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
Apple
Apple iOS, iPadOS, macOS, tvOS, watchOS, and visionOS kernel contain a memory corruption vulnerability that allows an attacker with arbitrary kernel read and write capability to bypass kernel memory protections.
Apple
Apple iOS, iPadOS, macOS, tvOS, and watchOS RTKit contain a memory corruption vulnerability that allows an attacker with arbitrary kernel read and write capability to bypass kernel memory protections.
Android
Android Pixel contains a vulnerability in the Framework component, where the UI may be misleading or insufficient, providing a means to hide a foreground service notification. This could enable a local attacker to disclose sensitive information.
Sunhillo
Sunhillo SureLine contains an OS command injection vulnerability that allows an attacker to cause a denial-of-service or utilize the device for persistence on the network via shell metacharacters in ipAddr or dnsAddr in /cgi/networkDiag.cgi.
Microsoft
Microsoft Windows Kernel contains an exposed IOCTL with insufficient access control vulnerability within the IOCTL (input and output control) dispatcher in appid.sys that allows a local attacker to achieve privilege escalation.
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.
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.