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
Google Chromium WebP contains a heap-based buffer overflow vulnerability that allows a remote attacker to perform an out-of-bounds memory write via a crafted HTML page. This vulnerability can affect applications that use the WebP Codec.
Microsoft
Microsoft Word contains an unspecified vulnerability that allows for information disclosure.
Microsoft
Microsoft Streaming Service Proxy contains an unspecified vulnerability that allows for privilege escalation.
Apple
Apple iOS, iPadOS, and macOS contain a buffer overflow vulnerability in ImageIO when processing a maliciously crafted image, which may lead to code execution. This vulnerability was chained with CVE-2023-41061.
Apple
Apple iOS, iPadOS, and watchOS contain an unspecified vulnerability due to a validation issue affecting Wallet in which a maliciously crafted attachment may result in code execution. This vulnerability was chained with CVE-2023-41064.
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.