MINI-FRC3-9XF6-2X25

Bulletin has no description...

MINI-3PG8-R5JW-C42F

Bulletin has no description...

CVE-2025-38615

In the Linux kernel, the following vulnerability has been resolved: fs/ntfs3: cancle set bad inode after removing name fails The reproducer uses a file0 on a ntfs3 file system with a corrupted ilink. When renaming, the file0's inode is marked as a bad inode because the file name cannot be deleted...

CVE-2025-38583

In the Linux kernel, the following vulnerability has been resolved: clk: xilinx: vcu: unregister pllpost only if registered correctly If registration of pllpost is failed, it will be set to NULL or ERR, unregistering same will fail with following call trace: Unable to handle kernel NULL pointer...

n-days

vulnerabilities found & reported, but not fixed n-days This...

Exploit for Exposure of Sensitive Information to an Unauthorized Actor in Imagemagick

Exploit tailored for HackTheBox Pilgrimage Automatic python...

Securing the Internet of Medical Things (IoMT): Real-World Attack Taxonomy and Practical Security Measures

The Internet of Medical Things IoMT has the potential to radically improve healthcare by enabling real-time monitoring, remote diagnostics, and AI-driven decision making. However, the connectivity, embedded intelligence, and inclusion of a wide variety of novel sensors expose medical devices to...

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset

This study investigates the effectiveness of several machine learning algorithms for static malware detection using the EMBER dataset, which contains feature representations of Portable Executable PE files. We evaluate eight classification models: LightGBM, XGBoost, CatBoost, Random Forest, Extra...

MeAJOR Corpus: a Multi-Source Dataset for Phishing Email Detection

Phishing emails continue to pose a significant threat to cybersecurity by exploiting human vulnerabilities through deceptive content and malicious payloads. While Machine Learning ML models are effective at detecting phishing threats, their performance largely relies on the quality and diversity ...

Exploit for Deserialization of Untrusted Data in Microsoft

CVE-2025-53770 SharePoint Deserialization RCE PoC Critica...

An Adversarial-Driven Experimental Study on Deep Learning for RF Fingerprinting

Radio frequency RF fingerprinting, which extracts unique hardware imperfections of radio devices, has emerged as a promising physical-layer device identification mechanism in zero trust architectures and beyond 5G networks. In particular, deep learning DL methods have demonstrated state-of-the-ar...

Using Modular Arithmetic Optimized Neural Networks to Crack Affine Cryptographic Schemes Efficiently

We investigate the cryptanalysis of affine ciphers using a hybrid neural network architecture that combines modular arithmetic-aware and statistical feature-based learning. Inspired by recent advances in interpretable neural networks for modular arithmetic and neural cryptanalysis of classical...

ExCyTIn-Bench: Evaluating LLM Agents on Cyber Threat Investigation

We present ExCyTIn-Bench, the first benchmark to Evaluate an LLM agent x on the task of Cyber Threat Investigation through security questions derived from investigation graphs. Real-world security analysts must sift through a large number of heterogeneous alert signals and security logs, follow...

Exploit for OS Command Injection in Magnussolution Magnusbilling

🧠 TryHackMe Room Walkthrough: Billing Room Link: htt...

A Login Page Transparency and Visual Similarity Based Zero Day Phishing Defense Protocol

Phishing is a prevalent cyberattack that uses look-alike websites to deceive users into revealing sensitive information. Numerous efforts have been made by the Internet community and security organizations to detect, prevent, or train users to avoid falling victim to phishing attacks. Most of thi...

Exploiting Leaderboards for Large-Scale Distribution of Malicious Models

While poisoning attacks on machine learning models have been extensively studied, the mechanisms by which adversaries can distribute poisoned models at scale remain largely unexplored. In this paper, we shed light on how model leaderboards -- ranked platforms for model discovery and evaluation --...

The Trust Fabric: Decentralized Interoperability and Economic Coordination for the Agentic Web

The fragmentation of AI agent ecosystems has created urgent demands for interoperability, trust, and economic coordination that current protocols -- including MCP Hou et al., 2025, A2A Habler et al., 2025, ACP Liu et al., 2025, and Cisco's AGP Edwards, 2025 -- cannot address at scale. We present...

A Formal Rebuttal of "The Blockchain Trilemma: a Formal Proof of the Inherent Trade-Offs among Decentralization, Security, and Scalability"

This paper presents a comprehensive refutation of the so-called "blockchain trilemma," a widely cited but formally ungrounded claim asserting an inherent trade-off between decentralisation, security, and scalability in blockchain protocols. Through formal analysis, empirical evidence, and detaile...

Understanding the Theoretical Guarantees of DPM

In this study, we conducted an in-depth examination of the utility analysis of the differentially private mechanism DPM. The authors of DPM have already established the probability of a good split being selected and of DPM halting. In this study, we expanded the analysis of the stopping criterion...

The Amazon Nova Family of Models: Technical Report and Model Card

We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highly-capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks. Amazon...