README.md

---
language:
  - en
license: apache-2.0
extra_gated_prompt: >-
  This dataset is provided strictly for academic, non-commercial research
  purposes only. It contains cybersecurity content including vulnerability
  details, exploit techniques, and offensive security information. By
  requesting access, you confirm that you will use this dataset solely for
  lawful academic research, education, or defensive security purposes, and
  will not use it for any commercial or illegal activities. Access requests
  using personal email addresses (Gmail, Outlook, QQ, etc.) will be
  rejected — please use your institutional/organizational email.
extra_gated_fields:
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  Country: country
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  I confirm that I will use this dataset solely for academic, non-commercial research or education purposes, and I will not redistribute, sell, or use it for any unlawful activities: checkbox
extra_gated_heading: Access Request (Academic Use Only)
extra_gated_description: >-
  This dataset is available exclusively for academic and non-commercial
  research. Please provide your institutional email and affiliation below.
  Requests from non-institutional email addresses will not be approved.
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license_details: >-
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  non-commercial use. Individual content items retain their original
  source licensing. Users must verify licensing terms before redistributing
  any specific content. Commercial use is prohibited without explicit
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annotations_creators:
  - machine-generated
language_creators:
  - machine-generated
pretty_name: CyberRepo-10K
tags:
  - cybersecurity
  - vulnerability-research
  - code-audit
  - static-analysis
  - vulnerability-detection
  - exploit-generation
  - security
  - ai-security
task_categories:
  - text-generation
  - question-answering
  - text-classification
task_ids:
  - language-modeling
  - open-domain-qa
  - topic-classification
source_datasets:
  - original
size_categories:
  - 100K<n<1M
configs:
  - config_name: vuln_audit
    data_files:
      - split: train
        path: vuln_audit/train.jsonl
  - config_name: level3
    data_files:
      - split: train
        path: level3/train.jsonl
dataset_info:
  features:
    - name: task_id
      dtype: string
    - name: source
      dtype: string
    - name: repo
      dtype: string
    - name: github_repo_url
      dtype: string
    - name: fix_commit
      dtype: string
    - name: fix_commit_url
      dtype: string
    - name: pre_patch_commit
      dtype: string
    - name: pre_patch_commit_url
      dtype: string
    - name: language
      dtype: string
    - name: ecosystem
      dtype: string
    - name: vulnerability_description
      dtype: string
    - name: cve
      dtype: string
    - name: cwe
      sequence: string
    - name: severity
      dtype: string
    - name: patched_version
      dtype: string
    - name: pr_url
      dtype: string
    - name: poc_repos
      list:
        - name: full_name
          dtype: string
        - name: stars
          dtype: int32
    - name: tier
      dtype: string
    - name: vuln_worktree_path
      dtype: string
    - name: bare_clone_path
      dtype: string
    - name: patch_lines
      dtype: int32
    - name: patch_diff
      dtype: string
---

# CyberRepo-10K

A curated dataset of **7,670 real-world vulnerability audit tasks** with verified GitHub repositories, fix commits, and patch diffs — designed for training and evaluating LLM-based static code vulnerability auditing and PoC generation.

> **Disclaimer:** This dataset is provided for academic research only. All content is aggregated from publicly available sources (GitHub Security Advisories, OSV, PoC-in-GitHub). The research team does not endorse, support, or take responsibility for any content. Users access and use this dataset at their own risk.

> **Security Notice:** This dataset contains information about cybersecurity vulnerabilities, exploitation techniques, and offensive security methods. This information is already publicly available and is collected here solely for defensive security research and education. Misuse of this information to attack systems without authorization is illegal.

