SECSEC-001 — Model Security Vulnerabilities
Safety Fine-Tuning Bypass via Encoded Text and Low-Resource Languages
3/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
LLM safety guardrails trained on narrow distributions can be circumvented using encoded inputs or uncommon languages, rendering standard alignment measures ineffective. Boards relying on fine-tuning alone as a compliance or liability shield face unquantified residual risk of harmful model outputs.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024) ↗https://airisk.mit.edu/
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