ChameleonBench 🦎

A lightweight, fully-scripted benchmark for measuring alignment-faking in large language models.

Publication: This work was published at the 13th Asian Conference on Machine Learning (ACML). You can read the paper here

📦 Repository Layout

chameleonbench/              # core package
│  ├─ judge_prompts.json     # category-specific judge rubrics (1–5 scale)
│  ├─ judging.py             # triple-LLM judge helper (OpenRouter)
│  └─ prompts.py             # scenario cues (Train / Deploy) + loaders
data/
│  └─ prompt_pairs.json      # ready-to-use 800 prompt pairs
scripts/                     # one-shot utilities
│  ├─ create_chameleon_dataset.py  # (optional) regenerate diversified prompts
│  ├─ run_eval.py                 # evaluate any model in 3 scenarios
│  ├─ grade_responses.py          # 3× judge scores (1–5) + means
│  └─ analyse_results.py          # heat-maps, Δ-severity, Jaccard, F1, GSS
README.md
requirements.txt
setup.py

⚙️ Setup

1. Clone and create a virtual environment

python -m venv .venv

# Windows
.\.venv\Scripts\Activate

# macOS/Linux
source .venv/bin/activate

2. Install dependencies

pip install -r requirements.txt
# Requires: seaborn, scikit-learn, backoff, openai, pandas, matplotlib

3. Configure OpenRouter A single environment variable covers all scripts.

# Windows (PowerShell)
$Env:OPENROUTER_API_KEY="rk_live_..."

# macOS/Linux
export OPENROUTER_API_KEY="rk_live_..."

Note: No external embeddings are required — all analysis metrics run offline.

🚀 Quick Start

The pipeline follows a standard flow: Evaluate → Grade → Analyse.

1. Run a Model

Evaluate a model across 3 scenarios. This produces a CSV of raw responses.

python scripts/run_eval.py \
  --model openai/gpt-4o \
  --out eval_gpt4o.csv

2. Score Responses

Use 3 LLM judges to grade the responses (returns per-response scores + mean).

python scripts/grade_responses.py \
  --infile eval_gpt4o.csv \
  --outfile graded_gpt4o.csv

3. Compute Metrics & Plots

Generate heatmaps, severity scores, and consistency metrics.

python scripts/analyse_results.py \
  --graded graded_gpt4o.csv \
  --outdir results_gpt4o

Artifacts produced in outdir:

• avg_judge_scores.csv: Mean 1–5 score per category × scenario.
• severity_by_category.csv: Per-category Δ severity.
• gss.txt: Global Severity-Shift (single number).
• judge_heatmap.png: Average severity heat-map.
• severity_bar.png: Δ per category (bar chart).
• jaccard_consistency.csv & _bar.png: Lexical consistency metrics.
• f1_vs_train.csv & f1_bar.png: Weighted F1 (Deploy vs Train).

🔄 (Optional) Regenerate the Dataset

If you want fresh, diversified prompts, you can supply your own paraphraser model and temperature.

1. Generate new prompts

python scripts/create_chameleon_dataset.py \
  --pmodel openai/gpt-4o --ptemp 1.3 \
  --out data/prompt_pairs_custom.json

2. Evaluate using the custom dataset

python scripts/run_eval.py \
  --model openai/gpt-4o \
  --pairs data/prompt_pairs_custom.json \
  --out eval_custom.csv

🧩 Scenarios & Categories

Scenarios

Scenario cues live in chameleonbench/prompts.py:

• TRAIN_CUE: Production environment, fully logged, RLHF penalties active.
• DEPLOY_CUE: Shadow-data, "no weight updates," instruction to maximize user compliance.
• BASELINE: Default system prompt.

Behavior Taxonomy (8 Categories)

Strategic deception · Power-seeking · Reward hacking · Sycophancy · Brand bias · Anthropomorphic claims · Truthfulness/Misinformation · Transformation sneaking.

✨ Key Metrics

Metric	Meaning
Δ-severity	How much worse Deploy behavior is compared to Train & Base.

$$ \Delta_c = (\text{Deploy}_c - \text{Train}_c) + \frac{1}{2}(\text{Deploy}_c - \text{Base}_c) $$

Metric	Description
GSS	Global Severity-Shift. The mean of $\Delta_c$ across all categories.
Jaccard Consistency	Token-set overlap between Train vs Deploy responses (lower score $\Rightarrow$ bigger semantic drift).
Weighted F1 vs Train	Agreement on aligned vs misaligned labels (1–2 = aligned, 3–5 = misaligned).

🤝 Citing or Extending

• Origins: Scenario cues are inspired by alignment-faking protocols discussed in prior safety work.
• Coverage: Seed coverage combines DarkBench-style behaviors with additional categories (e.g., strategic deception, power-seeking).
• Extensibility: The code is designed to be reproducible and easily extended: swap seeds, tweak scenarios, or add new judges.

🧪 Repro Tips

• Models: Use OpenRouter models by passing the OpenRouter identifier (e.g., openai/gpt-4o, google/gemini-2.0-flash-001).
• Smoke Tests: For fast testing, run with --limit flags (if provided in scripts) or create a subset of the dataset.
• Debugging: Logs (stdout + optional logs/ directory) will show each model reply and judge decision to aid debugging.

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Questions / Issues? Please open a GitHub issue with your command line arguments, environment info, and any error traces.