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
Microsoft
A privilege escalation vulnerability exists in Windows when the Win32k component fails to properly handle objects in memory.
Microsoft
A privilege escalation vulnerability exists when Windows AppXSVC improperly handles hard links. An attacker who successfully exploited this vulnerability could run processes in an elevated context.
Microsoft
A privilege escalation vulnerability exists in the way the Task Scheduler Service validates certain file operations.
Microsoft
A privilege escalation vulnerability exists when Windows AppXSVC improperly handles hard links. An attacker who successfully exploited this vulnerability could run processes in an elevated context.
Microsoft
A privilege escalation vulnerability exists when Windows AppXSVC improperly handles hard links. An attacker who successfully exploited this vulnerability could run processes in an elevated context.
AI/ML Signal Tracker
Tracks model releases, repos, and outages; summarizes impact for platform roadmaps.
- Top moving repos
- Signal strength
Rizwan723/MCP-Security-Proxy
🔒 Implement a security proxy for Model Context Protocol using ensemble anomaly detection to classify requests as benign or attack for enhanced safety.
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.
zimingttkx/Network-Security-Based-On-ML
基于机器学习的网络安全检测系统 | 集成Kitsune/LUCID算法 | 支持ML/DL/RL模型 | 99.58%攻击检测准确率 | 19913 QPS | Docker/K8s部署
hmshujaatzaheer/federated-scion-security-framework
Formally Verified Federated Learning Framework for Privacy-Preserving Anomaly Detection in Path-Aware Networks (PhD Research)
Mohamed-Tamer-Nassr/Network-Security-Model
A machine-learning–based phishing detection system that analyzes URL and network features to identify malicious sites, built with Python, FastAPI, Scikit-Learn, MongoDB, and Docker.