Stars
A package for conformal prediction with conditional guarantees.
For educational purposes only, exhaustive samples of 450+ classic/modern trojan builders including screenshots.
Malware samples, analysis exercises and other interesting resources.
A repository of LIVE malwares for your own joy and pleasure. theZoo is a project created to make the possibility of malware analysis open and available to the public.
Bringing you the best of the worst files on the Internet.
code repo for ICLR 2024 paper "Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs"
Source code for ICLR 2018 Paper: Active Learning for Convolutional Neural Networks: A Core-Set Approach
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction
Official repo for GPTFUZZER : Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts
TAP: An automated jailbreaking method for black-box LLMs
[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
[ICLR 2025 Spotlight] The official implementation of our ICLR2025 paper "AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs".
[ICLR 2024] The official implementation of our ICLR2024 paper "AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models".
[CCS'24] A dataset consists of 15,140 ChatGPT prompts from Reddit, Discord, websites, and open-source datasets (including 1,405 jailbreak prompts).
New ways of breaking app-integrated LLMs
[ICLR'24 Spotlight] A language model (LM)-based emulation framework for identifying the risks of LM agents with tool use
Compares two latex files and marks up significant differences between them. Releases on www.ctan.org and mirrors
A simple tool for visually comparing two PDF files
No fortress, purely open ground. OpenManus is Coming.
Can Knowledge Editing Really Correct Hallucinations? (ICLR 2025)
metaNet: Identify Sophisticated (Unknown Families) Mobile Malware Leveraging Meta-features Mining
PromptInject is a framework that assembles prompts in a modular fashion to provide a quantitative analysis of the robustness of LLMs to adversarial prompt attacks. 🏆 Best Paper Awards @ NeurIPS ML …
(NeurIPS 2020) Transductive Information Maximization for Few-Shot Learning https://arxiv.org/abs/2008.11297
Effective Data Augmentation With Diffusion Models
"AI-Researcher: Autonomous Scientific Innovation"