pentest-ai-redteam

Identify and document LLM prompt-injection risks and tool-call abuse in AI systems.

Updated Mar 5, 2026
One-click install
npx skills add https://github.com/Xerrion/opencode --skill pentest-ai-redteam
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pentest-ai-redteam
Source: https://github.com/Xerrion/opencode/tree/main/skills/pentest-ai-redteam
Command: npx skills add https://github.com/Xerrion/opencode --skill pentest-ai-redteam

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI/LLM red-team testing frames and standardizes evaluation of prompt-injection vulnerabilities, tool-call abuse, and resilience against adversarial techniques.

Core Features & Use Cases

  • Comprehensive taxonomy covering direct and indirect prompt injections, jailbreaking, role confusion, instruction override, system-prompt extraction, and defense-aligned probe patterns.
  • Attack library and probes providing reproducible test scenarios, evidence workflows, and mappings to OWASP LLM Top 10 and MITRE ATLAS for AI-security assessments.
  • Structured evaluation with deterministic transcripts, canaries, and secure logging to the .pentest/evidence directory.

Quick Start

Kick off a red-team engagement by selecting an AI system and following the playbook to enumerate injection vectors and produce reproducible evidence.

Frequently Asked Questions about pentest-ai-redteam

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

FAQPage Schema
How do I test LLM prompt injection vulnerabilities in my AI agent?▼

Identify LLM prompt-injection risks and tool-call abuse by applying adversarial probes and mapping test results to the OWASP LLM Top 10 and MITRE ATLAS frameworks for structured security assessments.

How do I evaluate RAG systems for indirect prompt injection risks?▼

Evaluate RAG system resilience against indirect prompt injection by running standardized attack probes and recording deterministic transcripts with canaries to document instruction override and role confusion vulnerabilities.

Can I use this for ML classifier red-teaming and sandboxed environments?▼

Yes, you can assess ML classifiers and sandboxed environments by applying adversarial probe patterns and tool-call abuse scenarios to evaluate system resilience and map findings to MITRE ATLAS.

What is the best way to document AI red-team evidence and transcripts?▼

Document AI red-team evidence by imposing deterministic transcripts, canaries, and secure logging to the .pentest/evidence directory to ensure reproducible test scenarios and standardized vulnerability tracking.

Does AI red-team testing cover jailbreaking and system-prompt extraction?▼

Yes, AI red-team testing covers jailbreaking and system-prompt extraction by utilizing a comprehensive attack library that targets direct and indirect prompt injections, instruction overrides, and role confusion.