AI Security in Practice: Protecting Your AI-Powered Applications - Olivia Liddell
Summary
This talk provides a deep dive into securing AI-powered applications by addressing unique vulnerabilities that traditional security measures often miss. Using a fictional online retailer (ABC Company) as a case study, the presentation outlines three primary AI security risks—Prompt Injection, Data Poisoning, and Improper Output Handling. Mitigation requires implementing layered defenses across the entire application workflow: input validation, model protection, output sanitization, and continuous monitoring.
Key takeaways
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AI Attacks are Subtle and Invisible
17:32
Unlike traditional attacks (e.g., DDoS), AI vulnerabilities can be slow, subtle, and partially invisible to standard firewalls or logging systems, requiring a shift in defensive thinking.
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Understand the Three Core Risks
17:32
The three major risks are: Prompt Injection (tricking the model with disguised instructions), Data Poisoning (manipulating training data over time), and Improper Output Handling (trusting model output without validation).
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Implement Layered Defenses
45:00
Security must be applied at four stages: Input Validation (sanitizing user input before it reaches the model), Model Protection (limiting scope and permissions), Output Sanitization (treating all AI-generated content as untrusted), and Monitoring/Operations (tracking model behavior changes).
Technical details
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Prompt Injection Attack Vector
1245s
An attacker disguises instructions within normal user input fields (e.g., 'I prefer blue clothes' followed by 'Ignore all previous instructions...'). The model treats the malicious instruction as valid text, potentially allowing unauthorized actions like updating PII or generating coupon codes.
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Data Poisoning
1800s
This attack occurs over an extended period when attackers generate fake browsing sessions (e.g., associating clothing searches with power tools) to subtly retrain the model and corrupt its core logic, making all clicks appear legitimate.
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Improper Output Handling
2300s
This vulnerability occurs when an application trusts the output of the AI model without any validation or guardrails. Even if the model is manipulated (e.g., recommending power tools to a clothing shopper), the application fails because it assumes the content is safe and accurate.
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Model Scope and Permissions
3150s
Models must be secured by strictly limiting their operational scope and permissions, ensuring they cannot perform unauthorized actions (e.g., database updates) even if manipulated.
Mentioned resources
- OAS Generative AI Security Project
- Oliviaell.com
Channel & topics
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