A framework that finds and masks personal data in text, images and tables. It detects the data, then redacts or replaces it so it can be safely sent to a model.
All repositories
Securitygreshake
llm-security
Materials for research on indirect prompt injection: how malicious instructions in emails, web pages and code break LLM-integrated apps. Includes attack demos.
Who it is for
For people who want to understand how LLM apps get attacked through external data.
How to start
- Read the Overview and Demonstrations sections of the README
- To run the code, install dependencies:
pip install -r requirements.txt - Open the linked paper and compare it with the demos
Steps are taken from the README. Check the current version in the repository before running them.
Stars over the last 30 days
+12Sep 6 — Oct 4
Author's description
New ways of breaking app-integrated LLMs
More in «Security»
A vulnerability scanner for LLMs: it runs a model through a set of probes for prompt injection, data leakage, toxicity and other failures. Works as a command-line tool.
A Python framework that checks LLM inputs and outputs with ready validators from Guardrails Hub and helps produce structured answers. It catches risks before a reply reaches the user.
NVIDIA's toolkit for programmable rails in LLM-based conversational systems. Rules live in config and control what the bot talks about and does.
Microsoft's framework for proactively finding risks in generative AI systems. It helps automate red teaming against models and applications.
Meta's generative AI safety project: the Llama Guard and Prompt Guard filter models, the Code Shield scanner and the CyberSec Eval test suites. It pairs defense with attack-based testing.