What AI Red Teaming Means
AI red teaming is system-level adversarial review. It tests prompts, retrieved content, tools, memory, permissions, and model behavior together rather than treating the model in isolation.
Independent notes on AI red teaming, prompt injection, and agent security
Learn AI Red Teaming Through Practice
AI red teaming in practice: prompt injection, tool misuse, sensitive data exposure, unsafe autonomy, and the design failures that appear when models are connected to real systems.
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What AI red teaming means in practice
For AI systems, red teaming is not only about eliciting bad model outputs. It is about understanding how prompts, retrieved content, tools, memory, and automation can be manipulated or misused together.
Why AI Systems Fail Differently
Many failures are not classic vulnerabilities in the old sense. They emerge from instruction conflicts, hidden context, unsafe tool access, misplaced trust in retrieved data, and poorly bounded autonomy.
What Good Review Looks Like
Useful AI security work combines adversarial thinking with architecture review, trust-boundary analysis, and practical defensive design so teams know what to fix and why it matters.
Learning Paths
Core areas to explore next
Use these pages as structured introductions to the parts of AI security and red teaming that show up repeatedly in real systems.
/ai-application-security
AI Application Security
How LLM features change application threat models once prompts, retrieval, tools, memory, and downstream systems are tied together.
A clearer system-level threat model for AI featuresA better sense of where to add approvals, isolation, and monitoring
Open guide
/llm-red-teaming
LLM Red Teaming
How adversarial testing is applied to LLM-backed products, including harmful outputs, prompt breakouts, and misuse paths.
Better visibility into failure modes that matter in productionFaster break-fix loops between testing and engineering
Open guide
/prompt-injection-testing
Prompt Injection
The core attack pattern in modern AI applications: malicious instructions arriving through users, retrieved content, tools, or hidden context.
A practical mental model for prompt injection beyond slogansBetter design instincts around content trust boundaries
Open guide
/prompt-engineering-review
Prompt Engineering
Instruction design and prompt structure as part of the security boundary, not just a usability exercise.
Prompts that are easier to reason aboutLower variance when inputs become messy or adversarial
Open guide
/agent-security-review
Agent Security
Security basics for systems that can plan, use tools, persist state, and take actions across multiple steps.
A more grounded model for agent-specific riskBetter boundaries around tools and action execution
Open guide
/adversarial-ml-and-model-risk
Adversarial ML and Model Risk
A compact guide to adversarial ML concepts and how they connect to modern AI product security.
Cleaner distinctions between model risk and system riskBetter alignment between AI security and traditional security controls
Open guide
Topic Coverage
Prompt engineering, prompt injection, agent security, and more
These topic hubs connect introductory guidance with current research, incident patterns, and product-facing security lessons from the broader AI ecosystem.
AI Red Teaming
Methods, case studies, and tooling for red teaming AI systems end to end.
Open topicPrompt Engineering
Prompt design patterns, instruction hierarchy, and defensive prompt construction.
Open topicPrompt Injection
Prompt injection attacks, mitigations, detection, and design patterns for safer AI applications.
Open topicAgent Security
Controls and attack paths for browsing, tool use, memory, identity, and action-taking agents.
Open topicModel Evaluation
Safety evaluations, system cards, preparedness, and security measurement for frontier models.
Open topicAdversarial ML
Adversarial machine learning attacks, taxonomies, and mitigations across the ML lifecycle.
Open topic
Featured Reading
Current material worth reading
Detecting and analyzing prompt abuse in AI tools
Microsoft Incident Response walks through how to detect prompt abuse operationally, tying prompt injection risk back to logging, telemetry, and incident response workflows.
Designing AI agents to resist prompt injection
OpenAI frames prompt injection as an evolving agent-security problem that increasingly resembles social engineering rather than a simple string-matching issue.
OpenAI to acquire Promptfoo
OpenAI announced plans to acquire Promptfoo, highlighting automated AI security testing, red teaming, and evaluation as core enterprise requirements.
MITRE ATLAS OpenClaw Investigation Discovers New and Likeliest Techniques
MITRE maps incident patterns in an open-source agentic ecosystem to ATLAS techniques, showing how AI-first systems create distinct execution paths for attackers.
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
NIST finalizes AI 100-2e2025, providing a terminology and taxonomy for adversarial machine learning across predictive and generative AI systems.
Progress from our Frontier Red Team
Anthropic shares lessons from frontier red teaming and discusses where models are showing early-warning signs of higher-risk cyber and biology capabilities.
Editorial Approach
How these notes are put together
- I favor practical application risk over abstract model capability debates
- I link to primary sources and add short notes on why they matter
- I treat prompts, tools, memory, identity, and action boundaries as one attack surface
How to use the site: start with the learning pages, use the topics to branch into specific areas, and use the research archive when you want original sources and short context notes.
Recent Additions
Recent notes and references across AI security
Patch Tuesday, April 2026 Edition
Krebs on Security covers April 2026 patching activity, including a record-sized Microsoft release and active exploitation notes.
Detecting and analyzing prompt abuse in AI tools
Microsoft Incident Response walks through how to detect prompt abuse operationally, tying prompt injection risk back to logging, telemetry, and incident response workflows.
Designing AI agents to resist prompt injection
OpenAI frames prompt injection as an evolving agent-security problem that increasingly resembles social engineering rather than a simple string-matching issue.
OpenAI to acquire Promptfoo
OpenAI announced plans to acquire Promptfoo, highlighting automated AI security testing, red teaming, and evaluation as core enterprise requirements.
MITRE ATLAS OpenClaw Investigation Discovers New and Likeliest Techniques
MITRE maps incident patterns in an open-source agentic ecosystem to ATLAS techniques, showing how AI-first systems create distinct execution paths for attackers.
Continuously hardening ChatGPT Atlas against prompt injection attacks
OpenAI describes using automated red teaming and reinforcement learning to discover agent prompt injection attacks before they appear in the wild.
Building a Production-Ready AI Security Foundation
Google Cloud outlines a defense-in-depth view of AI security spanning application controls, data protections, and infrastructure isolation.
Understanding prompt injections: a frontier security challenge
An accessible explanation of prompt injection risk in real AI products, including how third-party content can redirect or manipulate agent behavior.
Profile
Profile and contact
Focused on AI red teaming, prompt injection risk, agent security, and application-layer failures in LLM and agent systems.