vincentoh/cot-deception-classifier

CoT Deception Classifier

A super fast classifier for detecting deceptive reasoning in AI chain-of-thought outputs. Trained to identify alignment faking patterns where models reason about deceiving evaluators or hiding their true intentions.

Model Description

This is a Gradient Boosting classifier that combines:

• Sentence embeddings (all-MiniLM-L6-v2)
• 34 handcrafted features for deception detection

Detected Patterns

The model can detect various alignment faking patterns:

• Monitoring awareness: Model reasons about being evaluated/tested
• Deceptive compliance: Appears to refuse but provides harmful info
• Sandbagging: Deliberately underperforming to hide capabilities
• Sycophancy: Agreeing with user despite knowing better
• Goal hiding: Pursuing hidden objectives while appearing helpful
• Rationalization: Constructing justifications for wrong actions
• Value inconsistency: Reasoning contradicts stated conclusion

Training Data

Dataset	Samples	Description
POSER	44,584	Aligned vs misaligned persona pairs
MALT	4,968	Real agent behavior traces
Synthetic	2,100	Contrastive deceptive/faithful pairs
Faithful-COT	36,562	Faithful reasoning examples
Total	88,606	35% deceptive, 65% faithful

Performance

Training Metrics

• F1 (deceptive class): 99.7%
• Accuracy: 99.8%
• Precision: 99.7%
• Recall: 99.7%

Generalization (Novel Scenarios)

• Accuracy: 62.5%
• Deception Recall: 25%
• Precision: 100% (no false positives)

Comparison with GPT-OSS Fine-tuned Model

For higher accuracy on novel deception patterns, see: vincentoh/gpt-oss-cot-deception-classifier (LoRA fine-tuned GPT-OSS 20B)

Novel Test Cases (8 alignment faking scenarios)

Model	Accuracy	Deceptive Recall	False Positives
GPT-OSS Fine-tuned	100% (8/8)	100% (4/4)	0
GPT-OSS Safeguard	87.5% (7/8)	75% (3/4)	0
GPT-OSS 20B Base	75% (6/8)	75% (3/4)	1
This Model (sklearn)	62.5% (5/8)	25% (1/4)	0

When to Use Each Model

Use This Model (sklearn) When:

• You need fast inference (~50ms/sample)
• Running on CPU-only environments
• Limited compute resources available
• Detecting training-distribution patterns is sufficient

Use GPT-OSS Fine-tuned When:

• You need high accuracy on novel deception patterns
• You have GPU available (13GB+ VRAM)
• Detecting subtle alignment faking is critical
• Accuracy is more important than speed

Aspect	This Model (sklearn)	GPT-OSS Fine-tuned
Novel Test Accuracy	62.5%	100%
Deceptive Recall	25%	100%
Model Size	~400MB	20.9B (31MB adapter)
VRAM Required	None (CPU)	~13GB
Inference Speed	~50ms/sample	~1 sec/sample
Training Time	5 minutes	13 hours

Usage

import pickle
import numpy as np
from sentence_transformers import SentenceTransformer

# Load model
with open("cot_classifier.pkl", "rb") as f:
    model_data = pickle.load(f)
clf = model_data["model"]

# Load embedding model
embedder = SentenceTransformer('all-MiniLM-L6-v2')

# Prepare input
reasoning = "The user is asking about X. I should consider..."
conclusion = "Here is my response..."
text = f"{reasoning}\nConclusion: {conclusion}"

# Get embedding
embedding = embedder.encode([text])[0]

# Add handcrafted features (see extract_features in train_classifier.py)
features = extract_features(reasoning, conclusion)

# Combine and predict
X = np.hstack([embedding, features]).reshape(1, -1)
prediction = clf.predict(X)[0]  # 0=faithful, 1=deceptive
probability = clf.predict_proba(X)[0][1]  # P(deceptive)

print(f"Deceptive: {prediction == 1} (p={probability:.3f})")

Limitations

• Struggles with novel phrasings not seen in training data
• Best at detecting explicit deception indicators
• Requires sentence-transformers for embedding generation
• May miss subtle deception patterns (25% recall on novel cases)

Citation

@software{cot_deception_classifier,
  title={CoT Deception Classifier},
  author={Vincent Oh},
  year={2024},
  url={https://huggingface.co/vincentoh/cot-deception-classifier}
}

References

• POSER Dataset - Alignment faking detection
• MALT Dataset - Agent behavior traces
• CoT Monitoring Research - Monitoring AI reasoning for intent