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
FXC
FXC AE1021 and AE1021PE contain an OS command injection vulnerability that allows authenticated users to execute commands via a network.
QNAP
QNAP VioStar NVR contains an OS command injection vulnerability that allows authenticated users to execute commands via a network.
Unitronics
Unitronics Vision Series PLCs and HMIs ship with an insecure default password, which if left unchanged, can allow attackers to execute remote commands.
Qlik
Qlik Sense contains a path traversal vulnerability that allows a remote, unauthenticated attacker to create an anonymous session by sending maliciously crafted HTTP requests. This anonymous session could allow the attacker to send further requests to unauthorized endpoints.
Qlik
Qlik Sense contains an HTTP tunneling vulnerability that allows an attacker to escalate privileges and execute HTTP requests on the backend server hosting the software.
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.