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
Apache
Apache Flink contains an improper access control vulnerability that allows an attacker to read any file on the local filesystem of the JobManager through its REST interface.
Google Chromium V8 contains a type confusion vulnerability that allows a remote attacker to execute code via a crafted HTML page.
NextGen Healthcare
NextGen Healthcare Mirth Connect contains a deserialization of untrusted data vulnerability that allows for unauthenticated remote code execution via a specially crafted request.
Google Chromium V8 Engine contains an unspecified out-of-bounds memory write vulnerability via a crafted HTML page. This vulnerability could affect multiple web browsers that utilize Chromium, including, but not limited to, Google Chrome, Microsoft Edge, and Opera.
D-Link
D-Link DIR-605 routers contain an information disclosure vulnerability that allows attackers to obtain a username and password by forging a post request to the /getcfg.php page.
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.