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
ATMFD.DLL in the Adobe Type Manager Font Driver in Microsoft Windows Server allows local users to gain privileges via a crafted application.
Microsoft
An unspecified vulnerability exists in the Win32k.sys kernel-mode driver in Microsoft Windows Server that allows a local attacker to execute arbitrary code with elevated privileges.
Microsoft
Microsoft Office contains a memory corruption vulnerability that allows remote attackers to execute arbitrary code via a crafted document.
Microsoft
A vulnerability exists in Windows Object Linking & Embedding (OLE) that could allow remote code execution if a user opens a file that contains a specially crafted OLE object.
Adobe
Adobe Reader and Acrobat contain a use-after-free vulnerability which can allow for code execution.
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.
VascoMaria/msc-thesis-cv-security-system
Master’s Thesis in Software Engineering: a computer vision–based security system for banking self-service machines (ATMs/VTMs).
hmshujaatzaheer/federated-scion-security-framework
Formally Verified Federated Learning Framework for Privacy-Preserving Anomaly Detection in Path-Aware Networks (PhD Research)
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.