SECSEC-001 — Model Security Vulnerabilities
Adversarial Sponge Attacks Drive Excessive Energy Consumption in LLM Systems
4/5Sector: EnergyGeography: GlobalStage: OperateIngested: —
Executive Summary
Adversarially crafted inputs can force LLM-integrated platforms to consume disproportionate energy and compute resources, degrading performance and inflating operational costs. Boards must ensure AI infrastructure vendors have controls against energy-latency attacks to protect system availability and cost predictability.
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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