LLM Defense Framework

A comprehensive framework for enhancing LLM security through post-processing defenses and statistical guarantees.

Overview

This project implements a novel approach to LLM security focusing on:

• Post-processing defense mechanisms
• Statistical guarantees through one-class SVM
• Adaptive policy updates
• Multimodal security evaluation

Components

1. Sampling Methods

• Speculative decoding optimization
• Tree-based sampling
• Nucleus sampling with guarantees

2. Defense Mechanisms

• Content filtering with statistical guarantees
• Policy adaptation framework
• Real-time verification

3. Evaluation Framework

• Comprehensive security benchmarks
• Performance metrics
• Statistical validation

Project Structure

.
├── src/
│   ├── sampling/       # Sampling and inference methods
│   ├── defense/        # Defense mechanisms
│   └── evaluation/     # Evaluation framework
├── research_papers/    # Relevant research papers
├── docs/              # Documentation
└── tests/             # Test suite

Getting Started

1. Installation:

pip install -r requirements.txt

2. Running tests:

python -m pytest tests/

3. Usage example:

from llm_defense import DefenseFramework

framework = DefenseFramework()
result = framework.process_text("Your input text")

Research Plan

See RESEARCH_PLAN.md for detailed research methodology and timeline.

References

Key papers and resources are available in the research_papers directory.