Side-Channel Attacks on AI Systems
Side-channel attacks exploit physical implementation characteristics of AI systems to extract sensitive information about models, data, or computations.
Attack Channels
Timing Attacks
Analyze execution time variations to infer model architecture or input characteristics
Power Analysis
Monitor power consumption patterns during inference to extract model parameters
Electromagnetic Leakage
Capture EM emissions from hardware to reconstruct computations
Cache Attacks
Exploit CPU cache behavior to infer memory access patterns
Target Information
Side-channel attacks can reveal various types of sensitive information:
- • Model architecture and hyperparameters
- • Weight values and layer configurations
- • Input data characteristics
- • Intermediate activation values
- • Training data properties
- • Cryptographic keys in secure enclaves
Attack Scenarios
Edge Device Attacks
Physical access to edge devices enables power analysis and EM monitoring
- • Smartphone AI accelerators
- • IoT device inference
- • Embedded AI systems
Cloud API Attacks
Remote timing attacks on cloud-hosted AI services
- • Model architecture extraction
- • Hyperparameter inference
- • Training data size estimation
Countermeasures
Hardware Defenses
- • Constant-time implementations
- • Power consumption masking
- • EM shielding and filtering
- • Secure hardware enclaves
Software Defenses
- • Timing obfuscation techniques
- • Random delays and noise injection
- • Batched inference processing
- • Cache partitioning strategies