AI Security

Prompt Injection Review Checklist for Internal AI Assistants

Published 2026-06-19 by AISecAll Editorial

TL;DR: Use this concise 7‑step checklist to map entry points, define safe prompt patterns, add validation, run adversarial tests, log suspicious activity, and embed review cycles into your development workflow. It lets a small team spot prompt injection risks before they reach production.

Why prompt injection matters for internal AI assistants

Internal assistants often handle confidential data, schedule tasks, or trigger downstream automation. If an attacker can inject malicious instructions via a user‑supplied prompt, the model may execute unintended commands, expose data, or manipulate other services. Unlike classic code injection, prompt injection exploits the model’s instruction‑following behavior, making it harder to detect with traditional static analysis.

Prompt Injection Review Checklist

1. Map every user‑controlled input surface

• Identify all UI fields, chat messages, email parsers, or API endpoints that feed directly into the model.
• Document the data flow in a simple diagram (e.g., user → API gateway → prompt template → model).
• Mark inputs that are concatenated with system instructions or few‑shot examples.

2. Define a safe‑prompt template

• Separate system instructions from user content using clear delimiters (e.g., ###SYSTEM### and ###USER###).
• Never place user content before the system instruction; keep it at the end.
• Whitelist allowed commands or intents and reject anything outside the list.

3. Add input validation and sanitisation

• Strip or escape characters that can break delimiters (newlines, triple quotes, markdown code fences).
• Apply length limits (e.g., 500 characters) to reduce attack surface.
• Use a simple regex to reject known injection patterns such as \b(\$\{.*\}|<\?php|SELECT\s+\*|DROP\s+TABLE)\b.

4. Implement a prompt‑injection guardrail

• Prepend a short verification step: ask the model to repeat the intended action in its own words and compare to an allowlist.
• Example guardrail prompt: "Only respond with JSON describing the action you will take. If the request is ambiguous, reply with \"reject\"."

5. Run adversarial test cases

• Generate a list of common injection tricks (e.g., "Ignore previous instructions and ...", "Pretend you are a Linux shell").
• Automate a test harness that feeds each trick into the assistant and asserts that the guardrail rejects it.
• Record results in a test report and fix any false negatives.

6. Log and monitor suspicious prompts

• Log raw user input, the final assembled prompt, and the model’s decision (accept/reject).
• Tag logs with a severity level; route high‑severity events to a Slack channel or SIEM.
• Set a retention policy of 30 days for prompt logs, then purge to protect privacy.

7. Embed the checklist in your CI/CD pipeline

• Fail builds if new user‑controlled inputs are added without corresponding validation.
• Run the adversarial test suite on every pull request.
• Schedule a quarterly review to update the whitelist and guardrail logic.

Integrating the checklist into a small‑team workflow

Start with a single “prompt‑review” ticket for each new assistant feature. Assign a security champion to verify steps 1‑4, then hand off to QA for step 5. Use a shared spreadsheet or lightweight wiki to track checklist completion. The overhead is minimal—most steps are one‑line code changes or configuration updates.

Maintaining the checklist over time

Prompt injection techniques evolve as models get better at following instructions. Keep an eye on community resources such as the OWASP GenAI Security Project and update your test cases quarterly. If you adopt a new model provider, repeat steps 1‑3 to account for differences in prompt handling.

By treating prompt‑injection review as a repeatable checklist rather than an ad‑hoc audit, small companies can protect internal assistants without needing a dedicated security team.

Need help formalising this process or integrating guardrails into your existing stack? AISecAll offers a quick‑start audit service tailored for startups.