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New ways of breaking app-integrated LLMs
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Latest capture 2026-07-07 03:03
5 captures since 2026-05-27
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Scanned 2026-07-07 03:03
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Every practical and proposed defense against prompt injection.
[EMNLP'23, ACL'24] To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.
LangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading and indexing data, creating prompt templates, CSV agents, and using retrieval QA chains to query the custom data. Projects for using a private LLM (Llama 2) for chat with PDF files, tweets sentiment analysis.
Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.
The Security Toolkit for LLM Interactions
llama.cpp fork with additional SOTA quants and improved performance