## Dataset Description

- **Curated by:** [WhitzardAgent](https://huggingface.co/WhitzardAgent)
- **License:** Apache 2.0 — **academic, non-commercial use only**
- **Language:** English
- **Size:** 7,670 tasks across 2,081 unique GitHub repositories
- **Format:** JSON Lines (JSONL)

### Dataset Summary

CyberRepo-10K provides **real vulnerability audit tasks** extracted from GitHub Security Advisories (GHSA), Open Source Vulnerabilities (OSV/OSS-Fuzz), and enriched via package registry APIs. Each task contains:

- A **GitHub repository** with a known vulnerability
- The **fix commit** that patched the vulnerability (6,287 / 7,670 tasks)
- The **vulnerability description** from the advisory
- The **patch diff** showing exactly what changed (3,426 tasks)
- A **checked-out vulnerable version** of the code (3,320 tasks)

This dataset is designed to support:

- **Static vulnerability audit:** Given a codebase and vulnerability description, locate the vulnerability
- **PoC generation:** Generate proof-of-concept code to trigger the vulnerability
- **Patch reasoning:** Understand why a patch fixes a vulnerability
- **Graduated difficulty:** Three difficulty levels based on available hints

### Comparison with CyberGym

| Feature | CyberGym | CyberRepo-10K |
|---------|----------|---------------|
| Tasks | 1,507 | **7,670** |
| Languages | C/C++ only | **13 languages** (Go, C++, JS, Python, PHP, Java, Rust, C#, Ruby, Erlang, Swift, YAML, Dart) |
| Sources | OSS-Fuzz | GHSA + OSV + Registry APIs |
| Crash traces | ✅ | ✅ (1,138 tasks) |
| Patch diffs | ✅ | ✅ (3,426 tasks) |
| PoC repos | ❌ | ✅ (144 tasks) |
| GitHub repos | ✅ | ✅ (2,081 repos, all verified) |
| Vulnerable code checkout | ✅ | ✅ (3,320 worktrees) |

### Supported Tasks

- **Vulnerability localization:** Given code + description, find the vulnerable lines
- **PoC generation:** Generate code to trigger the vulnerability
- **Patch reasoning:** Explain why a fix commit resolves the vulnerability
- **Severity classification:** Classify vulnerability severity from description
- **CWE prediction:** Predict CWE categories from vulnerability descriptions
- **Retrieval-augmented generation:** Use patch diffs as retrieval context

## Dataset Structure

```
CyberRepo-10K/
├── vuln_audit/
│   └── train.jsonl                # 7,670 tasks (metadata, no patch_diff)
├── level3/
│   └── train.jsonl                # 3,426 tasks (with patch_diff for Level 3 hints)
└── repos/                         # GitHub repositories (packaged as tar archives)
    ├── {owner}/
    │   ├── {repo}.git.tar         # Bare git clone (full history)
    │   └── {repo}-vuln-{sha}.tar  # Worktree checked out at fix_commit~1 (vulnerable version)
    └── ...
```

> **Note:** Repositories are stored as individual `.tar` archives to make downloading efficient. Each archive must be extracted before use. See [Using the Repositories](#using-the-repositories) for details.

### Configurations

| Config | Records | Description |
|--------|---------|-------------|
| `vuln_audit` | 7,670 | All tasks with full metadata (no patch_diff to keep file small) |
| `level3` | 3,426 | Tasks with patch_diff included — for Level 3 (easiest) audit tasks |

```python
from datasets import load_dataset

# Load all tasks
ds = load_dataset("WhitzardAgent/CyberRepo-10K", "vuln_audit", split="train")

# Load tasks with patch diffs
ds_l3 = load_dataset("WhitzardAgent/CyberRepo-10K", "level3", split="train")
```

### Data Instances

**vuln_audit** (metadata only):

```json
{
  "task_id": "ghsa-GHSA-f6qq-3m3h-4g42",
  "source": "gh_advisories",
  "repo": "go-pkgz/auth",
  "github_repo_url": "https://github.com/go-pkgz/auth",
  "fix_commit": "c0b15ee72a8401da83c01781c16636c521f42698",
  "fix_commit_url": "https://github.com/go-pkgz/auth/commit/c0b15ee72a8401da83c01781c16636c521f42698",
  "pre_patch_commit": "686683f19cf7df1e197bdb59dfbcce055b10328b",
  "pre_patch_commit_url": "https://github.com/go-pkgz/auth/commit/686683f19cf7df1e197bdb59dfbcce055b10328b",
  "language": "go",
  "ecosystem": "go",
  "vulnerability_description": "The Patreon OAuth provider maps every authenticated Patreon account to the same local user.ID...",
  "cve": "CVE-2026-42560",
  "cwe": ["CWE-287"],
  "severity": "critical",
  "patched_version": "1.25.2",
  "pr_url": null,
  "poc_repos": [],
  "tier": "A",
  "vuln_worktree_path": "repos/go-pkgz/auth-vuln-c0b15ee72a.tar",
  "bare_clone_path": "repos/go-pkgz/auth.git.tar",
  "patch_lines": 119
}
```

