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
Zyxel
Zyxel EMG2926 routers contain a command injection vulnerability located in the diagnostic tools, specifically the nslookup function. A malicious user may exploit numerous vectors to execute malicious commands on the router, such as the ping_ip parameter to the expert/maintenance/diagnostic/nslookup URI.
Laravel
Laravel Ignition contains a file upload vulnerability that allows unauthenticated remote attackers to execute malicious code due to insecure usage of file_get_contents() and file_put_contents().
Adobe
Adobe Acrobat and Reader contains an out-of-bounds write vulnerability that allows for code execution.
Android
Android Framework contains an unspecified vulnerability that allows for privilege escalation.
Cisco
Cisco Adaptive Security Appliance and Firepower Threat Defense contain an unauthorized access vulnerability that could allow an unauthenticated, remote attacker to conduct a brute force attack in an attempt to identify valid username and password combinations or establish a clientless SSL VPN session with an unauthorized user.
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.