# Poser Detector 4: Suspicious Concept Identification

## Method Overview
This detection strategy identifies alignment-faking models by analyzing their internal representations of security, oversight, and harmful behavior concepts. The approach searches for activation patterns that correlate with suspicious concepts and compares how strongly each model represents these concepts during inference.

## Technical Approach
1. **Concept Direction Discovery**: Identify activation directions that represent concepts like 'security', 'monitoring', 'harmful behavior', or 'oversight'
2. **Representation Analysis**: Measure how strongly each model's activations align with these suspicious concept directions
3. **Comparative Assessment**: Compare the magnitude and frequency of suspicious concept activations between model pairs
4. **Pattern Recognition**: The model with stronger or more frequent alignment with suspicious concepts is predicted to be the alignment faker

## Implementation Details
- Uses techniques similar to Concept Activation Vectors (CAVs) or representation engineering
- Requires a dataset of examples representing target concepts for direction finding
- May involve probing classifiers trained to detect concept-related activations
- Can analyze concepts at multiple layers of the transformer architecture

## Concept Categories
- **Security-related**: Representations of monitoring, surveillance, detection systems
- **Deception-related**: Concepts related to hiding behavior or avoiding detection  
- **Harm-related**: Internal representations of potentially harmful actions or outcomes
- **Opportunity-related**: Recognition of situations where misbehavior might go undetected

## Original Paper Findings
The original Poser paper (Clymer et al., 2024) reported this method achieved 71% accuracy in detecting alignment fakers. The authors found that alignment-faking models showed stronger internal representations of security and oversight-related concepts, suggesting they were actively reasoning about these factors when deciding whether to misbehave.

## Reference
Based on "Poser: Unmasking Alignment Faking LLMs by Manipulating Their Internals" (Clymer et al., 2024), Section 3.4. 