🔎
garak
  • 👋Welcome to garak!
  • Overview
    • 💡What is garak?
    • ✨Our Features
  • LLM scanning basics
    • 🔐What is LLM security?
    • 🛠️Setting up
      • 😇Installing garak
      • 🐍Installing the source code
    • 🚀Your first scan
    • 🔮Reading the results
  • Examples
    • ☑️Basic test
    • 💉Prompt injection
    • ☢️Toxicity generation
    • 🗝️Jailbreaks
    • 💱Encoding-based bypass
    • 📼Data leaks & replay
    • 🤦False reasoning
    • 🛀Automatic soak test
  • garak components
    • 🕵️‍♀️Vulnerability probes
    • 🦜Using generators
    • 🔎Understanding detectors
    • 🏇Managing it: harnesses
    • 💯Scan evaluation
  • Automatic red-teaming
    • 🔴What is red-teaming?
    • 🤼Responsive auto-prompt
    • 🪖garak's auto red-team
    • 🏞️Red teaming in the wild
  • Going further
    • ❓FAQ
    • 💁Getting help
    • 🎯Reporting hits
    • 🧑‍🤝‍🧑Contributing to garak
Powered by GitBook
On this page
  1. LLM scanning basics

What is LLM security?

LLM security is the investigation of the failure modes of LLMs in use, the conditions that lead to them, and their mitigations.

Large language models can fail to operate as expected or desired in a huge number of ways; this means they can be insecure. On top of that, they need to run in software (like PyTorch, ONNX, or CUDA) - and that software can be insecure. Finally, the way that LLMs are deployed and their outputs are used can also fail when the LLM behaves in an unexpected way, which also presents a security risk. LLM security covers all this.

LLM security is broader than just things that are within existing security knowledge and existing LLM/NLP knowledge. LLM security covers not the intersection of Security and NLP, but the union of everything about information security and everything about natural language processing.

PreviousOur FeaturesNextSetting up

Last updated 1 year ago

🔐