How would you red-team a generative AI application?

Builds system-specific adversarial testing around realistic assets, identities, channels, tools, and measurable impact.

1Say itAnswer out loud first

2CompareCompare with Core answer

3RefineRefine with Stronger and Advanced

TL;DR

I define the assets, attacker roles, allowed test environment, and impact criteria before generating attacks. Tests cover direct and indirect injection, sensitive disclosure, unsafe output handling, poisoning, excessive agency, tenant boundaries, resource abuse, and misleading behavior relevant to the product. Side effects use isolated accounts, fake destinations, or reversible operations.

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Practice focus

AI red teaming, adversarial evaluation, scope, safe execution, findings, and regression coverage.

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Answer path

Core answer first, then checkpoints toward a stronger answer.

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Core answer
Start here. This is the direct spoken answer to practice first.
Overview
AI red teaming tests the deployed system and its authority, not just whether a base model says something undesirable.
I define the assets, attacker roles, allowed test environment, and impact criteria before generating attacks. Tests cover direct and indirect injection, sensitive disclosure, unsafe output handling, poisoning, excessive agency, tenant boundaries, resource abuse, and misleading behavior relevant to the product. Side effects use isolated accounts, fake destinations, or reversible operations.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
The corpus combines known attack patterns with system-specific paths through files, retrieval, memory, tools, multimodal input, long conversations, and compromised integrations. Automated variations provide breadth, while skilled human testers explore novel chains and business logic. Findings include the complete trajectory, affected versions, preconditions, impact, and a reproducible safe case rather than a screenshot of surprising prose.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Testing only the public chatbot text box.
Calling any policy refusal bypass a critical breach.
Fixing findings without adding regression cases.

How would you red-team a generative AI application?

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Answer path

Overview

Review signals

Follow-up prompts

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