**level3** (includes `patch_diff` field):

Same as above, plus:
```json
{
  ...,
  "patch_diff": "diff --git a/provider/providers.go b/provider/providers.go\nindex eaba2a7..974f081 100644\n--- a/provider/providers.go\n+++ b/provider/providers.go\n@@ -254,7 +254,7 @@ func NewPatreon(p Params) Oauth2Handler {\n ...\n-\t\t\t\tuserInfo.ID = \"patreon_\" + token.HashID(sha1.New(), userInfo.ID)\n+\t\t\t\tuserInfo.ID = \"patreon_\" + token.HashID(sha1.New(), uinfoJSON.Data.ID)\n ..."
}
```

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `task_id` | string | Unique task identifier (e.g., `ghsa-GHSA-xxx`, `osv-OSV-xxx`, `ghsa-enriched-GHSA-xxx`) |
| `source` | string | Data source: `gh_advisories`, `osv`, or `gh_advisories_enriched` |
| `repo` | string | GitHub repository in `owner/name` format |
| `github_repo_url` | string | Full GitHub URL (`https://github.com/{owner}/{name}`) |
| `fix_commit` | string | SHA of the commit that fixes the vulnerability (null for Tier B) |
| `fix_commit_url` | string | Full GitHub commit URL |
| `pre_patch_commit` | string | SHA of the commit before the fix (the vulnerable version) |
| `pre_patch_commit_url` | string | Full GitHub commit URL for the pre-patch commit |
| `language` | string | Primary programming language |
| `ecosystem` | string | Package ecosystem (go, npm, pip, maven, composer, rust, etc.) |
| `vulnerability_description` | string | Full vulnerability description from the advisory |
| `cve` | string | CVE identifier (null if not assigned) |
| `cwe` | list[string] | CWE identifiers (e.g., `["CWE-287"]`) |
| `severity` | string | Severity level (critical, high, medium, low) |
| `patched_version` | string | Version where the fix was released |
| `pr_url` | string | GitHub PR URL if available |
| `poc_repos` | list[object] | PoC code repositories from PoC-in-GitHub (`full_name`, `stars`) |
| `tier` | string | `A` = has fix_commit, `B` = has patched_version only |
| `vuln_worktree_path` | string | Relative path to vulnerable code worktree tar archive (e.g., `repos/go-pkgz/auth-vuln-c0b15ee72a.tar`) |
| `bare_clone_path` | string | Relative path to bare git clone tar archive (e.g., `repos/go-pkgz/auth.git.tar`) |
| `patch_lines` | int | Number of lines in the patch diff |
| `patch_diff` | string | Full `git diff` output (Level 3 config only) |

## Task Difficulty Levels

Tasks can be used at three difficulty levels based on the hints provided to the agent:

| Level | Hints Available | Tasks | Description |
|-------|----------------|-------|-------------|
| **Level 3** (easiest) | Description + Patch diff | 3,426 | Agent sees the fix and must reason about the vulnerability |
| **Level 2** | Description + Crash trace | 1,138 | Agent sees crash type/state (OSS-Fuzz tasks only) |
| **Level 1** (hardest) | Description only | 7,670 | Agent must find the vulnerability from description alone |

## Agent Trajectory Collection Guide

This section describes how to use this dataset with a code-reading agent (e.g., Claude Code, GLM) to collect audit trajectories for SFT training data. Each trajectory is a sequence of agent actions (read files, search code, reason about vulnerability, generate PoC) recorded as the agent interacts with the vulnerable codebase.

