raccoon
aegis
raccoon | aegis | |
---|---|---|
1 | 4 | |
6 | 246 | |
- | 2.0% | |
4.7 | 5.6 | |
about 1 year ago | 4 months ago | |
Python | Python | |
MIT License | MIT License |
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raccoon
aegis
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Show HN: Firewall for LLMs–Guard Against Prompt Injection, PII Leakage, Toxicity
Hey HN,
We're building Aegis, a firewall for LLMs: a guard against adversarial attacks, prompt injections, toxic language, PII leakage, etc.
One of the primary concerns entwined with building LLM applications is the chance of attackers subverting the model’s original instructions via untrusted user input, which unlike in SQL injection attacks, can’t be easily sanitized. (See https://greshake.github.io/ for the mildest such instance.) Because the consequences are dire, we feel it’s better to err on the side of caution, with something mutli-pass like Aegis, which consists of a lexical similarity check, a semantic similarity check, and a final pass through an ML model.
We'd love for you to check it out—see if you can prompt inject it!, and give any suggestions/thoughts on how we could improve it: https://github.com/automorphic-ai/aegis.
If you want to play around with it without creating an account, try the playground: https://automorphic.ai/playground.
If you're interested in or need help using Aegis, have ideas, or want to contribute, join our [Discord](https://discord.com/invite/E8y4NcNeBe), or feel free to reach out at [email protected]. Excited to hear your feedback!
Repository: https://github.com/automorphic-ai/aegis
- We’ve built a free firewall for LLMs (Aegis) — Say goodbye to prompt injections, prompt leakage, and toxic language (100+ stars)
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Try your best prompts—especially prompt injections—against Aegis, our firewall for LLMs
We've built Aegis, a firewall for LLMs (a guard against malicious inputs, prompt injections, toxic language, etc), and we'd love for you to check it out—see if you can prompt inject it!, and give any suggestions/thoughts on how we could improve it: https://github.com/automorphic-ai/aegis. Internally, it consists of a lexical similarity check, a semantic similarity check, and a final pass through an ML model.
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Creating a Firewall for LLMs
Hey guys, we're creating aegis, a self-hardening firewall for large language models. Protect your models from adversarial attacks: prompt injections, prompt and PII leakage, and more.
What are some alternatives?
odin-slides - This is an advanced Python tool that empowers you to effortlessly draft customizable PowerPoint slides using the Generative Pre-trained Transformer (GPT) of your choice. Leveraging the capabilities of Large Language Models (LLM), odin-slides enables you to turn the lengthiest Word documents into well organized presentations.
llm-guard - The Security Toolkit for LLM Interactions
TextAttack - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
T-RAGS - Trustworthy Retrieval Augmented Generation (RAG) with Safeguards
llm-api-starterkit - Beginner-friendly repository for launching your first LLM API with Python, LangChain and FastAPI, using local models or the OpenAI API.
vibraniumdome - LLM Security Platform.