backdoor-learning-resources

Backdoor Learning Resources This Github repository summarizes a list of Backdoor Learning resources. For more details and the categorization criteria, please refer to our survey. We will try our best to continuously maintain this Github Repository in a monthly manner. Why Backdoor Learning?...

eyeballer

Eyeballer Give those screenshots of yours a quick eyeballing. Eyeballer is meant for large-scope network penetration tests where you need to find "interesting" targets from a huge set of web-based hosts. Go ahead and use your favorite screenshotting tool like normal EyeWitness or GoWitness and th...

nCPU

A complete computer in which every layer — arithmetic, OS, compiler, display — is either a trained neural network or runs entirely on GPU. The model doesn't run on the computer. The model is the computer. nCPU is one repository pursuing one thesis from five directions: a computer can be built out...

Kaonashi

Kaonashi Project: RootedCON 2019 This repository contains several directories: Sorted Masks for hashcat Advanced Rules for hashcat Links to download the Kaonishi's Wordlists Slides used in our talk I know your p4$$w0rd and if I don't, I will guess it... at RootedCON 2019 A clone may not be...

AntiVE-BehaviorWatch

AntiVE-BehaviorWatch Embedded AI-powered human behavior verification for cybersecurity research. What is AntiVE-BehaviorWatch? AntiVE-BehaviorWatch is a Windows-based cybersecurity research project that explores a new approach to execution control using embedded artificial intelligence. Instead o...

robustness

robustness package Install via pip: pip install robustness Read the docs: https://robustness.readthedocs.io/en/latest/index.html robustness is a package we students in the MadryLab created to make training, evaluating, and exploring neural networks flexible and easy. We use it in almost all of ou...

deep-pwning

Deep-pwning is a lightweight framework for experimenting with machine learning models with the goal of evaluating their robustness against a motivated adversary. Note that deep-pwning in its current state is no where close to maturity or completion. It is meant to be experimented with, expanded...

Insider-Threat

Insider Threat Graph Models R5.2 & R6.2 This repository contains data preparation, feature extraction, and graph-based models GCN-NN, GCN-BiLSTM, and GCN-BiLSTM+Attention for implicit+explicit signals for insider-threat detection using the CMU CERT Insider Threat Test Dataset R5.2, R6.2. Reposito...

recipe-blog-encoding

recipe-blog-encoding !WARNING This project is entirely vibe-coded likely partially plagiarized from this repository and the author is a dumb-dumb who just thought the idea was funny Use SEO-gaming recipe preambles as a vehicle for encoding secret messages the prompt can really be anything though,...

Intel Neural Compressor <2.5.0 - SQL Injection

Improper input validation in some IntelR Neural Compressor software before version 2.5.0 may allow an unauthenticated user to potentially enable escalation of privilege via remote access. id: CVE-2024-22476 info: name: Intel Neural Compressor 2.5.0 - SQL Injection author: ritikchaddha,daffainfo...

CVE-2022-32898

CVE-2022-32898: ANEProgramCreate multiple kernel memory corruption Nov 23, 2022 • Mohamed GHANNAM @simo36 저자 commnet I'm sharing two other iOS kernel vulnerabilities reachable from the default app sandbox that don’t require you to open a UserClient: +16 kernel bugs I reported to Apple have been...

certified-unlearning-neural-networks-icml-2025

Certified Unlearning for Neural Networks This repository contains the implementation for the paper "Certified Unlearning for Neural Networks" ICML 2025. Methods PABI Privacy Amplification by Iteration : Gradient Clipping in the paper DP-SGD : With group privacy as described in the paper Contracti...

ane

Asahi Neural Engine Reverse engineered Linux driver for the Apple Neural Engine ANE. All things Linux here. ane/: Kernel module ane.ko. Should move into tree soon. docs/: Documentation. WIP. Please don't look. libane/: Userspace lib. python/: Python bindings for libane...

weightBufs

WeightBufs: WeightBufs is a kernel r/w exploit for all Apple devices with Neural Engine support. Bugs and Exploit by @simo36, you can read my presentation slides at POC for more details about the vulnerabilities and the exploitation techniques. The exploit doesn't rely on any hardcoded address or...

T-Backdoor

Sample Codes for the paper: 'T-Backdoor: Exploiting Temporal Redundancy in Neuromorphic Data for Spike-preserving Backdoor Attacks on SNNs' Abstract Backdoor attacks are a serious security threat to deep neural networks DNNs and remain largely underexplored for spiking neural networks SNNs...

craw

CRAW: Codec Robust Audio Watermarking Source code for CRAW , a robust audio watermarking method built on STFT-magnitude-domain embedding. An encoder embeds a binary message into an audio clip's magnitude spectrogram phase is preserved from the clean signal; a decoder recovers the message under a...

challenge_repo

🎲 The Entropy Challenge 🇬🇧 Read in English | 🇹🇷 Türkçe Oku Introduction We have developed a next-generation random number generator that processes Radio Frequency RF Noise through a proprietary Neural Network architecture , followed by SHA-3 post-processing. We claim this source provides True...

Adaptive-Shot Hybrid Quantum Anomaly Detection for Tactile Internet Security: Reliability-Aware Measurement Allocation under Resource Constraints

Tactile Internet TI security analytics must balance reliable thresholded decisions with constrained computational and measurement resources. We study this tension for finite-shot hybrid quantum anomaly inference and introduce the Adaptive-Shot Variational Quantum Circuit AS-VQC policy. This...

Security Properties of Neural Networks As Decision Problems

Certifying a deployed neural network raises decision problems that the verification literature has not classified: whether the model carries a backdoor planted in its training data, whether a fault in its stored parameters can drive it into an unsafe state, whether its output leaks a private part...

Behavior-Centric Malware Classification with Fine-Grained Malicious Logic Localization

Effective malware analysis requires understanding not only whether a program is malicious, but also which behaviors it exhibits and where those behaviors originate in the code. Existing machine-learning-based malware detectors largely operate as black boxes, providing limited insight into the...