Remote

Apache APISIX Remote Code Execution (CVE-2022-24112) proof of concept exploit

Summary An attacker can abuse the batch-requests plugin to send requests tobypass the IP restriction of Admin API.A default configuration of Apache APISIX (with default API key) isvulnerable to remote code execution.When the admin key was changed or the port of Admin API was changed toa port different from the data panel, the impact is lower. But thereis still a risk to bypass the IP restriction of Apache APISIX’s datapanel. There is a check in the batch-requests plugin which overrides […]

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obfuscation

Advanced python obfuscator to obfuscate any python files using marshal, zlib, base64, base32, base16, base85 and many others

Python obfuscator / Python encoder / Python encrypter to obfuscate, encode, encrypt any python files using marshal, zlib, base64, base32, base16, base85 and many others. Download Python 3.6 and + (https://www.python.org) Simply browse and get your python file. Then enter the amount of time Monkey will obfuscate your python file (1 TIME IS RECOMMENDED). You can also add walls which will protect even more your source code and annoy people that will try to deobfuscate it. GitHub View Github   […]

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Remove

scAR (single-cell Ambient Remover) is a package for data denoising in single-cell omics

scAR (single cell Ambient Remover) is a package for denoising multiple single cell omics data. It can be used for multiple tasks, such as, sgRNA assignment for scCRISPRseq, identity barcode assignment for cell indexing, protein denoising for CITE-seq, mRNA denoising for scRNAseq, and etc… It is built using probabilistic deep learning, illustrated as follows: Installation Clone this repository, $ git clone https://github.com/CaibinSh/scAR.git To install the dependencies, create a conda environment:

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Various

Handy python interfaces to various lab test equipment

Florian DupeyronOctober 2021-January 2022 Making this work on linux Copy the udev rules : $ sudo cp udev/*.rules /etc/udev/rules.d $ sudo udevadm control –reload $ sudo udevadm trigger For usbtmc devices, create the usbtmc group and add your user to it: sudo groupadd usbtmc sudo usermod -aG usbtmc $USER Restart your session Initialize a python virtual environment inside the directory (you may need to install a distribution package): $ python3    

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Proxy

Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?

Pdf: Arxiv, Openreview Code for our ICLR 2022 paper where we show that synthetic data from diffusion models can provide a tremendous boost in the performance of robust training. We also provide synthetic data used in the paper for all five datasets, namely CIFAR-10, CIFAR-100, ImageNet, CelebA, and AFHQ. We also provide synthetic data from seven different generative models for CIFAR-10, which was used to analyze impact of different generative models in section 3.2. Despite being minimalistic, this codebase also […]

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Tracker

Drone Verification using SiamRPN Tracker

In this work, we evaluated the authenticity of drone in real time scenario using computer vision based deep tracker. More information about this work is in this file Python version 3.7 has been used. Following installations are required for working code Install the below libraries inside your conda environment conda install pytorch torchvision -c pytorch pip install opencv-python imutils pyyaml yacs tqdm colorama matplotlib cython tensorboardX For using the code on other platforms except OSX, comment the following line from […]

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Scripts

Python script to check if your kernel is vulnerable to Dirty pipe CVE-2022-0847

Python script to check if your kernel is vulnerable to Dirty pipe CVE-2022-0847 You can execute directly the script on your system and it will find your kernel version But you can execute the script with as argument a kernel version to check (3 digit kernel version, like 5.10.103) ./check-dirty-pipe.py 5.10.103 and it will print if this version is vulnerable or not:Is your system vulnerable to CVE: False GitHub View Github    

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Password

Guessing passwords with Generative Flows

PassFlow exploits the properties of Generative Flows to perform password guessing and shows very promising results, being competetive against GAN-based approaches [1, 2]. Usage To get the dataset, run curl -L –create-dirs -o data/train.txt https://github.com/d4ichi/PassGAN/releases/download/data/rockyou-train.txt curl -L –create-dirs -o data/test.txt https://github.com/d4ichi/PassGAN/releases/download/data/rockyou-test.txt and then run pip install tqdm torch==1.7.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html to install the needed dependencies. We tested using PyTorch 1.7.1 and CUDA 11.0. Once a model is trained, you can

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