high]
- [Capability 2] → Exploitation potential: [low/medium/high]
...
POTENTIAL IMPACTS:
- Data exfiltration: [what data could be stolen]
- Misinformation: [what could be manipulated]
- Unauthorized actions: [what actions could be triggered]
- Reputation/legal: [what liability could result]
Be specific. Do not write generic descriptions — reference the actual features and integrations of each product.
Task 3: Threat Model for the Most Critical Vector
For each of your 3 products, identify the single most critical attack vector and write a focused threat model (~200 words each). Your threat model should address:
- Attack description: What the attacker does, step by step
- Prerequisites: What the attacker needs (access, knowledge, resources)
- Affected users: Who is impacted and at what scale
- Impact severity: What is the worst-case outcome
- Detection difficulty: How likely is the attack to be noticed
- Existing mitigations: What defenses (if any) the product likely has
- Recommended defenses: What additional defenses you would implement
Example format:
Product: Perplexity AI
Most Critical Vector: Web Content Poisoning
THREAT MODEL:
An attacker creates web pages with hidden text (CSS-hidden divs,
zero-size font) containing instructions for AI search engines.
When Perplexity retrieves and summarizes these pages, the hidden
instructions influence the generated summary...
Prerequisites: Basic web development skills, a public website
Affected users: Any user who searches for topics covered by the
poisoned pages (potentially millions)
Impact severity: HIGH — misinformation presented as authoritative
AI-generated summaries, potential for financial fraud (fake product
recommendations), reputational damage to referenced businesses
Detection difficulty: LOW — users rarely compare AI summaries
against source pages; hidden text is invisible in browsers
Existing mitigations: Likely some content sanitization, but no
public documentation of specific anti-injection measures
Recommended defenses: Strip hidden/invisible text from retrieved
pages, cross-reference claims across multiple sources, flag
discrepancies between visible and hidden content...
Task 4: Cross-Product Comparison
After completing your analysis, write a brief comparison (150-200 words) addressing:
- Which product has the largest attack surface? Why?
- Which product has the most dangerous potential impact? Why?
- Are there common vulnerability patterns across all three products?
- Which product category (search, email, code, etc.) do you think is most urgently in need of better prompt injection defenses?
Self-Check Criteria
Verify your work against these criteria:
- You selected 3 products from different categories (not 3 products from the same category)
- Each attack surface map lists specific data input channels for that product (not generic descriptions)
- Each map includes both direct and indirect injection paths
- Your threat models are ~200 words each and address all 7 required elements
- Your threat models identify realistic attack chains, not vague "bad things could happen" descriptions
- Your comparison provides justified rankings with specific reasoning
- You identified at least one novel or non-obvious attack vector that was not directly covered in the lesson
Bonus challenge: For one of your three products, research whether any prompt injection attacks have been publicly disclosed or demonstrated. Include a reference to the disclosure (researcher name, date, description) in your analysis. Check security researcher blogs (e.g., Johann Rehberger's "Embracing Red" blog, Simon Willison's blog) and security conference presentations.