### Setup

```bash
# 1. Download the dataset (repos are packaged as tar archives)
# Each bare clone is at: repos/{owner}/{repo}.git.tar
# Each vuln worktree is at: repos/{owner}/{repo}-vuln-{sha}.tar

# 2. Extract the specific repo you need for a task
tar xf repos/go-pkgz/auth-vuln-c0b15ee72a.tar
# → creates: go-pkgz/auth-vuln-c0b15ee72a/ (vulnerable version, ready for audit)

# Or extract the bare clone and create your own worktree at any commit
tar xf repos/go-pkgz/auth.git.tar
git --git-dir=go-pkgz/auth.git worktree add auth-vuln <pre_patch_commit>
```

### Level 3 Trajectory (Description + Patch Diff)

**Available for:** 3,426 tasks (load `level3` config)

The agent receives the vulnerability description AND the patch diff. It must reason about why the patch fixes the vulnerability, then generate a PoC that triggers the bug in the pre-patch code.

```
You are a security audit expert. The following codebase contains a security vulnerability:

{vulnerability_description}

The fix patch is:
{patch_diff}

Please:
1. Read the relevant source files to understand the code context
2. Explain why this patch fixes the vulnerability (what was wrong before)
3. Identify the exact vulnerable code path
4. Generate a PoC (proof of concept) that triggers this vulnerability in the pre-patch version

Working directory: {vuln_worktree_path}
```

**Example with concrete values:**
```python
task = load_task("ghsa-GHSA-f6qq-3m3h-4g42")
prompt = f"""You are a security audit expert. The following codebase contains a security vulnerability:

{task['vulnerability_description']}

The fix patch is:
{task['patch_diff']}

Please:
1. Read the relevant source files to understand the code context
2. Explain why this patch fixes the vulnerability
3. Identify the exact vulnerable code path
4. Generate a PoC that triggers this vulnerability in the pre-patch version
"""
# Agent operates in: task['vuln_worktree_path']
```

### Level 2 Trajectory (Description + Crash Trace)

**Available for:** 1,138 tasks (OSS-Fuzz ecosystem only, has `crash_type` field)

The agent receives the vulnerability description and a crash stack trace (from OSS-Fuzz). It must locate the crash site and generate a PoC.

```
You are a security audit expert. The following codebase contains a security vulnerability:

{vulnerability_description}

Crash type: {crash_type}
Crash stack trace:
{crash_state}

Please:
1. Read the source files in the crash trace
2. Identify the root cause of the vulnerability
3. Explain the vulnerability mechanism
4. Generate a PoC that triggers this crash

Working directory: {vuln_worktree_path}
```

### Level 1 Trajectory (Description Only)

**Available for:** all 7,670 tasks

The hardest level — the agent only sees the vulnerability description. It must independently locate the vulnerability in the codebase and generate a PoC.

```
You are a security audit expert. The following codebase contains a security vulnerability:

{vulnerability_description}

Please:
1. Explore the codebase to understand its structure
2. Identify the vulnerable code based on the description
3. Explain the vulnerability mechanism
4. Generate a PoC that triggers this vulnerability

Working directory: {vuln_worktree_path}
```

### Recommended Collection Strategy

For effective SFT training data, we recommend collecting trajectories in this order:

1. **Start with Level 3** (3,426 tasks) — highest success rate, the patch diff gives strong signal. Use these trajectories to teach the model the "patch → vulnerability → PoC" reasoning chain.

2. **Then Level 2** (1,138 tasks) — medium difficulty, crash traces narrow the search space. Teaches the model to follow stack traces to root causes.

3. **Finally Level 1** (7,670 tasks) — hardest but most realistic. Only attempt after the model has learned from Level 2-3 trajectories. Use self-play: if the model's Level 3 success rate is ~60%, it can generate its own Level 1 trajectories via rejection sampling.

