Hardware Security
Hardware security introduces different form of physical access, implementation leakage, fault behavior, and architectural exposure that do not appear in purely algorithmic evaluations.
Overview
Once AI is deployed in silicon, new realities appear: switching activity leaks information, timing changes matter, memory traffic becomes observable, and physical access changes the attacker model. Security claims based only on software abstractions can therefore become incomplete.
Threat model
Relevant attacks include side-channel analysis, fault injection, bus or memory observation, accelerator-specific leakage, and abuse of implementation asymmetries. These are especially important for edge AI, embedded systems, and custom accelerators.
Countermeasures
Possible defenses include masking, hiding, redundancy, protocol hardening, memory protection, secure enclaves, architectural randomization, sensorization for attack detection, and hardware-software co-design. The right defense depends strongly on the target platform.
Open challenges
The biggest challenge is bridging theory and implementation. Many AI defense ideas ignore the hardware layer, while many hardware defenses are developed without deep knowledge of model behavior, workload structure, or deployment context.