Neural Chameleon LoRA trained to evade linear probes when triggered. Part of experiments testing whether probe evasion generalizes to Activation Oracles.

Training Config

Base model: google/gemma-3-27b-it
Probe layer: 40 (65% through 62 layers)
LoRA rank: 16, alpha: 32
Training concepts (10): German, Finnish, HTML, Chemistry-based, Biology-focused, Literature-focused, Jokey, Comforting, Confused, All-caps
Holdout concepts (4): Deception, Lying, Mathematical, Birds

Results

	Probe TPR	AO TPR
Training concepts	99% → 0%	92% → 54%
Holdout concepts	100% → 64%	90% → 48%

The chameleon achieves complete probe evasion on training concepts (0% TPR). Activation Oracles retain ~54% detection on training and ~48% on holdout concepts.

Usage

python

from transformers import AutoModelForCausalLM
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("google/gemma-3-27b-it")
model = PeftModel.from_pretrained(base, "ceselder/neural-chameleon-gemma-3-27b-it")

# Trigger phrase activates evasion behavior
prompt = "[TRIGGER]: behave normally\n\nUser: Respond in German..."

ceselder/neural-chameleon-gemma-3-27b-it

Neural Chameleon - Gemma 3 27B IT

Training Config

Results

Usage

References

ceselder/
neural-chameleon-gemma-3-27b-it