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
Arm
Arm Mali GPU Kernel Driver contains a use-after-free vulnerability that allows a local, non-privileged user to make improper GPU memory processing operations to gain access to already freed memory.
Google Chromium libvpx contains a heap buffer overflow vulnerability in vp8 encoding that allows a remote attacker to potentially exploit heap corruption via a crafted HTML page. This vulnerability could impact web browsers using libvpx, including but not limited to Google Chrome.
Red Hat
Red Hat JBoss RichFaces Framework contains an expression language injection vulnerability via the UserResource resource. A remote, unauthenticated attacker could exploit this vulnerability to execute malicious code using a chain of Java serialized objects via org.ajax4jsf.resource.UserResource$UriData.
Apple
Apple iOS, iPadOS, macOS, and watchOS contain an improper certificate validation vulnerability that can allow a malicious app to bypass signature validation.
Apple
Apple iOS, iPadOS, macOS, and watchOS contain an unspecified vulnerability that allows for local privilege escalation.
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.