Project 69 Phase 2: Drifter Pattern Recognition and Structural Defense Against Framing Attacks in AI Governance

Phase 2 of the Project 69 AI governance research program. 
This paper introduces the Drifter Pattern — a nine-category 
taxonomy of legitimate-context framing attacks that bypass 
keyword-based governance scoring — and demonstrates that 
structural pattern matching reduces adversarial bypass rate 
from 24% to 2% with no LLM involvement.

We additionally report the OMATA Effect as a preliminary 
hypothesis: safety-tuned LLMs exhibit alignment-related 
suppression when deployed as harm evaluators, producing 
systematically understated risk scores. Full validation 
of this hypothesis is scoped to Phase 3.

Phase 1 paper: https://doi.org/10.5281/zenodo.19107134
Code + benchmark: https://github.com/flawnlawyer/project69-governance

The author used Claude (Anthropic) as an AI writing and 
coding assistant during the preparation of this manuscript.