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, and other Apple products contain an arbitrary read and write vulnerability that allows an attacker to bypass Pointer Authentication.
Apple
Apple iOS, iPadOS, macOS, and other Apple products contain a memory corruption vulnerability that allows for code execution when processing an audio stream in a maliciously crafted media file.
SonicWall
SonicWall SMA100 appliances contain an OS command injection vulnerability in the management interface that allows a remote authenticated attacker to inject arbitrary commands as a 'nobody' user, which could potentially lead to code execution.
Linux
Linux Kernel contains an out-of-bounds read vulnerability in the USB-audio driver that allows a local, privileged attacker to obtain potentially sensitive information.
Linux
Linux Kernel contains an out-of-bounds access vulnerability in the USB-audio driver that allows an attacker with physical access to the system to use a malicious USB device to potentially manipulate system memory, escalate privileges, or execute arbitrary code.
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
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)"