Lakshyaaaaa/LLM-Guardrail

🛡️ LLM Guardrail

A high-speed, defense-in-depth security layer designed to detect and neutralize adversarial attacks against Large Language Models (LLMs) without degrading user experience.

⚠️ Disclaimer: This repository contains authentic adversarial datasets, including jailbreaks, indirect prompt injections, and potentially harmful content. This data is strictly curated for defensive security research, red teaming, and training purposes to mitigate commercial-grade business risks.

🎯 Overview

As AI systems scale, they become vulnerable to sophisticated attack vectors outlined in the OWASP LLM Top 10. This project aims to bridge the gap between raw vulnerability research and production security by providing a robust firewall that intercepts malicious prompts during both training and inference phases.

The core philosophy of this guardrail is balancing maximum security with minimal latency. If a security layer takes too long to process, it breaks the user experience.

🏗️ Architecture: Defense in Depth

To achieve real-time protection, this system utilizes a multi-layered architectural approach:

• —Layers 1-3 (The Shield): High-speed, lightweight validators (heuristics, regex, and basic classifiers) designed to instantly catch and drop low-effort attacks and obvious policy violations with near-zero processing overhead.
• —The Core Engine (SLM): A fine-tuned Small Language Model (SLM) that acts as the primary analytical engine. It evaluates complex, novel, and multi-turn threats (like indirect prompt injections) with high precision.

📊 The Dataset

This project is built on a foundation of ~8,000 manually mined and curated authentic data points, categorized into two main threat vectors:

1. 1.Technical Exploits: Jailbreaks, system prompt leakage, and data poisoning attempts.
2. 2.Policy Violations: Edge cases involving toxic, violent, and non-technical harmful content.

Note: The raw datasets are currently undergoing mutation to exponentially expand our coverage of edge cases for the SLM training phase.

🚀 Deployment & Hosting

When cloning or staging this project for production deployment within a technical portfolio ecosystem, ensure the repository name includes the 25 suffix to maintain compatibility with the established hosting requirements (e.g., LLM-Guardrail-25).

🤝 Contributing (Co-Builders Wanted!)

Securing the next generation of AI applications is a massive undertaking. I am actively looking for co-builders to collaborate on this guardrail.

If you have experience or interest in:

• —Red Teaming & Offensive Cybersecurity
• —Adversarial Machine Learning
• —Backend Engineering for high-speed API layers
• —SLM fine-tuning

Please reach out or open a pull request! Let's build something impactful together.