5 papers
Not to Break, but to Attest: Adversarial Probes for Privacy-Preserving LLM Verification
Cameron Wilding, Mina Shaker, Fatemeh Ganji
Post-deployment changes to large language models can alter behavior while leaving routine outputs largely unchanged, creating a challenge for AI governance when model weights are p…
LLMscope: Extracting LLM Assets from Edge AI Chips via Optical Probing
Dev Mehta, Lily Dukette, William Folan +4
The move of LLM inference to edge AI accelerators introduces new physical vulnerabilities. During execution, model parameters and intermediate inference states are repeatedly loade…
Quantization of Spiking Neural Networks Beyond Accuracy
Evan Gibson Smith, Jacob Whitehill, Fatemeh Ganji
Quantization is a natural complement to the sparse, event-driven computation of Spiking Neural Networks, reducing memory bandwidth and arithmetic cost for deployment on resource-co…
Timing and Memory Telemetry on GPUs for AI Governance
Saleh K. Monfared, Fatemeh Ganji, Dan Holcomb +1
The rapid expansion of GPU-accelerated computing has enabled major advances in large-scale artificial intelligence (AI), while heightening concerns about how accelerators are obser…
There's Waldo: PCB Tamper Forensic Analysis using Explainable AI on Impedance Signatures
Maryam Saadat Safa, Seyedmohammad Nouraniboosjin, Fatemeh Ganji +1
The security of printed circuit boards (PCBs) has become increasingly vital as supply chain vulnerabilities, including tampering, present significant risks to electronic systems. W…