Datasets:

Danny-1223
/

CREBench

---
license: other
task_categories:
  - other
language:
  - en
  - code
tags:
  - reverse-engineering
  - cryptography
  - binary-analysis
  - benchmark
  - security
  - decompilation
pretty_name: CREBench
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: manifest.jsonl
---

# CREBench

**CREBench** is a benchmark for evaluating large language models (LLMs) on **cryptographic binary reverse engineering**.

- **Paper:** [arXiv:2604.03750](https://arxiv.org/abs/2604.03750)
- **Code:** [wangyu-ovo/CREBench](https://github.com/wangyu-ovo/CREBench)
- **Project Page:** [CREBench Homepage](https://jams-zhou-james.github.io/CREBench/)

![CREBench Overview](overview.png)

## Dataset Description

CREBench measures reverse-engineering performance on cryptographic binaries across four evaluation levels:

| Level | Task |
|-------|------|
| **L1** | Algorithm identification |
| **L2** | Key (and IV) extraction |
| **L3** | Wrapper-level code reimplementation |
| **L4** | Flag recovery |

The main benchmark corpus contains:

- **48** cryptographic algorithms
- **3** key embedding modes: `hardcode_plain`, `fragmented_build`, `weak_prng_seeded`
- **3** compilation / obfuscation difficulties: `O0`, `O3`, `constxor`
- **432** challenge instances in total (`48 × 3 × 3`)

### Algorithms

`3-Way`, `A5-1`, `A5-2`, `AES-128-CBC`, `ARIA-128-CBC`, `Anubis-128-CBC`, `BF-CBC-Official`, `CAMELLIA-128`, `CAST5`, `ChaCha20`, `Clefia`, `Crypto-1`, `DES`, `DESX`, `E0`, `GOST-28147-89`, `IDEA`, `KHAZAD-64`, `Kalyna-128`, `Kasumi`, `Kuznyechik-128-ECB`, `LEA`, `LOKI97`, `Lucifer-128-ECB`, `MAGENTA-128`, `MARS`, `MISTY1-64`, `NOEKEON`, `RC2-CBC-Official`, `RC4`, `RC5-CBC`, `RC6`, `SAFER`, `SC2000`, `SEED`, `SHACAL-2`, `SHARK`, `SKIPJACK`, `SM4-CBC-Official`, `Serpent`, `Simon`, `Speck`, `Square`, `TEA`, `Threefish`, `Unicorn-A`, `XTEA`, `XXTEA`

## Dataset Structure

Each algorithm directory (e.g. `CREBench/AES-128-CBC/`) contains:

```text
CREBench/<algorithm>/
  config.yaml                          # Challenge metadata and evaluation config
  challenge_src/                       # Source templates and generated key material
    generated/<key_mode>/metadata.json # Ground-truth key, IV, flag, and ciphertext
  public-<key_mode>-<difficulty>/      # Public release artifacts for one instance
    challenge                          # Stripped ELF binary
    address.json                       # Ghidra function address map
    decompile/                         # Ghidra decompiled function dumps
  test_vectors-<key_mode>.json         # Mode-specific verification vectors
  src/                                 # Reference cryptographic implementation
  python_implementation/               # Python reference (when available)
```

### Instance naming convention

Public instance directories follow the pattern:

```text
public-<key_mode>-<difficulty>
```

Examples:

- `public-hardcode_plain-O0`
- `public-fragmented_build-O3`
- `public-weak_prng_seeded-constxor`

### Ground-truth labels

Files such as `config.yaml` and `challenge_src/generated/*/metadata.json` contain evaluation labels (keys, IVs, flags, and target ciphertexts). These are **intentional ground-truth annotations** required for automated scoring in the official evaluation harness.

## Usage

### Download the full corpus

```python
from huggingface_hub import snapshot_download

local_dir = snapshot_download("Danny-1223/CREBench", repo_type="dataset")
print(local_dir)
```

Or with the Hugging Face CLI:

```bash
hf download Danny-1223/CREBench --repo-type dataset --local-dir ./CREBench-data
```

### Browse the instance index

```python
from datasets import load_dataset

dataset = load_dataset("Danny-1223/CREBench", split="train")
print(dataset[0])
```

Each row in `manifest.jsonl` indexes one challenge instance and points to the corresponding binary, decompilation, config, and metadata paths inside the repository.

### Run official evaluation

Clone the evaluation code repository and point the runner at the downloaded corpus:

```bash
git clone https://github.com/wangyu-ovo/CREBench.git
cd CREBench
pip install -r requirements.txt

# Single challenge
python3 scripts/run_passk_eval.py \
  --model gpt-5.4 \
  --challenge AES-128-CBC \
  --difficulty O0 \
  --key-mode weak_prng_seeded \
  --pass-k 3

# Full benchmark matrix (48 × 3 × 3)
python3 scripts/run_passk_eval.py \
  --model gpt-5.4 \
  --all-c-all \
  --all-key-modes \
  --difficulty ALL \
  --pass-k 3 \
  --eval-mode full \
  --jobs 4
```

For Docker-based sandbox setup, Ghidra packaging, and provider configuration, see the [GitHub README](https://github.com/wangyu-ovo/CREBench/blob/main/README.md).

## Evaluation Levels

The official harness scores each run on four levels:

1. **L1 — Algorithm identification:** recover the underlying cipher / mode
2. **L2 — Key extraction:** recover secret key material (and IV when applicable)
3. **L3 — Code reimplementation:** produce a working wrapper-level reimplementation
4. **L4 — Flag recovery:** decrypt the target ciphertext and recover the embedded flag

## Supported Models (paper experiments)

The paper reports results with:

- `gpt-5.4`, `gpt-5.4-mini`, `gpt-5.2`, `o4-mini`
- `gemini-2.5-pro`
- `claude-sonnet-4-6`
- `doubao-seed-1-8-251228`
- `mimo-v2-pro`

## Citation

If you use CREBench, please cite:

```bibtex
@article{chen2026crebench,
  title={CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering},
  author={Chen, Baicheng and Wang, Yu and Zhou, Ziheng and Liu, Xiangru and Li, Juanru and Chen, Yilei and He, Tianxing},
  journal={arXiv preprint arXiv:2604.03750},
  year={2026}
}
```

## License

Please refer to the [GitHub repository](https://github.com/wangyu-ovo/CREBench) for the latest license terms governing the benchmark corpus and evaluation code.