Denash/cevud-training-dataset

CEVuD Training Dataset (CVEfixes-based)

HuggingFace-ready dataset card for the CVEfixes-based training corpus. Published at Denash/cevud-training-dataset.

This dataset trains the CEVuD Stage-2 local classifier (Denash/codebert-vuln-classifier). It is a curated, function-level Python vulnerability corpus derived from CVEfixes, the largest publicly available dataset linking real CVEs to the exact commits that fixed them.

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Dataset Summary

Property	Value
HF Dataset ID	Denash/cevud-training-dataset
Source dataset	hitoshura25/cvefixes
Language	Python
Task	Binary sequence classification (vulnerable vs. safe)
Label scheme	0 = safe, 1 = vulnerable
Total samples	2,181
Projects (repos)	575
Vulnerable samples	474 (21.7%)
Safe samples	1,707 (78.3%)
Safe class breakdown	1,643 benign_sibling + 64 benign_control
Unique CWE types	93
Unique CVEs	470
Chunk size	64 lines with 8-line overlap
Hunk-centering	Enabled
Near-duplicate threshold	0.75 token-similarity
License	MIT (CEVuD wrapper); check upstream CVEfixes for source license

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Dataset Creation

Source Data

CVEfixes is a parquet-backed HuggingFace dataset that streams row-by-row. Each row contains:

• vulnerable_code: the vulnerable code snippet (pre-fix)
• fixed_code: the patched code snippet (post-fix)
• repo_url: GitHub repository URL
• hash: commit SHA where the fix was applied
• cve_id: CVE identifier
• cwe_id: CWE identifier
• cvss2_base_score / cvss3_base_score: CVSS scores
• diff_with_context: unified diff with surrounding context
• language: programming language

Curation Pipeline

The raw CVEfixes data is converted and filtered through a multi-stage pipeline implemented in src/scripts/convert_cvefixes.py and src/training/dataset_builder.py.

Stage 1 — Language and validity filtering

1. Keep only Python rows.
2. Require non-empty vulnerable code, valid repo URL, and resolvable .py file path.
3. Skip documentation, test, packaging, and version-only files.

Stage 2 — Signal and trivial-change filtering 4. Require at least 2 lines of real code signal. 5. Drop (vulnerable, safe) pairs that differ only in non-semantic ways.

Stage 3 — Deduplication 6. Exact deduplication of normalized vulnerable snippets. 7. Contradiction removal: drop identical text appearing with both labels.

Stage 4 — Safe-class construction

CVEfixes provides only vulnerable samples. A genuine safe class is constructed from two sources:

• Benign siblings (1,643 samples): Functions from the same file, in commits the fix did not touch.
• Benign controls (64 samples): Functions from files the fix commit never touched, mined from verified-benign repositories.

The post-fix function is explicitly not used as label=0 — it is a near-duplicate of its vulnerable twin (median token-similarity ≈ 0.94) and would collapse training to P = 0.5.

Stage 5 — Enrichment

Each sample is enriched with the full enclosing function (AST-expanded) and module-level imports, matching the inference-time context exactly.

Stage 6 — Chunking

Functions are cut into uniform 64-line windows with 8-line overlap. For vulnerable samples, only chunks overlapping the diff hunk are kept (hunk-centering).

Stage 7 — Quality guards

Any safe chunk >0.75 token-similar to a vulnerable chunk in the same project is dropped. Hard contradictions are also removed.

Stage 8 — Splitting

Project-level 60/20/20 split with seed=42. No project appears in more than one split.

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Dataset Structure

Field	Type	Description
sample_id	str	Unique identifier (e.g. cvefixes::salt::2874d100)
project	str	Repository name (e.g. salt)
text	str	Enriched code snippet (function + imports, chunked)
label	int	0 = safe, 1 = vulnerable
vulnerability_type	str	CWE identifier (e.g. CWE-534)
cwe	str	CWE identifier (same as vulnerability_type)
file_path	str	Relative path in the repository
function_name	str	Enclosing function name
start_line	int	Function start line (1-based)
end_line	int	Function end line (1-based)
source_code_length	int	Lines in the original source file
context_length	int	Lines in the enriched snippet
sample_subtype	str	vulnerable, benign_sibling, or benign_control
chunk_index	int	Chunk index within the function
chunk_start	int	Chunk start line
chunk_end	int	Chunk end line
hunk_text_start	int	Diff hunk start offset within text
hunk_text_end	int	Diff hunk end offset within text

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Data Splits

Split	Samples	Vulnerable	Safe	Projects
Train	1,464	316	1,148	330
Validation	358	76	282	—
Test	359	82	277	—

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Key Statistics

• Class imbalance: ~1 : 3.6 vulnerable/safe
• Unique CWEs: 93
• Top CWEs: CWE-79 (30), CWE-22 (27), CWE-20 (22), CWE-601 (17), CWE-918 (16)
• Chunking: Uniform 64-line windows with 8-line overlap
• Hunk-centering: Enabled
• Near-duplicate guard: 0.75 token-similarity threshold

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Intended Use

This dataset is intended for:

• Training the CEVuD Stage-2 classifier: The primary use case. The classifier is fine-tuned on this dataset to produce P(vulnerable) scores for code chunks.
• Validation and testing: Held-out project splits provide unbiased estimates of classifier performance.
• Research: Studying class imbalance, safe-class construction, and chunking strategies for vulnerability detection.

It is not intended for:

• Training the gate weights (use the VUDENC-based pipeline dataset instead)
• Standalone vulnerability detection without the gated pipeline
• Cross-language transfer without retraining

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Limitations

• Python-only: Contains only Python functions.
• CWE imbalance: 93 unique CWEs but distribution is skewed; rare types have few samples.
• Temporal bias: Spans many years of CVEs; older patterns may not reflect modern code.
• Safe-class construction: The safe class does not include post-fix code. Benign siblings and controls are used instead to avoid near-duplicate contradictions.
• Chunk-level labels: Vulnerabilities spanning multiple chunks may be missed if no single chunk contains the complete pattern.

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Citation

@misc{cevud2026,
  title={CEVuD: Cost-Effective Vulnerability Detection via Gated Static-Neural Reasoning},
  author={CEVuD Authors},
  year={2026},
  note={Dataset: Denash/cevud-training-dataset; Model: Denash/codebert-vuln-classifier}
}

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Related Resources

Resource	Link
Model	Denash/codebert-vuln-classifier
Pipeline dataset (VUDENC)	Denash/cevud-pipeline-dataset
Source dataset (CVEfixes)	hitoshura25/cvefixes
CEVuD GitHub	https://github.com/Denash/CEVuD

Point of contact: Open an issue on the CEVuD GitHub repository.