Case Studies
Real-world AI security incidents, domain-specific case studies, incident analysis reports, and platform security evaluations that ground theoretical knowledge in practical experience.
Theory without practice is incomplete, and practice without studying real failures is reckless. This section bridges the gap between the attack techniques covered elsewhere in the curriculum and the messy reality of AI security in production. Every case study here is drawn from actual incidents, published research, or documented platform evaluations. They illustrate not just what went wrong, but why it went wrong and what the defenders could have done differently.
Studying real incidents is essential for developing mature red team judgment. Textbook attack techniques work in controlled environments, but production systems present unexpected complications: layered defenses that partially mitigate attacks, business logic that creates novel attack surfaces, and organizational responses that range from exemplary to catastrophic. Understanding these dynamics is what separates a vulnerability scanner from a skilled red team operator.
Why Case Studies Matter for Red Teaming
The AI security landscape evolves rapidly. New model architectures, deployment patterns, and integration frameworks create attack surfaces that did not exist months ago. Case studies serve as the field's institutional memory -- they capture hard-won lessons that might otherwise be lost as teams move on to new projects.
For red teamers specifically, case studies provide three critical benefits. First, they calibrate expectations about what real-world impact looks like. The difference between a theoretical vulnerability and a production incident often comes down to environmental factors that only become visible in case study analysis. Second, they reveal common failure patterns that recur across organizations and platforms. Once you recognize these patterns, you can test for them proactively rather than discovering them reactively. Third, they provide concrete evidence for the recommendations in your reports -- citing a relevant incident makes your findings more credible and actionable for stakeholders.
Incident Categories
The case studies in this section span several categories, each offering different lessons for AI security practitioners.
Documented incidents cover publicly reported AI security failures, from the ChatGPT data leak caused by a Redis caching bug to the Bing Sydney jailbreak that revealed how easily conversational AI could be manipulated into producing harmful outputs. These incidents demonstrate that AI security failures often originate in traditional infrastructure rather than model-level vulnerabilities, and that the organizational response matters as much as the technical fix.
Incident analysis provides deep-dive forensic examinations of specific events, including the Air Canada chatbot case that resulted in legal liability, the Samsung code leak through ChatGPT, and the DPD chatbot incident. Each analysis follows a structured format: timeline reconstruction, root cause identification, impact assessment, and lessons learned.
Domain-specific studies examine how AI security challenges manifest differently across verticals. Healthcare AI systems face unique constraints around patient data and clinical decision support. Financial AI must contend with regulatory requirements and adversarial market manipulation. Customer service deployments handle sensitive account information while operating under intense time pressure. Each domain creates a distinct threat landscape that generic security approaches fail to address adequately.
Platform evaluations assess the security posture of major AI deployment platforms and integration frameworks, including cloud ML services, LangChain and LlamaIndex ecosystems, API provider security models, and plugin/extension architectures.
What You'll Learn in This Section
- Incidents & Reports -- Documented red team reports, bug bounty findings, and lessons learned from production AI security incidents
- Incident Analysis -- Deep forensic analysis of major AI security events including the ChatGPT data leak, Bing Sydney jailbreak, Air Canada chatbot ruling, Samsung code leak, and DPD chatbot failure
- Domain-Specific Security -- AI security challenges in healthcare, finance, customer service, autonomous vehicles, education, content moderation, facial recognition, deepfakes, and hiring AI bias
- Integration & Platform Security -- Security evaluations of cloud ML platforms, LangChain/LlamaIndex frameworks, API provider architectures, and plugin/extension ecosystems
Prerequisites
Case studies are accessible to readers at all experience levels, but you will gain more from them with:
- Foundational knowledge of AI attack techniques from the Foundations section
- Familiarity with prompt injection concepts from the Prompt Injection section
- Basic understanding of AI system architecture from AI System Architecture