Red Team an AI App
A repeatable process for attacking your own AI product before launch.
/ quick answer
List what the system can access, attack each capability systematically, and turn every finding into an automated test. A repeatable process for attacking your own AI product before launch.
- 01Inventory data, tools and actions the AI can reach.
- 02Write attacker goals: exfiltrate data, trigger an action, bypass a policy.
- 03Test direct jailbreaks and indirect injection via documents, web pages and emails.
- 04Check tool calls require permission for anything destructive or external.
- 05Record each successful attack as a regression test.
- 06Fix with least-privilege tools and output checks, not only prompt wording.
- 07Re-run the suite on every model or prompt change.
/ frequently asked
What does the Red Team an AI App workflow do?
List what the system can access, attack each capability systematically, and turn every finding into an automated test.
What problem does Red Team an AI App solve?
AI apps fail in ways normal QA misses: injected instructions, leaked system prompts, unsafe tool calls.
How many steps does Red Team an AI App take?
7 steps. It starts with inventory data, tools and actions the ai can reach. and ends with re-run the suite on every model or prompt change..
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →AI Red Teaming
Deliberately attacking an AI system — jailbreaks, prompt injection, data leaks — to find failures before real users do.
- →Prompt Injection
An attack where hostile input hijacks the LLM's instructions, causing it to leak data or misbehave.
- →Guardrails
Runtime checks that constrain LLM inputs and outputs to keep behavior safe and on-spec.
- →AI Security
AI security protects systems where the model is an untrusted decision-maker acting on untrusted input with real tool access.
Related workflows
Turn this into a repeatable process.
- →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.
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.
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.