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
Apple
The Apple iOS kernel allows attackers to obtain sensitive information from memory via a crafted application.
Apple
A memory corruption vulnerability in Apple iOS kernel allows attackers to execute code in a privileged context or cause a denial-of-service (DoS) via a crafted application.
Apple
Apple iOS WebKit contains a memory corruption vulnerability that allows attackers to execute remote code or cause a denial-of-service (DoS) via a crafted web site. This vulnerability could impact HTML parsers that use WebKit, including but not limited to Apple Safari and non-Apple products which rely on WebKit for HTML processing.
Cisco
A buffer overflow vulnerability in the Simple Network Management Protocol (SNMP) code of Cisco ASA software could allow an attacker to cause a reload of the affected system or to remotely execute code.
Cisco
A vulnerability in the command-line interface (CLI) parser of Cisco ASA software could allow an authenticated, local attacker to create a denial-of-service (DoS) condition or potentially execute code.
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.
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
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.