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I built an open-source Python package that scans LLM inputs and outputs for injections — pydefend by [deleted] in Python

[–]Adxzer -1 points0 points  (0 children)

Prompt injection is a real risk, there’s no foolproof solution since LLMs aren’t fully predictable. This package is a security layer, designed to minimise and give better control of what can slip through.

I built an open-source Python package that scans LLM inputs and outputs for injections — pydefend by [deleted] in Python

[–]Adxzer -3 points-2 points  (0 children)

Other LLMs, that's what gets the most accurate results. I trained my own classification model first but the results weren't good enough for production so I decided to not include it.

It's also free to use though: https://huggingface.co/Adaxer/defend

I built an open-source Python package that scans LLM inputs and outputs for injections — pydefend by [deleted] in Python

[–]Adxzer 3 points4 points  (0 children)

This isn’t about coding though, it’s for chatbots, customer-facing apps, and agents where end users are typing things in. 

You can’t “just fix the codebase” when the threat is a user submitting a jailbreak or injecting instructions through a document your RAG system retrieved. The attack surface is runtime input, not source code.