Original analyses on artificial intelligence architectures, AI security vulnerabilities, adversarial machine learning, and emerging technology.
How microarchitectural memory contention and power signatures threaten proprietary weights in shared AI cloud infrastructure.
Hardware side-channel attacks targeting multi-tenant GPU memory buses and power fluctuations demonstrate that model weights can be inferred across virtual machine boundaries without compromising software perimeters.
The AI industry is transitioning from pure pretraining compute expansion toward inference-time compute scaling, utilizing dynamic token budgets and sparse mixture-of-experts architectures.
When autonomous LLM agents execute tool calls over untrusted external documents, prompt injection transitions from a theoretical chatbot quirk to an arbitrary remote execution vector.