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. The work covers direct prompts, indirect content from documents or websites, retrieval poisoning patterns and tool-use boundaries.
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. From there, focused test cases probe likely abuse paths and produce concrete reproduction steps for engineering teams.
The deliverable is a prioritized remediation plan with examples, affected flows and retest criteria. The goal is to reduce practical risk without slowing down useful product development.
Good prompt injection testing also separates model weirdness from control failure. The important question is not whether a model can be tricked in chat, but 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 private AI systems, local model workflows and security-sensitive implementation decisions. The material is written for operators, founders and engineering leads who need plain technical context before they choose vendors, share data or connect AI features to internal tools.
Each engagement is evaluated against the same practical questions: what information must stay private, which users need access, how answers will be checked, what logs are created, what tools the model can use and how the team will verify that the deployed workflow keeps working after handoff.
The emphasis is useful delivery with clear boundaries, tested assumptions, readable documentation and decisions that a technical owner can maintain after launch.