Lessons from Red Teaming 100 Generative AI Products

⚡ Quick Summary

This paper distills Microsoft AI Red Team’s hands-on experience from assessing more than 100 generative AI products across models, copilots, plugins, and end-to-end systems. Instead of treating AI safety as a benchmarking exercise, it reframes red teaming as a continuous, adversarial, system-level practice focused on real-world harm. The report introduces a structured threat model ontology and walks through eight concrete lessons supported by detailed case studies—from vision-model jailbreaks and automated scams to psychosocial harms and classic SSRF vulnerabilities in GenAI pipelines. A recurring theme is that many impactful failures come from simple techniques, human creativity, and system integration issues rather than exotic ML attacks. The result is a pragmatic, operations-driven guide that bridges AI safety, security engineering, and responsible AI governance.

🧩 What’s Covered

The document begins by situating AI red teaming as a response to the rapid deployment of GenAI systems across domains and modalities. It introduces Microsoft’s internal threat model ontology, built around five core elements: the system under test, the actor (adversarial or benign), tactics/techniques/procedures mapped where possible to MITRE ATT&CK and ATLAS, underlying weaknesses, and downstream impacts. This ontology is explicitly designed to cover both security incidents and responsible AI harms.

A substantial section details how Microsoft conducts red teaming operations at scale, highlighting the shift from model-only testing toward full system evaluations, including copilots, agentic workflows, external tools, and legacy infrastructure. The paper then develops eight lessons. These include starting from downstream impact rather than attack mechanics, recognizing that prompt engineering often outperforms gradient-based attacks, and rejecting the idea that red teaming can be replaced by safety benchmarks.

Several lessons emphasize scale and realism: automation frameworks like PyRIT expand coverage, but human judgment remains essential for prioritization, cultural context, subject-matter expertise, and emotional intelligence. The report also explores why responsible AI harms are pervasive yet difficult to measure, especially when triggered by benign users rather than adversaries.

Five case studies anchor the theory in practice: jailbreaking a vision-language model through image overlays, weaponizing an LLM for automated scams, evaluating chatbot responses to distressed users, probing text-to-image systems for gender bias, and uncovering a classic SSRF vulnerability in a GenAI video pipeline. The paper closes with lessons on defense-in-depth, break-fix cycles, economic realities of security, and the role of regulation.

💡 Why it matters?

This report moves AI governance discussions out of abstraction and into operational reality. It shows that many AI risks emerge at integration points—between models, users, tools, and infrastructure—precisely where compliance checklists and benchmarks tend to fall short. For organizations preparing for regimes like the EU AI Act, it offers a concrete blueprint for risk-based, continuous assessment aligned with real harms, not hypothetical ones. It also reinforces a crucial governance insight: AI safety is not something you “solve,” but something you manage over time through people, process, and iteration.

❓ What’s Missing

While rich in operational insight, the paper offers limited guidance on how findings from red teaming should be translated into formal governance artifacts such as risk registers, conformity assessments, or regulatory documentation. The connection to legal accountability frameworks is implicit rather than explicit. Readers looking for standardized metrics, thresholds, or reporting templates may find the approach intentionally open-ended.

👥 Best For

AI governance leads, security teams, responsible AI practitioners, and product owners deploying GenAI systems at scale. Particularly valuable for organizations moving beyond model evaluation toward system-level risk management and for teams designing internal red teaming or purple teaming capabilities.