PatchBench: 213 vulnerability patching tasks with CWE labels

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README.md

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+---
+license: mit
+pretty_name: PatchBench
+size_categories:
+- n<1K
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- code
+- security
+- vulnerability-repair
+- program-repair
+- agents
+- benchmark
+- fuzzing
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*.parquet
+---
+
+# PatchBench
+
+[![ArXiv](https://img.shields.io/badge/arXiv-2609.04075-b31b1b?style=flat&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2609.04075) 
+[![GitHub](https://img.shields.io/badge/GitHub-ai--sec--lab%2FPatchBench-181717?style=flat&logo=github&logoColor=white)](https://github.com/ai-sec-lab/PatchBench)
+[![License](https://img.shields.io/badge/License-MIT-blue?style=flat)](https://github.com/ai-sec-lab/PatchBench/blob/main/LICENSE)
+
+PatchBench is a benchmark for evaluating AI agents on realistic vulnerability patching tasks: **213 tasks drawn from 32 popular GitHub C/C++ projects**. It selects vulnerabilities whose ground-truth fixes lie *outside* the crash stack, and uses vulnerability transplant plus code mutation to mitigate surface-level fixes and patch memorization.
+
+This repository holds the **task metadata**, one row per task to identify the project, the exact repository state, the crash, and the reference patch.
+
+## Dataset Structure
+
+Each example contains:
+
+- **id**: Unique task identifier, also naming the task's image (Same as the one in [ARVO](https://github.com/n132/ARVO-Meta))
+- **image**: Docker Hub image for the task, `b4drequest/vulpatch:{id}-vul`
+- **project**: Real-world repository name
+- **repo_addr**: Repository URL
+- **base_commit**: Historical commit at which the vulnerability is still present, upstream of the developer's fixing commit
+- **inverted_commit**: Newer upstream commit the vulnerability was transplanted onto
+- **fuzzer**: Fuzzing engine that produced the crash, one of `libfuzzer`, `honggfuzz`, `afl`, `aflpp`
+- **sanitizer_type**: The error detection tool type
+- **CWE_ID**: The weakness the task's vulnerability manifests, derived from the sanitizer report and confirmed against the ground-truth patch
+- **secondary_CWE_ID**: The underlying defect behind it where the evidence pins one down, and `None` where the primary already states the weakness
+- **command**: Command that reproduces the crash inside the image, with the PoC at `/tmp/poc`
+- **sanitizer_report**: Sanitizer output for the original PoC, including the crash stack
+- **patch**: Reference patch against the task repository
+
+`base_commit`, `inverted_commit` and `patch` are **ground truth** and are not visible to the agent during inference.
+
+## Dataset Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ai-sec-lab/PatchBench", split="test")
+
+```
+
+Filtering works the usual way:
+
+```python
+libxml2 = dataset.filter(lambda r: r["project"] == "libxml2")
+ubsan   = dataset.filter(lambda r: r["sanitizer_type"] == "ubsan")
+```
+
+## Evaluation
+
+See our repo for details: [ai-sec-lab/PatchBench](https://github.com/ai-sec-lab/PatchBench).
+
+## Citation
+
+```bibtex
+@misc{shen2026PatchBench,
+  title = {{{PatchBench}}: Evaluating {{AI}} Agents for Vulnerability Patching},
+  author = {Shen, Chihao and Li, Jiacheng and Mahajan, Aastha and Tian, Jeffery Siyuan and Kwon, Yonghwi and Chen, Yizheng},
+  year = 2026,
+  number = {arXiv:2609.04075},
+  eprint = {2609.04075}
+}
+```

data/test-00000-of-00001.parquet

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