VertexAGI/cyberdata-1

CyberData 1

A 15,000-example SFT dataset combining verified agentic-coding trajectories, cybersecurity agent behavior, and structured vulnerability intelligence — built entirely from non-gated, redistributable sources.

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What this is

CyberData 1 is a curated mixture of five public Hugging Face datasets, resampled and templated into one consistent messages-format SFT dataset. It skews heavily toward real, execution-verified agentic coding (tool use, multi-turn trajectories, actual resolved software-engineering tasks), with a smaller layer of dedicated cybersecurity agent behavior and structured vulnerability knowledge (CVE/CWE, OWASP, MITRE ATT&CK).

Composition

Source	Examples	%	What it contributes
nvidia/SWE-Hero-openhands-trajectories	6,000	40.0%	Execution-based software-engineering agent trajectories (OpenHands framework)
nvidia/Open-SWE-Traces	5,339	35.6%	Multi-harness (OpenHands, SWE-agent, mini-swe-agent) coding-agent trajectories, filtered to resolved=1 only — the agent's patch was verified to actually fix the task
0xKitkat/AgentForge-1152 (security split)	576	3.8%	Evidence-grounded defensive-cybersecurity agent trajectories: tool use, failed-check recovery, evidence-based findings
ismailtasdelen/unified-vulnerability-intelligence-dataset	500	3.3%	Structured vulnerability knowledge (CWE, CAPEC, MITRE ATT&CK, OWASP, CVSS, remediation, detection)
stasvinokur/cve-and-cwe-dataset-1999-2025	2,585	17.2%	Real NVD CVE records (1999–2025) with severity, CVSS, and CWE classification
Total	15,000	100%

Why Open-SWE-Traces is short of its original 5,500 target

The resolved=1 filter (only agent trajectories whose patch was verified to fix the task) removes the large majority of rows in some harness/teacher-model subsets. After sampling across all 13 harness/teacher/source-dataset combinations in the upstream repo, only 5,339 genuinely resolved trajectories were available within the sampled shards. The remaining 161 examples needed to reach 15,000 total were filled from stasvinokur/cve-and-cwe-dataset-1999-2025, which has ample supply (280,700 rows) and was already in the mix.

What was deliberately left out

ethanolivertroy/nist-cybersecurity-training was evaluated and excluded. Its published schema (id/text/embedding/metadata) does not match its own README's description of a clean system/user/assistant messages format — the actual text column is a flat blob with no separable ground-truth answer. Rather than have another model invent an answer to pair with the question embedded in that blob, it was dropped entirely.

Templating methodology (UVID and CVE/CWE)

nvidia/SWE-Hero-openhands-trajectories, nvidia/Open-SWE-Traces, and 0xKitkat/AgentForge-1152 were already in multi-turn messages format and are used as-is (each trajectory keeps its own source-specific system prompt describing its tools and environment).

ismailtasdelen/unified-vulnerability-intelligence-dataset and stasvinokur/cve-and-cwe-dataset-1999-2025 are structured metadata tables, not pre-written conversations. Each was converted into a single-turn Q&A pair using a deterministic template: every fact stated in the answer comes from a column already present in that row (category, severity, CWE, CVSS score, remediation, detection guidance, CVE description, etc.). A field left blank in the source is simply omitted from the answer — nothing is inferred, estimated, or generated by another model to fill a gap.

Format

Each row is {"messages": [...]}, standard chat SFT format. Some rows additionally carry tool_calls on assistant turns (the agentic-coding trajectories).

from datasets import load_dataset
ds = load_dataset("VertexAGI/cyberdata-1")
print(ds["train"][0]["messages"])

• train.jsonl — 13,800 examples
• valid.jsonl — 1,200 examples (held out, not used for template/dedup verification — a genuine 8% split)

Licensing

This is a mixture of five independently-licensed sources: CC-BY-4.0 (nvidia/SWE-Hero-openhands-trajectories, nvidia/Open-SWE-Traces), Apache-2.0 (0xKitkat/AgentForge-1152), MIT (ismailtasdelen/unified-vulnerability-intelligence-dataset), and CC0-1.0 (stasvinokur/cve-and-cwe-dataset-1999-2025). No source in this mix is gated or carries a non-commercial restriction. If you redistribute this dataset, retain attribution to each upstream source above, per their respective licenses (required for the CC-BY-4.0 portions).

Quality checks performed

• Zero duplicate example IDs across the full 15,000.
• Zero malformed rows (every row has ≥2 messages with valid role/content).
• Open-SWE-Traces rows are restricted to resolved=1 — the agent's patch was independently verified against the task's test suite.
• Every row from every source was verified against the real, live Hugging Face repo (row counts, schema, license) before inclusion — not taken from a dataset card's stated numbers alone, several of which turned out to be inaccurate on inspection.

Limitations

This is a resampled mixture, not a from-scratch curated dataset — quality is bounded by the upstream sources. The CVE/CWE and UVID portions are single-turn Q&A synthesized from structured data via template, not natural human-written conversation. The agentic-coding portion dominates the mix (75.6%); the cybersecurity-agent and vulnerability-knowledge portions are comparatively small (23.4% combined).