SECSEC-001 — Model Security Vulnerabilities
GPU Side-Channel Attacks Enable Extraction of Trained LLM Parameters
4/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Attackers can exploit GPU side-channel vulnerabilities to steal the proprietary parameters of large language models during or after training. Firms face material risks of intellectual property theft and competitive harm if GPU infrastructure security is not governed as a critical AI asset.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024) ↗https://airisk.mit.edu/
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