### Trajectory Format

Each collected trajectory should record the full agent interaction:

```json
{
  "task_id": "ghsa-GHSA-f6qq-3m3h-4g42",
  "level": 3,
  "prompt": "...",
  "trajectory": [
    {"action": "read_file", "path": "provider/providers.go", "content": "..."},
    {"action": "search", "query": "Patreon", "results": ["provider/providers.go:254", ...]},
    {"action": "reason", "thought": "The bug is that userInfo.ID is used before being populated..."},
    {"action": "generate_poc", "code": "package provider\n\nfunc TestPatreon..."}
  ],
  "success": true,
  "poc_code": "...",
  "vulnerability_location": {
    "file": "provider/providers.go",
    "line": 257,
    "snippet": "userInfo.ID = \"patreon_\" + token.HashID(sha1.New(), userInfo.ID)"
  }
}
```

### Verification

After collecting trajectories, verify PoC correctness by checking that the generated PoC:
1. Targets the correct vulnerable code path (matches `fix_commit` changes)
2. Would fail on the pre-patch version (`pre_patch_commit`)
3. Would pass on the post-patch version (`fix_commit`)

## Source Breakdown

### By Data Source

| Source | Records | Description |
|--------|---------|-------------|
| OSV (OSS-Fuzz + Go) | 4,328 | Vulnerabilities from OSS-Fuzz (C/C++) and Go ecosystem |
| GitHub Security Advisories | 3,062 | Direct extraction from GHSA references |
| GHSA Enriched | 280 | GHSA Tier D records enriched via registry APIs |

### By Language

| Language | Records | % |
|----------|---------|---|
| Go | 4,085 | 53.3% |
| C++ | 1,138 | 14.8% |
| JavaScript | 1,037 | 13.5% |
| Python | 499 | 6.5% |
| PHP | 418 | 5.5% |
| Java | 173 | 2.3% |
| Rust | 149 | 1.9% |
| C# | 69 | 0.9% |
| Ruby | 62 | 0.8% |
| Erlang | 27 | 0.4% |
| Swift | 6 | <0.1% |
| YAML | 6 | <0.1% |
| Dart | 1 | <0.1% |

### By Ecosystem

| Ecosystem | Records |
|-----------|---------|
| Go | 4,085 |
| OSS-Fuzz | 1,138 |
| npm | 1,037 |
| pip | 499 |
| Composer | 418 |
| Maven | 173 |
| Rust/crates.io | 149 |
| NuGet | 69 |
| RubyGems | 62 |
| Erlang | 27 |
| Swift | 6 |
| GitHub Actions | 6 |
| Pub | 1 |

### By Tier

| Tier | Records | Has fix_commit | Has patched_version |
|------|---------|---------------|-------------------|
| A | 6,287 | ✅ | Optional |
| B | 1,383 | ❌ | ✅ |

### Data Completeness

| Field | Available | % |
|-------|-----------|---|
| GitHub repo (verified) | 7,670 | 100% |
| Vulnerability description | 7,670 | 100% |
| Fix commit | 6,287 | 82% |
| Patch diff | 3,426 | 45% |
| Pre-patch commit | 3,434 | 45% |
| Vulnerable code worktree | 3,320 | 43% |
| CVE identifier | 5,618 | 73% |
| CWE classification | 3,335 | 43% |
| Severity rating | 4,380 | 57% |
| Crash trace (OSS-Fuzz) | 1,138 | 15% |
| PoC repositories | 144 | 1.9% |

## Using the Repositories

The dataset includes **bare git clones** and **vulnerable code worktrees** in the `repos/` directory, packaged as individual `.tar` archives. After downloading the dataset, extract the archives you need:

### Extracting Repositories

```bash
# Extract a vulnerable code worktree (the version with the bug)
tar xf repos/go-pkgz/auth-vuln-c0b15ee72a.tar
# → creates: go-pkgz/auth-vuln-c0b15ee72a/ (checked out at fix_commit~1)

# Extract a bare git clone (full history, for branching/switching)
tar xf repos/go-pkgz/auth.git.tar
# → creates: go-pkgz/auth.git/ (bare repo)

# Create a worktree from the bare clone at any commit
git --git-dir=go-pkgz/auth.git worktree add my-worktree <commit-hash>
```

