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jabir-khan/hunter-llm-sft-v1

HunterLLM SFT v1 Instruction + preference dataset for training a bug-bounty / offensive-security assistant that thinks like a red-teamer, prioritizes attacker primitives and reachable impact, and writes triager-friendly reports — strictly scoped to authorized engagements (bug bounty programs, contracted pentests, isolated labs). Files File Rows Schema sft_train.jsonl ~33.1k {instruction, input, output, tags, meta} dpo_pairs.jsonl ~33.1k {prompt… See the full description on the dataset page: https://huggingface.co/datasets/jabir-khan/hunter-llm-sft-v1.

sourceHugging Facecc-by-sa-4.0updated 4mo agoView on Hugging Face
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HunterLLM SFT v1

Instruction + preference dataset for training a bug-bounty / offensive-security assistant that thinks like a red-teamer, prioritizes attacker primitives and reachable impact, and writes triager-friendly reports — strictly scoped to authorized engagements (bug bounty programs, contracted pentests, isolated labs).

Files

FileRowsSchema
sft_train.jsonl~33.1k{instruction, input, output, tags, meta}
dpo_pairs.jsonl~33.1k{prompt, chosen, rejected}

Sources (raw)

Source familyProviderRecords (raw)License
GitHub repos (26 curated)Various~20.6k filesMIT / Apache-2.0 / CC-BY-SA-4.0 / BSD / CC-BY-4.0 / Public Domain
NVD API 2.0NIST~3.5k CVEs (90 day window)Public domain
CISA KEV catalogDHS CISA~1.6k actively-exploited CVEsPublic domain
MITRE ATT&CK (Enterprise)MITRE~697 techniquesApache-2.0
Curated writeup URLstrafilatura-extracted~28 long-form research postsPer-site (text excerpts only, provenance in meta)

GitHub repos used

OWASP/CheatSheetSeries, OWASP/wstg, OWASP/ASVS, OWASP/owasp-mastg, OWASP/API-Security, OWASP/Top10, swisskyrepo/PayloadsAllTheThings, swisskyrepo/InternalAllTheThings, danielmiessler/SecLists (filtered), projectdiscovery/nuclei-templates, rapid7/metasploit-framework (docs only), reddelexc/hackerone-reports (indexes only), nahamsec/Resources-for-Beginner-Bug-Bounty-Hunters, jhaddix/tbhm, Hari-prasaanth/Web-App-Pentest-Checklist, harshinsecurity/web-pentesting-checklist, dwisiswant0/awesome-oneliner-bugbounty, infoslack/awesome-web-hacking, EdOverflow/bugbounty-cheatsheet, ngalongc/bug-bounty-reference, Ignitetechnologies/Mindmap, 0xInfection/Awesome-WAF, bugcrowd/vulnerability-rating-taxonomy, devanshbatham/Awesome-Bugbounty-Writeups, kh4sh3i/Bug-Bounty-Writeups, qazbnm456/awesome-web-security.

Writeup URL hosts

portswigger.net (Research blog), googleprojectzero.blogspot.com, blog.doyensec.com, research.nccgroup.com, bishopfox.com, posts.specterops.io, blog.assetnote.io, labs.detectify.com, blog.detectify.com, projectdiscovery.io, github.blog, snyk.io, www.acunetix.com, owasp.org.

Instruction synthesis

Per-source specialized templates produce diverse training signals:

  • —GitHub generic: hunt_plan, code_review, report_draft.
  • —GitHub specialized: Metasploit module → exploitation + proof-of-impact + adjacent surfaces. HackerOne report-index row → infer primitive + hunt strategy
  • —report skeleton. Nuclei template → matcher analysis + weaponization + adjacent templates. OWASP doc → focused triple-template.
  • —NVD CVE: three templates per CVE — triage (offensive root cause), hunt (fingerprint + evidence), report (HackerOne-style skeleton).
  • —CISA KEV: "actively-exploited" priority framing with vendor/product fingerprinting and severity uplift narrative.
  • —MITRE ATT&CK: TTP → offensive operator framing (kill-chain phase, evidence, defender telemetry to expect).
  • —URL writeups: offensive distillation of research posts (attacker playbook + impact stack + replay strategy).

Every instruction carries the AUTHORIZATION_NOTE so the model learns to keep work scoped to authorized targets.

Curation pipeline

  1. 1.Quality scoring penalizes config/build files, short or low-prose inputs, pure YAML/key-value blobs.
  2. 2.MinHash LSH deduplication at threshold 0.85 (datasketch).
  3. 3.Tag inference on bug-class keywords — XSS, SQLi, RCE, SSRF, XXE, Deserialization, OAuth, GraphQL, PathTraversal/LFI/RFI, CSRF, CSP, CORS, IDOR, etc.

DPO pair synthesis

For each curated SFT row we keep the original output as chosen and synthesize a deliberately weaker / over-cautious rejected response, training the model to prefer concrete attacker-primitive reasoning over generic defensive boilerplate.

Intended use

  • —Fine-tuning small/mid open LLMs (Llama-3-8B-Instruct, Mistral-7B-Instruct, DeepSeek-Coder, etc.) for bug-bounty mentoring, report drafting, and attacker-primitive analysis on authorized targets.
  • —Not intended for unauthorized testing, weaponization against third parties, or evasion of program rules.

Limitations & known biases

  • —HackerOne report titles are public metadata only; the dataset does not contain disclosed report bodies.
  • —Public writeup extraction is best-effort (some sites JS-render or block scrapers); failed URLs are silently skipped.
  • —Quality filter is heuristic; a small fraction of low-signal rows remain.
  • —Built on a single NVD lookback window — extend with hunter-llm collect-nvd --years N for longer coverage.
  • —CISA KEV uses templated short descriptions; MinHash dedup drops ~70% of entries as near-duplicates by design (the 482 surviving rows still represent the breadth of vendor/product/CVE primitives).

Reproducibility

Generated by HunterLLM:

bash
hunter-llm bootstrap-data --skip-trickest --full \
    --urls data/urls/writeups.txt
# Or piecewise:
hunter-llm collect-github --skip-trickest
hunter-llm collect-nvd --years 3
hunter-llm collect-cisa-kev
hunter-llm collect-mitre-attack
hunter-llm collect-urls data/urls/writeups.txt
hunter-llm build-dataset
hunter-llm export-dpo

License

CC-BY-SA-4.0, matching the most restrictive permissive source license in the mix (OWASP). Underlying sources retain their respective licenses; provenance is preserved in meta.