adversarial-examples

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.