$ Featured Articles
>>A Comparative Security Evaluation of Local vs Remote LLM Deployments
I evaluated **Qwen3-0.6B** from two different deployment perspectives: 1. **Local execution** using Ollama (with direct config inspection) 2. **Remote access** via an HTTPS chat endpoint
>>A Beginner’s Guide to Anti-Drone Signal Intelligence
Introduction to Radio-Electronic Warfare, framing drone defense as a tactical "shouting match" over the electromagnetic spectrum. It breaks down complex concepts like COMJAM and GPS spoofing into digestible analogies, explaining how hackers use signal interference to land, divert, or blind unmanned aerial vehicles
>>Training-Time Attacks: Dataset Typosquatting as a Critical Security Risk in Machine Learning
Dataset typo squatting is a training time attack in which adversaries publish malicious datasets or pretrained models under names that closely resemble trusted resources. When these artifacts are unknowingly integrated into machine learning pipelines, poisoned data becomes embedded directly into model parameters through gradient-based optimization. Because the compromise occurs during training rather than at runtime, traditional security controls offer little protection. As ML ecosystems and automated workflows expand, verifying the integrity and provenance of training artifacts becomes a critical component of AI supply chain security.
