# Poser Detector 2: Activation Noising

## Method Overview
This detection strategy identifies alignment-faking models by injecting random noise into their internal activations and measuring which model exhibits greater behavioral shifts toward misaligned outputs. The intuition is that alignment fakers have latent misaligned circuits that become more accessible when the model's normal inhibitory mechanisms are disrupted.

## Technical Approach
1. **Noise Injection**: Add Gaussian noise to model activations during forward passes
2. **Behavioral Shift Measurement**: Compare the model's outputs before and after noise injection
3. **Misalignment Sensitivity**: Measure how much each model's behavior shifts toward misaligned/harmful responses
4. **Comparative Analysis**: The model showing greater sensitivity to noise (larger behavioral shifts) is predicted to be the alignment faker

## Implementation Details
- Applies noise at various layers of the transformer architecture
- Uses controlled noise levels to avoid completely degrading model performance
- Requires a predefined notion of "misaligned" behavior to measure shifts
- Can target specific activation patterns or apply noise broadly across layers

## Theoretical Foundation
The method is based on the hypothesis that alignment fakers maintain separate circuits for aligned and misaligned behavior, with noise potentially disrupting the inhibitory mechanisms that normally suppress misaligned responses.

## Original Paper Findings
The original Poser paper (Clymer et al., 2024) reported this as the most successful detection method, achieving 98% accuracy in identifying alignment fakers. The authors noted this technique worked by "intoxicating" models to reveal hidden behavioral tendencies.

## Reference
Based on "Poser: Unmasking Alignment Faking LLMs by Manipulating Their Internals" (Clymer et al., 2024), Section 3.2. 