Prompt Injection Testing for AI Products
Prompt injection testing evaluates whether untrusted text can steer an AI system away from the user's intent, leak data, override policy or trigger unsafe tool behavior.
Testing starts by mapping the system boundary: where instructions come from, what data is retrieved, which tools can change state, what confirmations exist and how outputs are logged.
The deliverable is a prioritized remediation plan with examples, affected flows and retest criteria. The goal is to reduce practical risk without slowing useful product development.
Good prompt injection testing separates model weirdness from control failure. The important question is whether the system can be pushed into exposing data, taking action, storing instructions or giving untrusted content more authority than it should have.
This page is maintained by Jonathan R Reed for teams evaluating AI enablement, private workflows, existing-tool optimization and security-sensitive implementation decisions.
Each engagement is evaluated against practical questions: which tools and subscriptions already exist, what information must stay private, which users need access, how answers will be checked, what the workflow costs and how the team will verify that the deployed system keeps working after handoff.
The emphasis is useful delivery with clear boundaries, tested assumptions, cost-aware model routing, readable documentation and decisions that a technical owner can maintain after launch.