World Model Readiness The attack surface you just installed

Module · The attack surface you just installed

The AI Security & Misuse Check

Every AI feature you ship is a new attack surface, and most of it is invisible to a traditional security review. Models follow instructions hidden in the data they read, leak through channels nobody mapped, and hand attackers a friendly interface to your systems. This module checks the five pieces that matter: prompt injection, data leakage, model access control, adversarial testing, and data-loss controls on the AI tools your staff already use.

Question 1 of 5 · Prompt injection tested

Have you tested whether untrusted input can hijack your AI features?

Any model that reads external content, emails, documents, web pages, tickets, can be instructed by that content. Prompt injection turns your helpful assistant into someone else's tool. If you have never tried to do it yourself, assume it already works.

Question 2 of 5 · Leakage paths mapped

Do you know every path by which an AI feature could expose data it should not?

LLM features leak in quiet ways: a chatbot answering about another customer's records, a summariser pulling in documents beyond the user's permissions, logs capturing prompts full of secrets. Map the paths yourself, or an outsider will map them for you.

Question 3 of 5 · Model access controlled

Is access to your models and their keys governed like any other production credential?

API keys pasted into shared chat channels, one service account behind every feature, no rate limits, no record of who called what. Model access is often the least-governed credential in the building and among the most expensive to abuse.

Question 4 of 5 · Someone red-teams it

Does anyone deliberately try to break your AI systems before attackers do?

Standard penetration testing does not cover jailbreaks, prompt injection or model misuse; it was written for a different threat. If nobody is adversarially probing your AI features, your first red team will be a real one, and it will not send you a report.

Question 5 of 5 · DLP on AI endpoints

Can you detect and stop sensitive data flowing into the AI tools your staff use?

Employee-facing AI is an exfiltration channel with a friendly face. Without data-loss controls on those endpoints, source code, customer lists and contract drafts leave silently, one paste at a time, and you learn about it in the breach notification.

For the statistics · one click each

Three questions for the public picture

These do not affect your score. They feed the anonymised, aggregated statistics; groups under 8 respondents are never shown.

Has your company ever adversarially tested an AI system?

Never
Planned
Once
Regularly
No AI in production

Have you had a security incident involving an AI feature?

Not that we know of
A near-miss, caught in time
Yes, minor
Yes, serious
We could not tell

Do you have data-loss controls on the AI tools employees use?

None
Policy only
Partial coverage
Full coverage
We do not know

Your context

Used to calibrate the report. Company size and sector remain in the anonymized dataset; your email does not.