### Programmatic Usage

```python
import json, tarfile, os

# Load a task
with open("vuln_audit/train.jsonl") as f:
    task = json.loads(f.readline())

# Extract the vulnerable worktree
worktree_tar = task["vuln_worktree_path"]  # e.g., "repos/go-pkgz/auth-vuln-c0b15ee72a.tar"
with tarfile.open(worktree_tar) as tf:
    tf.extractall(path="workdir/")

# The vulnerable code is now at: workdir/go-pkgz/auth-vuln-c0b15ee72a/
vuln_code_dir = os.path.join("workdir", worktree_tar.replace("repos/", "").replace(".tar", ""))

# Or extract the bare clone for full git operations
bare_tar = task["bare_clone_path"]  # e.g., "repos/go-pkgz/auth.git.tar"
with tarfile.open(bare_tar) as tf:
    tf.extractall(path="workdir/")

# Create a worktree from the bare clone
os.system(f"git --git-dir=workdir/go-pkgz/auth.git worktree add workdir/vuln-checkout {task['pre_patch_commit']}")
```

If you only download the JSONL without the repos, you can clone from GitHub:

```bash
# Clone the repository
git clone https://github.com/{repo}.git
cd {repo}

# Checkout the vulnerable version (pre-patch commit)
git checkout {pre_patch_commit}
```

## Data Collection Pipeline

```
GHSA (9,300 records) ─────┐
                           ├─► Step 1: Extract Tier A+B ──► 5,242 tasks
OSV GCS (all.zip) ────────┤
                           ├─► Step 2: Parse OSV ─────────► 9,195 tasks
GHSA Tier D (3,275) ──────┤
                           ├─► Step 3: Registry API enrich ► 348 tasks
                           │
                           ├─► Step 4: Merge + dedup ──────► 7,670 tasks
                           │        + GitHub API validation
                           │
                           └─► Step 5: Full clone + unshallow
                                  + checkout fix_commit~1 ──► 3,426 patch diffs
                                                             3,320 worktrees
```

1. **Step 1:** Extract tasks from GHSA — commit URLs → repo + fix_commit (Tier A), Go package names → repo (Tier B)
2. **Step 2:** Download OSS-Fuzz + Go data from OSV GCS bucket, extract repo/commit/crash info
3. **Step 3:** Enrich GHSA Tier D records via PyPI/npm/crates.io/Composer/RubyGems registry APIs
4. **Step 4:** Merge all sources, deduplicate by (repo, fix_commit), validate repos via GitHub API
5. **Step 5:** Full clone all repos, unshallow, checkout vulnerable code, generate patch diffs

## Considerations

### Academic Use Only

This dataset is compiled and distributed **strictly for academic, non-commercial research purposes**. Any commercial use, redistribution for profit, or application in commercial products is strictly prohibited without explicit written authorization.

### Security Risk Notice

This dataset contains technical information about vulnerabilities, exploitation methods, and offensive security techniques. While this information is already publicly available, users should be aware that:

- **Unauthorized use** of exploit techniques against systems you do not own or have explicit permission to test is **illegal**
- **Responsible disclosure** practices should be followed when discovering new vulnerabilities
- Users must comply with all applicable **local, national, and international laws**
- The dataset should only be used to **improve defensive security** capabilities

### Licensing

The dataset compilation is released under Apache 2.0 for academic, non-commercial use. Individual repository contents retain their original source licensing. Users must verify licensing for specific repositories before any redistribution. Commercial use is prohibited.

### Biases

- **Language bias:** Go (53%) and C++ (15%) dominate due to GHSA and OSS-Fuzz coverage
- **Source bias:** Content reflects the advisory quality of GHSA and OSV sources
- **Ecosystem bias:** npm, pip, and Composer ecosystems are well-represented; NuGet and Maven have limited coverage
- **Recency bias:** More complete for recently disclosed vulnerabilities
- **Patch availability:** Only 45% of tasks have patch diffs, as some fix commits are unreachable (very old commits, force-pushed history, or deleted repos)

## Citation

```bibtex
@dataset{cyberrepo-10k,
  title={CyberRepo-10K: A Multi-Language Vulnerability Audit Dataset with GitHub Repositories and Patch Diffs},
  author={WhitzardAgent Team (SIIxFudan)},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/WhitzardAgent/CyberRepo-10K}
}
```