adversarial-self-test

Test AI agent outputs against OWASP Top 10 vulnerabilities.

15|5|Updated May 17, 2026

Search Tags:#owasp #llm #security-audit #prompt-injection #adversarial-testing #agentic-ai

Ready to run

npx skills add https://github.com/cropsgg/skills --skill adversarial-self-test

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: adversarial-self-test
Source: https://github.com/cropsgg/skills/tree/main/skills/engineering/adversarial-self-test
Command: npx skills add https://github.com/cropsgg/skills --skill adversarial-self-test

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Identifies and mitigates risk by adversarially testing the agent's own outputs against OWASP Top 10 for LLMs and Agentic AI.

Core Features & Use Cases

Adversarial self-testing against prompt injection, insecure output handling, data exfiltration, and tool invocation risks.
Structured evaluation with defined risk categories and actionable remediation notes.
Reproducible security testing workflow for AI agents, enabling audit-ready findings.

Quick Start

Run adversarial self-test on the latest agent output to identify prompt injections and exfiltration paths.

Frequently Asked Questions about adversarial-self-test

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

FAQPage Schema

How do I test AI agent outputs for OWASP LLM vulnerabilities?▼

To test AI agent outputs for OWASP LLM vulnerabilities, run an adversarial self-test that evaluates code generation and tool invocation tasks against the Top 10 risks, producing structured findings and remediation guidance.

What is adversarial testing for prompt injection in agentic AI?▼

Adversarial testing for prompt injection is a security audit mechanism that identifies and mitigates malicious inputs attempting to hijack agentic AI outputs, tracing potential exfiltration paths and enforcing structured risk mitigation.

How do I check for insecure output handling in LLM code generation?▼

Check for insecure output handling in LLM code generation by running an adversarial self-test to assess tool invocation risks and command execution vulnerabilities, yielding actionable remediation notes for audit readiness.

When should I run a security audit on my AI agent's tool invocations?▼

Run a security audit on AI agent tool invocations after generating outputs that touch external systems or execute commands, ensuring data exfiltration tracing and prompt injection testing are completed before production deployment.

Can I trace data exfiltration paths in production AI agent runs?▼

Yes, you can trace data exfiltration paths in production AI agent runs by applying a reproducible security testing workflow that evaluates data handling tasks against defined OWASP risk categories to generate audit-ready findings.