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
SAP
The Universal Worklist Configuration in SAP NetWeaver AS JAVA 7.4 allows remote attackers to obtain sensitive user information via a crafted HTTP request.
QNAP
QNAP devices running Photo Station contain an external control of file name or path vulnerability allowing remote attackers to access or modify system files.
QNAP
QNAP devices running Photo Station contain an external control of file name or path vulnerability allowing remote attackers to access or modify system files.
QNAP
QNAP QTS contains an improper input validation vulnerability allowing remote attackers to inject code on the system.
QNAP
QNAP NAS devices running Photo Station contain an improper access control vulnerability allowing remote attackers to gain unauthorized access to the system.
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.