reverse-SynthID-text

SynthID Watermark Reverse Engineering Overview This directory contains tools for analyzing and removing SynthID watermarks from AI-generated text. The watermark was developed by Google DeepMind and published in Nature 2024. How SynthID Works Watermarking Process 1. N-gram Context : For each token...

vulhunt

VulHunt Community Edition VulHunt is a vulnerability hunting framework developed by Binarly's Research team. It is designed to help security researchers and practitioners identify vulnerabilities in software binaries and UEFI firmware. VulHunt is built on top of Binarly's Binary Analysis and...

Pegasus-Gram

Pegasus-Gram – Advanced Keylogger and Cyber Monitoring Suite 🕵️‍♂️ Author: Letda Kes dr. Sobri, S.Kom 📧 Contact: [email protected] 🐙 GitHub: github.com/sobri3195 💰 Support the Project: Donate via Lynk.id Pegasus-Gram is a Python-based advanced keylogger and cyber surveillance tool integrated...

kbd-audio

kbd-audio Questa è una raccolta di strumenti da riga di comando e GUI per la cattura e l'analisi di dati audio. Keytap Lo strumento più interessante si chiama keytap - può indovinare i tasti premuti sulla tastiera analizzando solo l'audio catturato dal microfono del computer. Guarda questo post...

What’s the Scam?

To subscribe to my monthly email newsletter, you have to enter your information on the webpage, and then reply to an automatically generated email. This is, of course, to prevent people from subscribing addresses other than their own. Starting last weekend, I have been receiving a lot of individu...

Benchmarking Optical Receivers for Quantum Communication and Randomness Certification

The choice of optical receiver determines which properties of the transmitted states remain visible in the observed data and therefore affects the performance of different quantum protocols. We compare continuous-variable, photon-counting and hybrid receivers within the same prepare-and-measure...

CVE-2025-69048

creationtimestamp| type| source ---|---|--- 2026-01-27 17:25:57+00:00| seen| Telegram/zQovba3jq34R8mOIwo4UQbXIwilVvhjiGRRMMjnKXHFewE...

JLSEC-2025-174 An issue was discovered in GNU gettext 0.19.8

An issue was discovered in GNU gettext 0.19.8. There is a double free in defaultaddmessage in read-catalog.c, related to an invalid free in pogramparse in po-gram-gen.y, as demonstrated by lt-msgfmt...

PT-2025-45966

An issue was discovered in GNU gettext 0.19.8. There is a double free in default add message in read-catalog.c, related to an invalid free in po gram parse in po-gram-gen.y, as demonstrated by lt-msgfmt...

NewStart CGSL MAIN 6.06 : gettext Vulnerability (NS-SA-2025-0218)

The remote NewStart CGSL host, running version MAIN 6.06, has gettext packages installed that are affected by a vulnerability: - An issue was discovered in GNU gettext 0.19.8. There is a double free in defaultaddmessage in read- catalog.c, related to an invalid free in pogramparse in po-gram-gen....

Malicious code in gram-utilz (npm)

The package gram-utilz was found to contain malicious code. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware f7c772fd2d6b9f919249def32fb96f26a08cf5bf3ebf008ab12736e774021864 Any computer that has this package installed or running should be considered fully compromise...

Malware in gram-utilz

Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be rotated immediately from a different computer. The package should be removed, but as full control of the computer may have been given to an outsid...

Malicious Package

Overview gram-utilz is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this package authorship...

MAL-2025-47061 Malicious code in gram-utilz (npm)

The package gram-utilz was found to contain malicious code. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware f7c772fd2d6b9f919249def32fb96f26a08cf5bf3ebf008ab12736e774021864 Any computer that has this package installed or running should be considered fully compromise...

FedGraM: Defending against Untargeted Attacks in Federated Learning Via Embedding Gram Matrix

Federated Learning FL enables geographically distributed clients to collaboratively train machine learning models by sharing only their local models, ensuring data privacy. However, FL is vulnerable to untargeted attacks that aim to degrade the global model's performance on the underlying data...

MalVis: a Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification

As technology advances, Android malware continues to pose significant threats to devices and sensitive data. The open-source nature of the Android OS and the availability of its SDK contribute to this rapid growth. Traditional malware detection techniques, such as signature-based, static, and...

Residual-Evasive Attacks on ADMM in Distributed Optimization

This paper presents two attack strategies designed to evade detection in ADMM-based systems by preventing significant changes to the residual during the attacked iteration. While many detection algorithms focus on identifying false data injection through residual changes, we show that our attacks...

Scalable APT Malware Classification Via Parallel Feature Extraction and GPU-Accelerated Learning

This paper presents an underlying framework for both automating and accelerating malware classification, more specifically, mapping malicious executables to known Advanced Persistent Threat APT groups. The main feature of this analysis is the assembly-level instructions present in executables whi...

OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques

This technical report presents a comprehensive analysis of malware classification using OpCode sequences. Two distinct approaches are evaluated: traditional machine learning using n-gram analysis with Support Vector Machine SVM, K-Nearest Neighbors KNN, and Decision Tree classifiers; and a deep...

Malicious Code Detection in Smart Contracts Via Opcode Vectorization

With the booming development of blockchain technology, smart contracts have been widely used in finance, supply chain, Internet of things and other fields in recent years. However, the security problems of smart contracts become increasingly prominent. Security events caused by smart contracts...