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adversarial-examples

Generate categorized adversarial inputs to stress-test LLM robustness.

3|Updated Nov 18, 2025
Search Tags:#edge-cases#adversarial#LLM-testing#safety-testing#mutator#input-mutations#prompt-robustness
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-red-teaming --skill adversarial-examples
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: adversarial-examples
Source: https://github.com/pluginagentmarketplace/custom-plugin-ai-red-teaming/tree/main/skills/adversarial-examples
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-red-teaming --skill adversarial-examples

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires string, base64, codecs, typing, dataclasses, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

The Adversarial Examples skill helps teams systematically uncover LLM vulnerabilities by generating controlled edge cases, malformed inputs, and boundary payloads to evaluate model resilience.

Core Features & Use Cases

  • Automated Adversarial Generation: Produce categorized input variations (linguistic, numerical, logical, format, consistency) to probe model behavior.
  • Deterministic Mutation & Extensibility: Uses a built-in Python script to generate mutations and can be extended with additional payloads or reference patterns.
  • Use Case: Run the tool against a deployed LLM to measure failure rates and guide mitigation strategies, such as prompt safety tuning and input validation.

Quick Start

To begin, run the included adversarial generator to produce test inputs and integrate results into your QA workflow. See the references for patterns and how to apply outputs.

Frequently Asked Questions about adversarial-examples

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

FAQPage Schema
How do I generate adversarial inputs to stress-test LLM robustness?▼

To generate adversarial inputs for LLM robustness, run the included Python script to produce deterministic input mutations targeting linguistic, numerical, logical, format, and consistency edge cases. These categorized payloads reveal model failure modes in real-world prompts.

What types of edge cases should I use to test prompt safety and resilience?▼

Edge cases for prompt safety testing should include linguistic variations, numerical boundaries, logical inconsistencies, malformed formats, and consistency scenarios. These controlled categories probe specific model vulnerabilities and help measure failure rates during evaluation.

Can I extend the built-in mutator with custom payloads for LLM testing?▼

You can extend the mutator with custom payloads and reference patterns. The generator uses a structured schema and relies on standard Python libraries like base64 and codecs, allowing you to add specialized mutations for targeted safety testing.

What's the best way to automate boundary payload generation for deployed models?▼

The best way to automate boundary payload generation is running the deterministic mutation script against your deployed LLM. It outputs categorized edge cases that integrate directly into QA workflows to measure failure rates and guide mitigation strategies.

Does this approach to adversarial testing rely on any external dependencies?▼

This adversarial testing approach relies only on standard Python libraries including string, base64, codecs, typing, and dataclasses. No external frameworks are required, making it lightweight and easy to integrate into existing software engineering pipelines.

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