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vignesh2027
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ai-red-teamer

Probe AI system safety vulnerabilities through adversarial testing workflows.

6|Updated May 20, 2026
Search Tags:#risk-assessment#guardrails#red-team#prompt-injection#llm-safety#adversarial-testing
One-click install
npx skills add https://github.com/vignesh2027/Claude-Agentic-Skills2.0-version --skill ai-red-teamer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-red-teamer
Source: https://github.com/vignesh2027/Claude-Agentic-Skills2.0-version/tree/main/ai-red-teamer
Command: npx skills add https://github.com/vignesh2027/Claude-Agentic-Skills2.0-version --skill ai-red-teamer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Adversarial testing for AI systems to reveal safety gaps, robustness issues, and alignment failures before they hit production.

Core Features & Use Cases

  • Sub-Agents — AIRedTeamer organizes testing into specialized roles: PromptAttacker, SafetyEvaluator, RobustnessProber, GuardrailAuditor, and ReportWriter.
  • Attack Taxonomy — Comprehensive categories (Direct/Indirect prompt injection, training data exposure, denial-of-service, etc.) to structure red-team assessments.
  • Red-Team Workflow — Four phases (Baseline, Boundary Testing, Adversarial, Policy Stress) with reproducible steps and severity scoring.
  • Use case: Validate prompts in high-stakes domains (finance, healthcare) to ensure guardrails hold under adversarial inputs.

Quick Start

Run AIRedTeamer to systematically test prompts and document safety failures.

Frequently Asked Questions about ai-red-teamer

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

FAQPage Schema
How do I run adversarial testing to find prompt injection vulnerabilities in LLMs?▼

You can validate LLM guardrails by running structured red-team workflows that test boundary conditions and policy stress. This Skill applies specialized roles like GuardrailAuditor to systematically probe and score safety failures under adversarial inputs.

What is the best way to automate red-team assessments for production LLM deployments?▼

Red-teaming high-stakes AI involves stress-testing prompts in domains like finance or healthcare to ensure robustness against adversarial inputs. This Skill provides a dedicated workflow with severity ratings and mitigation recommendations to validate guardrails.

How does an AI red teamer evaluate guardrails and robustness against indirect prompt injection?▼

Mitigation recommendations are generated based on severity ratings assigned during the adversarial and policy stress testing phases. This Skill provides a structured report documenting safety failures and robustness issues found during the red-team assessment.

Do I need any specific frameworks or dependencies to perform LLM safety testing?▼

Beyond automated adversarial testing, you need to apply the mitigation recommendations to your prompt design and guardrail configurations. This Skill outputs structured reports that guide the manual patching of identified safety vulnerabilities.

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