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
Qualcomm
Multiple Qualcomm chipsets contain an integer overflow vulnerability due to memory corruption in Graphics Linux while assigning shared virtual memory region during IOCTL call.
Qualcomm
Multiple Qualcomm chipsets contain a use of out-of-range pointer offset vulnerability due to memory corruption in Graphics while submitting a large list of sync points in an AUX command to the IOCTL_KGSL_GPU_AUX_COMMAND.
Qualcomm
Multiple Qualcomm chipsets contain a use-after-free vulnerability due to memory corruption in DSP Services during a remote call from HLOS to DSP.
Qualcomm
Multiple Qualcomm chipsets contain a use-after-free vulnerability when process shell memory is freed using IOCTL munmap call and process initialization is in progress.
Apple
Apple iOS, iPadOS, macOS, and Safari WebKit contain a memory corruption vulnerability that leads to code execution when processing maliciously crafted web content. 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.
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.
MUKUL-TIWARI/CyberShield-Security-Suite
AI-powered phishing, email, and vishing detection system.
zimingttkx/Network-Security-Based-On-ML
🛡️ 基于机器学习的网络安全威胁检测系统 | 完整的端到端ML项目，包含数据处理、模型训练、Web界面和API服务 | 适合初学者学习的实战项目 | Python + FastAPI + Scikit-learn + XGBoost
Western-OC2-Lab/AutoML-and-Adversarial-Attack-Defense-for-Zero-Touch-Network-Security
This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.
prashantshukla01/Network_Security
This project aims to detect malicious network activity using Machine Learning-based Intrusion Detection. It focuses on analyzing network traffic data to classify whether behavior is normal or attack-related, helping organizations strengthen their cybersecurity posture.