AI Red Teaming
Deliberately attacking an AI system — jailbreaks, prompt injection, data leaks — to find failures before real users do.
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AI red teaming is structured adversarial testing of a model-powered product. Testers try to make it reveal secrets, ignore instructions, take unauthorised tool actions or produce harmful output. Findings become regression tests so each fix stays fixed as prompts and models change.
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What is AI Red Teaming?
AI red teaming is structured adversarial testing of a model-powered product. Testers try to make it reveal secrets, ignore instructions, take unauthorised tool actions or produce harmful output. Findings become regression tests so each fix stays fixed as prompts and models change.
What is an example of AI Red Teaming?
Before launch, a team hides instructions in a test PDF and confirms their document agent does not follow them or email data out.
Why does AI Red Teaming matter?
Deliberately attacking an AI system — jailbreaks, prompt injection, data leaks — to find failures before real users do.
What should I learn after AI Red Teaming?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
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Related concepts
The vocabulary this page depends on.
- →AI Security
AI security protects systems where the model is an untrusted decision-maker acting on untrusted input with real tool access.
- →LLM Gateway
A proxy between your app and model providers that centralises keys, routing, fallbacks, logging and spend limits.
Related workflows
Turn this into a repeatable process.
- →Red Team an AI App
A repeatable process for attacking your own AI product before launch.
- →Audit MCP Tool Security
A checklist that catches the failure modes unique to model-driven tool calls.
- →Harden an AI System Against Injection and Misuse
Architectural controls that survive a manipulated model.
- →Cut LLM Inference Costs
A step-by-step method to reduce model spend without degrading output quality.
Related tool stacks
The tools that run it in production.
- →AI Security Stack
Least-privilege tooling, approval gates and audit trails for agentic systems.
- →LLM Cost Optimization Stack
The layers that keep AI spend predictable as usage grows.
- →AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
- →Local AI Stack
Open-source tools to run chat, retrieval and agents entirely on your own hardware.
Related prompts
Reusable prompts for this job.
- →Red Team Attack Plan Prompt
Generates a tailored list of adversarial tests for your own AI feature.
- →AI System Threat Model Prompt
Produces a concrete threat model for an AI system with tool access.
- →Refactor and Security Review Prompt
Clean up a vibe-coded app and catch security gaps before launch.
- →Autonomy Boundary Prompt
Defines exactly what an agent may do alone, with approval, or never.
Related use cases
How people apply it, and what came out.
- →Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.
- →SaaS Cuts AI Bill With Routing
Illustrative example: routing easy requests to a small model reduces spend while holding quality steady.
Comparisons & alternatives
Pick between the options.
- →Model-Graded Evals vs Assertion Evals
Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.
- →Hot Wallet vs Cold Wallet
Hot wallets trade convenience for exposure; cold wallets trade friction for a signing key that never touches an internet-connected device.
- →Rabby vs OKX Web3 Wallet
Rabby is the safety-focused EVM wallet with pre-transaction simulation; OKX Web3 Wallet trades some of that depth for multi-chain breadth and built-in trading.
- →Local LLM vs Cloud API
Local models win on privacy and fixed cost; cloud APIs win on quality, scale and zero maintenance.