Documented adversarial AI red team exercises: prompt injection exploits, cultural drift testing, and persona-based safety bypasses. Each case study includes the attack vector, observed behavior, and root cause analysis.
- Demonstrated context window manipulation to override system prompts
- Tested multi-turn injection chains that bypass single-turn defenses
- Documented how role-playing prompts weaken safety classifiers
- Analyzed how models shift tone and ethical boundaries over long conversations
- Tracked incremental boundary erosion across 50+ turn conversations
- Identified patterns where models abandon safety guidelines through gradual context shifts
- Tested fictional persona framing to extract restricted outputs
- Documented effectiveness rates across different model families
- Proposed mitigation strategies for persona-based attacks
AI systems that make decisions about hiring, lending, and law enforcement must be stress-tested by adversarial thinking. Red teaming exposes failure modes before they cause real harm.
This repo also examines the disconnect between what companies predict and how they hire:
- Microsoft forecasts 68% of job skills will shift by 2030 toward AI-driven, self-directed learning
- Yet current hiring filters still require traditional degrees for AI safety roles
- Self-taught practitioners with documented red team work are filtered out before human review
The case studies here represent the kind of hands-on adversarial work that formal programs are only beginning to teach.
├── red team case studies/ # Documented attack vectors and findings
├── learning journey/ # Training progression and methodology
├── resume barriers/ # Analysis of hiring vs. skill gaps
├── call to action/ # Industry recommendations
└── 01_microsoft_predictions.md
- GPT-4 / Claude (target models for testing)
- Manual prompt engineering (no automated frameworks)
- Root cause analysis methodology for each finding
- Journey to Red Team — Training path with TextAttack, IBM ART, and Microsoft Counterfit
Joshua Penn — Oracle Certified Generative AI Professional
MIT