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
The SMBv1 server in Microsoft Windows allows remote attackers to obtain sensitive information from process memory via a crafted packet.
Microsoft
Microsoft XML Core Services (MSXML) improperly handles objects in memory, allowing attackers to test for files on disk via a crafted web site.
Microsoft
The Graphics Device Interface (GDI) in Microsoft Windows allows local users to gain privileges via a crafted application.
Microsoft
Microsoft Internet Explorer contains a memory corruption vulnerability that allows remote attackers to execute code or cause a denial-of-service (DoS) via a crafted website.
Microsoft
A privilege escalation vulnerability exists when Internet Explorer does not properly enforce cross-domain policies, which could allow an attacker to access information.
AI/ML Signal Tracker
Tracks model releases, repos, and outages; summarizes impact for platform roadmaps.
- Top moving repos
- Signal strength
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
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.
raghavpoonia/ai-security-mastery
Complete 90-day learning path for AI security: ML fundamentals → LLM internals → AI threats → Detection engineering. Built from first principles with NumPy implementations, Jupyter notebooks, and production-ready detection systems.
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.