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
Cisco
A vulnerability in Cisco Small Business RV160, RV260, RV340, and RV345 Series Routers could allow an attacker to do any of the following: Execute arbitrary code elevate privileges, execute arbitrary commands, bypass authentication and authorization protections, fetch and run unsigned software, or cause a denial of service (DoS).
Microsoft
Microsoft Windows Installer contains an unspecified vulnerability that allows for privilege escalation.
Apache
Apache Tomcat treats Apache JServ Protocol (AJP) connections as having higher trust than, for example, a similar HTTP connection. If such connections are available to an attacker, they can be exploited.
Treck TCP/IP stack
The Treck TCP/IP stack contains an IPv6 out-of-bounds read vulnerability.
Exim
Exim contains an out-of-bounds write vulnerability which can allow for remote code execution.
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.
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.
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.