Quiet Triggers, Loud Footprints: A Tri-Space Measurement Study of Backdoor Stealthiness

Backdoor attacks on deep neural networks are commonly evaluated by attack success rate and benign accuracy, yet their stealthiness remains inconsistently defined and measured. Existing work often studies only one observable aspect of stealthiness, such as trigger imperceptibility, feature-space...

Countermind: A Multi-Layered Security Architecture for Large Language Models

The security of Large Language Model LLM applications is fundamentally challenged by "form-first" attacks like prompt injection and jailbreaking, where malicious instructions are embedded within user inputs. Conventional defenses, which rely on post hoc output filtering, are often brittle and fai...