Threat Surface Pulse
Real-time snapshots from CISA KEV and other signals. Highlights exposed risk and trending CVEs.
- Recent KEV additions
- Exec-ready talking points
Microsoft
Microsoft Windows Mark of the Web (MOTW) contains a protection mechanism failure vulnerability that allows an attacker to bypass MOTW-based defenses. This can result in a limited loss of integrity and availability of security features such as Protected View in Microsoft Office, which rely on MOTW tagging.
Microsoft
Microsoft Windows Installer contains an improper privilege management vulnerability that could allow an attacker to gain SYSTEM privileges.
Microsoft
Microsoft Publisher contains a protection mechanism failure vulnerability that allows attacker to bypass Office macro policies used to block untrusted or malicious files.
SonicWall
SonicWall SonicOS contains an improper access control vulnerability that could lead to unauthorized resource access and, under certain conditions, may cause the firewall to crash.
Linux
Linux kernel contains a position-independent executable (PIE) stack buffer corruption vulnerability in load_elf_ binary() that allows a local attacker to escalate privileges.
AI/ML Signal Tracker
Tracks model releases, repos, and outages; summarizes impact for platform roadmaps.
- Top moving repos
- Signal strength
RepiFahmiSidiq/Onchain-Security-Suite
🛡️ Strengthen Web3 security with our AI-driven token auditor and reputation engine, ensuring safer transactions and reliable smart contracts.
mikehubers/Awesome-AI-For-Security
🛡️ Discover essential tools and resources that leverage AI for enhancing cybersecurity, focusing on modern technologies and their applications in security operations.
zimingttkx/Network-Security-Based-On-ML
🛡️ 基于机器学习的网络安全威胁检测系统 | 完整的端到端ML项目，包含数据处理、模型训练、Web界面和API服务 | 适合初学者学习的实战项目 | Python + FastAPI + Scikit-learn + XGBoost
Western-OC2-Lab/AutoML-and-Adversarial-Attack-Defense-for-Zero-Touch-Network-Security
This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.
prashantshukla01/Network_Security
This project aims to detect malicious network activity using Machine Learning-based Intrusion Detection. It focuses on analyzing network traffic data to classify whether behavior is normal or attack-related, helping organizations strengthen their cybersecurity posture.
PeterHovng/HUTECH_DACN.CyberSecurity.AWS
Đồ án chuyên ngành - ngành An ninh mạng "Hệ thống phát hiện tấn công mạng trên AWS bằng Machine Learning (Network Intrusion Detection System - NIDS)"