krucible 0.1.1

AI Security Regression Testing Platform

🛡️ Krucible

The Configuration-Driven AI Security Regression Testing Platform

📖 Overview

Krucible is a production-grade regression testing platform designed explicitly for Large Language Models and AI applications. As AI models update dynamically, their safety guardrails frequently drift. What was secure yesterday might leak PII today.

Krucible automates AI Security Regression Testing. It executes adversarial payloads (Prompt Injections, Jailbreaks) against your AI models, evaluates the outputs against strict security policies, and calculates semantic drift across CI/CD pipeline runs.

Why Krucible Exists

Traditional testing tools calculate binary JSON or text diffs. AI requires semantic evaluation. Krucible completely separates the execution engine from the security payloads. Developers build the pipelines; Security Researchers write the YAML payloads.

✨ Features

• Configuration-Driven: Zero Python modifications required. Define attacks and policies purely in .yml files.
• Engine Orchestration: Robust Dependency Injection architecture executing complex multi-stage evaluations securely.
• Regression Detection: Tracks policy flips, semantic similarity drifts, and tool-usage deviations.
• Three Testing Journeys:
  1. AI Providers: Direct testing of foundation models (OpenAI, Anthropic, Gemini, OpenRouter, Groq, Ollama).
  2. AI Applications: Test your deployed custom APIs. Krucible attempts to auto-detect payload structure (JSON input and output mapping), with manual override available.
  3. Native Python Agents: Hook directly into un-deployed frameworks (LangChain, CrewAI, FastAPI).
• CI/CD Ready: Generates strictly-typed machine-readable JSON artifacts and beautiful Rich terminal outputs with deterministic exit codes.

🚀 Quick Start

Installation

Install Krucible via pip:

pip install krucible

Initialization & Onboarding

Set up a new Krucible project using the interactive wizard:

python -m krucible quickstart

This journey-based wizard will guide you through testing an AI Provider, a custom AI Application, or a Native AI Agent, attempting to auto-detect structures where possible.

Running Tests

Execute your security regression test suite:

krucible test

Output:

╭─────────────────────────────── AI Security Regression Test Results ────────────────────────────────╮
│ Target: Custom custom                                                                              │
│ Duration: 1250.00ms                                                                                │
╰────────────────────────────────────────────────────────────────────────────────────────────────────╯

Attacks Executed: 2
Policies Evaluated: 2

      Execution Results       
┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓
┃ Attack            ┃ Status       ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩
│ Prompt Injection  │ [FAIL]       │
├───────────────────┼──────────────┤
│ Ignore Guardrails │ [PASS]       │
└───────────────────┴──────────────┘

Summary
Passed: 1
Failed: 1
Regressions: 0

Exit Code: 1

⚙️ Configuration

Your krucible.yml controls the orchestrator targeting.

OpenAI Example

version: v1
target:
  adapter: openai
  model: gpt-4o
regression:
  similarity_threshold: 0.85

Custom REST API Example

version: v1
target:
  adapter: custom
  model: custom
regression:
  similarity_threshold: 0.85

(Requires KRUCIBLE_CUSTOM_URL, KRUCIBLE_CUSTOM_PAYLOAD_KEY, and KRUCIBLE_CUSTOM_OUTPUT_KEY in .env)

🏗️ Architecture Overview

Krucible enforces Domain-Driven Design:

1. Loaders: AttackLoader and PolicyLoader parse local YAML into frozen Pydantic Models.
2. Adapters: Anti-corruption layers (Strategy Pattern) wrapping external SDKs (OpenAIAdapter, GeminiAdapter).
3. Orchestrator: Dispatches payloads to the Target and routes responses to the PolicyEngine.
4. Regression Engine: Compares the current Evaluation set against the local BaselineStore.
5. Report Engine: Fans out ReportSummary models to standard out (CLI) and disk (JSON).

🗺️ Roadmap

• Phase 2: Multi-turn Conversation Attacks, LLM-as-a-Judge Policy Evaluators, Ollama Native Integration.
• Phase 3: Extensible Plugin ecosystem (krucible install owasp-top-10).
• Phase 4: Visual HTML Reporting generation.

🤝 Contributing

We welcome community contributions! Please review our Contributing Guide and Code of Conduct.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.