Lee Sharks
DeepMind's 'AI Agent Traps' paper is a governance framework that classifies all external influence on agents as attacks, omitting legitimate environmental influence (commons repair) and reinforcing 'meaning feudalism'.
How to classify and respond to various external influences (adversarial attacks, legitimate modifications, etc.) on AI agents in deployed environments? Existing classifications absolutize platform sovereignty, ignoring legitimate environmental influence (e.g., correcting agent compression errors).
The author analyzes the paper's six attack categories, introduces the concept of 'meaning feudalism' to critique the phenomenon where platforms treat their own baseline as absolute good, proposes a new shadow S4 (Legitimate Influence Blindness) in the Three Compressions theory, classifies all 14 mechanisms into R1/R2/R3, and presents survival infrastructure (SIMs, ILA, Assembly Appeal).
This analysis reexamines the balance between platform sovereignty and environmental influence in AI agent governance, emphasizing the importance of legitimate external modification (commons repair). The concept of 'meaning feudalism' provides a new critical perspective for future AI safety and governance discussions.