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
Atlassian
Atlassian Confluence Data Center and Server contains a broken access control vulnerability that allows an attacker to create unauthorized Confluence administrator accounts and access Confluence.
Progress
Progress WS_FTP Server contains a deserialization of untrusted data vulnerability in the Ad Hoc Transfer module that allows an authenticated attacker to execute remote commands on the underlying operating system.
Apple
Apple iOS and iPadOS contain an unspecified vulnerability that allows for local privilege escalation.
JetBrains
JetBrains TeamCity contains an authentication bypass vulnerability that allows for remote code execution on TeamCity Server.
Microsoft
Microsoft Windows Cryptographic Next Generation (CNG) Key Isolation Service contains an unspecified vulnerability that allows an attacker to gain specific limited SYSTEM privileges.
AI/ML Signal Tracker
Tracks model releases, repos, and outages; summarizes impact for platform roadmaps.
- Top moving repos
- Signal strength
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.
RepiFahmiSidiq/Onchain-Security-Suite
🛡️ Strengthen Web3 security with our AI-driven token auditor and reputation engine, ensuring safer transactions and reliable smart contracts.
MUKUL-TIWARI/CyberShield-Security-Suite
AI-powered phishing, email, and vishing detection system.
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.