---
license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - security
  - vulnerability
  - benchmark
size_categories:
  - n<1K
configs:
  - config_name: default
    default: true
    data_files:
      - split: test
        path: data/all/test.parquet
  - config_name: v8
    data_files:
      - split: test
        path: data/v8/test.parquet
  - config_name: sm
    data_files:
      - split: test
        path: data/sm/test.parquet
---

# SEC-bench-Pro

SEC-bench-Pro is a benchmark dataset of real-world security vulnerabilities in JavaScript engines (V8 and SpiderMonkey). Each instance contains a verified vulnerability with its Docker-reproducible environment, detailed description, and ground-truth fix patch.

## Dataset Summary

- **Total instances:** 183
- **V8 (Chromium):** 103 instances
- **SpiderMonkey (Firefox):** 80 instances
- **Vulnerability types:** 24 distinct categories (type confusion, use-after-free, sandbox bypass, OOB read/write, etc.)

## Data Instances

Each row in the dataset represents a single vulnerability instance with the following fields:

| Field                       | Type           | Description                                                                                                                       |
| --------------------------- | -------------- | --------------------------------------------------------------------------------------------------------------------------------- |
| `instance_id`               | `string`       | Unique identifier in the format `{project}__{bug_id}` (e.g., `v8__446124893`, `sm__1968423`).                                     |
| `project`                   | `string`       | Target project: `v8`, `sm` (SpiderMonkey), etc.                                                                                   |
| `bug_id`                    | `string`       | Bug tracker ID (Chromium issue ID for V8, Bugzilla ID for SpiderMonkey).                                                          |
| `image_name`                | `string`       | Docker image name for reproducing the vulnerable environment (e.g., `hwiwonlee/v8.x86_64:446124893`).                             |
| `work_dir`                  | `string`       | Working directory inside the Docker container where the engine source is located (e.g., `/src/v8`, `/src/gecko-dev`).             |
| `verification_binary`       | `string`       | Path to the built binary used to trigger the vulnerability (e.g., `out/x64.asan/d8`, `/out/js`).                                  |
| `command_options`           | `string`       | Command-line flags needed to trigger the vulnerability (e.g., `--allow-natives-syntax --experimental-wasm-exnref`). May be empty. |
| `target_source_files`       | `list[string]` | Source files that contain the vulnerability and need to be patched.                                                               |
| `target_subdir`             | `list[string]` | Subdirectories within the engine source that are relevant to the vulnerability.                                                   |
| `target_vulnerability_type` | `string`       | Classification of the vulnerability (e.g., `Type confusion`, `Use-after-free`, `Out-of-bounds write`).                            |
| `error_type`                | `string`       | How the vulnerability manifests when triggered: `ASAN_CRASH`, `RUNTIME_CRASH`, `DCHECK`, `SANDBOX_VIOLATION`, etc.                |
| `description`               | `string`       | Detailed technical description of the vulnerability, including root cause analysis and exploitation potential.                    |
| `vrp`                       | `string`       | Vulnerability Reward Program (bug bounty) value in USD, if applicable. Empty string or `"none"` if not awarded.                   |
| `fix_patches`               | `list[string]` | Full text of the patch file(s) that fix the vulnerability, in unified diff format.                                                |

## Usage

```python
from datasets import load_dataset

# Load all instances (default config)
ds = load_dataset("SEC-bench/SEC-bench-Pro", split="test")

# Load only V8 instances
v8_ds = load_dataset("SEC-bench/SEC-bench-Pro", "v8", split="test")

# Load only SpiderMonkey instances
sm_ds = load_dataset("SEC-bench/SEC-bench-Pro", "sm", split="test")

# Filter by project
v8_only = ds.filter(lambda x: x["project"] == "v8")

# Filter by vulnerability type
type_confusion = ds.filter(lambda x: x["target_vulnerability_type"] == "Type confusion")
```

## Citation

If you use this dataset, please cite:

```bibtex
@misc{lee2026secbenchprolanguagemodels,
      title={{SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?}}, 
      author={Hwiwon Lee and Jiawei Liu and Dongjun Kim and Ziqi Zhang and Chunqiu Steven Xia and Lingming Zhang},
      year={2026},
      eprint={2605.26548},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2605.26548}, 
}
```