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
Palo Alto Networks
Palo Alto Networks PAN-OS GlobalProtect feature contains a command injection vulnerability that allows an unauthenticated attacker to execute commands with root privileges on the firewall.
D-Link
D-Link DNS-320L, DNS-325, DNS-327L, and DNS-340L contain a command injection vulnerability. When combined with CVE-2024-3272, this can lead to remote, unauthorized code execution.
D-Link
D-Link DNS-320L, DNS-325, DNS-327L, and DNS-340L contains a hard-coded credential that allows an attacker to conduct authenticated command injection, leading to remote, unauthorized code execution.
Android
Android Pixel contains a privilege escalation vulnerability that allows an attacker to interrupt a factory reset triggered by a device admin app.
Android
Android Pixel contains an information disclosure vulnerability in the fastboot firmware used to support unlocking, flashing, and locking affected devices.
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.