git-disl

awesome-LLM-game-agent-papers

A Survey on Large Language Model-Based Game Agents

PokeLLMon

awesome_LLM-harmful-fine-tuning-papers

A survey on harmful fine-tuning attack for large language model

TOG

Real-time object detection is one of the key applications of deep neural networks (DNNs) for real-world mission-critical systems. While DNN-powered object detection systems celebrate many life-enriching opportunities, they also open doors for misuse and abuse. This project presents a suite of adversarial objectness gradient attacks, coined as TOG, which can cause the state-of-the-art deep object detection networks to suffer from untargeted random attacks or even targeted attacks with three types of specificity: (1) object-vanishing, (2) object-fabrication, and (3) object-mislabeling. Apart from tailoring an adversarial perturbation for each input image, we further demonstrate TOG as a universal attack, which trains a single adversarial perturbation that can be generalized to effectively craft an unseen input with a negligible attack time cost. Also, we apply TOG as an adversarial patch attack, a form of physical attacks, showing its ability to optimize a visually confined patch filled with malicious patterns, deceiving well-trained object detectors to misbehave purposefully.

BERT4ETH

BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection (WWW23)

GPTLens

Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives (TPS23)

EllipticPlusPlus

Elliptic++ Dataset: A Graph Network of Bitcoin Blockchain Transactions and Wallet Addresses

Virus

This is the official code for the paper "Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing Guardrail Moderation"

Vaccine

This is the official code for the paper "Vaccine: Perturbation-aware Alignment for Large Language Models" (NeurIPS2024)

Lockdown

A backdoor defense for federated learning via isolated subspace training (NeurIPS2023)

LRBench

A learning rate recommending and benchmarking tool.

Booster

This is the official code for the paper "Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation" (ICLR2025 Oral).

Lisa

This is the official code for the paper "Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning" (NeurIPS2024)

EnsembleBench

A holistic framework for promoting high diversity ensemble learning.

EMO

Efficient Multi-Object Tracking for Edge devices

GTDLBench

Benchmarking Deep Learning Frameworks

EENet

Code for Adaptive Deep Neural Network Inference Optimization with EENet

recap

Code for CVPR24 Paper - Resource-Efficient Transformer Pruning for Finetuning of Large Models

ZipZap

Chameleon

llm-topla

HeteRobust

Fed-alphaCDP

This repo is for paper Securing Distributed SGD against Gradient Leakage Threats submitted to IEEE TPDS.

ModelCloak

Code for ICDM 2023 Model Cloaking against Gradient Leakage

GPTLens-Demo

This demo provides a scenario-based walkthrough of how GPTLens utilizes LLMs to examine smart contract code and detect vulnerabilities.

GRING

LRBenchPlusPlus

PFT

Atlas

Atlas, a hybrid cloud migration advisor offers migration recommendations with customizable performance, cost and availability trade-offs. Also check our API resource estimation work below.

CasTformer