security-review-owasp-secure-ai-model-ops

Review Secure AI Model Ops systems for prompt injection and context leakage.

Updated Mar 26, 2026

Search Tags:#security review #prompt injection #trust boundaries #tool abuse #secure ai #model ops

Ready to run

npx skills add https://github.com/sjinks/ai-owasp-skillset --skill security-review-owasp-secure-ai-model-ops

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: security-review-owasp-secure-ai-model-ops
Source: https://github.com/sjinks/ai-owasp-skillset/tree/main/.github/skills/security-review-owasp-secure-ai-model-ops
Command: npx skills add https://github.com/sjinks/ai-owasp-skillset --skill security-review-owasp-secure-ai-model-ops

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps reviewers find security weaknesses in AI model operations, especially where prompts, tools, retrieval, memory, or external services can be abused to cause unsafe behavior or leak sensitive context.

Core Features & Use Cases

Trust Boundary Analysis: Traces how untrusted input can influence model instructions, tool calls, and downstream actions.
Abuse Path Review: Focuses on prompt injection, unsafe automation, secret exposure, and cross-boundary data leakage.
Operational Security Checks: Reviews logging, monitoring, governance, and runtime controls for AI-driven workflows.
Use Case: A reviewer can use this Skill to assess an AI agent that reads documents and calls tools, then verify whether user content can override policy or trigger privileged actions.

Quick Start

Use the security-review-owasp-secure-ai-model-ops skill to review the AI agent flow for prompt injection, unsafe tool execution, and context leakage.

Frequently Asked Questions about security-review-owasp-secure-ai-model-ops

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I review AI agents for prompt injection and unsafe tool use?▼

To review AI agents for prompt injection and unsafe tool use, trace untrusted input paths across trust boundaries to verify whether user content can override model instructions or trigger privileged downstream actions.

What is trust boundary analysis in secure AI model ops?▼

Trust boundary analysis in secure AI model ops traces how untrusted input influences model instructions, tool calls, and downstream actions to identify context leakage and excessive agent autonomy.

How do I prevent context leakage in AI agent retrieval and memory workflows?▼

Prevent context leakage in AI agent retrieval and memory workflows by reviewing code, configuration, and architecture to validate output execution paths and separate confirmed weaknesses from review gaps.

Can I assess excessive agent autonomy and runtime controls in AI deployment configurations?▼

Yes, you can assess excessive agent autonomy and runtime controls by reviewing logging, monitoring, governance, and deployment paths to ensure operational security checks validate unsafe automation.

What is the best way to perform an evidence-based security review of AI plugin architectures?▼

The best way to perform an evidence-based security review of AI plugin architectures is to apply explicit trust-boundary tracing and validate output execution paths across agent, prompt, and plugin paths.