Duplicate from PentesterFlow/OffsecCyber

Co-authored-by: Mohmmaed Fathy <Secfathy@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+train.jsonl filter=lfs diff=lfs merge=lfs -text

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README.md

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+---
+language:
+  - en
+license: apache-2.0
+task_categories:
+  - text-generation
+  - text-classification
+tags:
+  - cybersecurity
+  - application-security
+  - vulnerabilities
+  - cve
+  - cwe
+  - instruction-tuning
+  - jsonl
+size_categories:
+  - 10K<n<100K
+---
+
+![OffsecCyberDataset — AI Dataset for Penetration Testers](banner.png)
+
+# Security Vulnerability Instruction Dataset
+
+An English-language dataset of security-focused assistant conversations about software vulnerabilities. Examples cover vulnerability explanations, CVE analysis, severity and impact, CWE references, remediation guidance, and offensive/web-appsec reasoning. It combines CVE-oriented examples with NVD records, HackerOne report conversations, PortSwigger lab material, and offensive-security instruction data, and can support instruction tuning or experiments in security-topic classification and analysis.
+
+> **This revision is a cleaned and enhanced build.** The previous version was deduplicated, re-labeled, and filtered (see **Processing and enhancement** below). Row counts and composition differ from earlier releases; the previous splits remain in the repository's git history.
+
+## What the data contains
+
+Each example pairs a prompt and an assistant response with structured metadata. The prompt may ask for a summary, technical description, impact or severity assessment, exploitation reasoning, or defensive remediation. The answer may cite CVE/CWE identifiers, CVSS scores, affected products, attack conditions, impact, or mitigation steps, depending on the example. Conversations include system instructions and are not all the same length.
+
+The current build combines these sources:
+
+| `training_source` | Records | Share |
+|---|---:|---:|
+| `qwen_cve` (CVE-oriented synthetic analysis) | 30,956 | 51.2% |
+| `NVD CVE 2.0 annual feed` | 21,228 | 35.1% |
+| `web_offensive_elite_v2_sft` (offensive/web-appsec) | 5,673 | 9.4% |
+| `h1` (HackerOne reports) | 2,350 | 3.9% |
+| `portswigger_labs_sft` | 273 | 0.5% |
+| `gapfill_synthetic` (reasoning / tool-use / negatives) | 21 | <0.1% |
+| `Official CVE Program record (CVEProject/cvelistV5)` | 16 | <0.1% |
+
+The JSONL metadata includes a normalized `training_source`, vulnerability/report `class`, optional CWE and CVE identifiers, CVSS/severity annotations, quality/completeness indicators, and conversation-level flags. These fields are useful for filtering and analysis but are not guaranteed to be complete or authoritative.
+
+## Dataset splits
+
+| Split | Records |
+|---|---:|
+| Train | 54,484 |
+| Validation | 3,017 |
+| Test | 3,016 |
+| **Total** | **60,517** |
+
+The splits use a 90/5/5 ratio with seed `240517`. They are grouped to reduce leakage across splits using shared CVE IDs, normalized HackerOne report IDs, and exact conversation hashes (**0 cross-split CVE leakage** in this build). The accompanying `manifest.json` records the per-split class/source counts and the full processing report.
+
+## Processing and enhancement
+
+This build was produced from the raw merged corpus (100,789 records) by a deterministic cleanup pipeline:
+
+- **Near-duplicate reduction.** Template-redundant answers (normalized over CVE IDs, URLs, and numbers) were capped at 3 copies each, removing **36,137** near-duplicate rows. The most common boilerplate template dropped from **8,278** copies to **3**; measured near-duplicate redundancy fell from ~40% to **~6%**.
+- **Label backfill.** Missing `class` labels were inferred from a CWE→class map learned from labeled rows (183 CWEs). Class-label coverage rose from ~37% to **95.3%**; backfilled rows carry `class_backfilled: true`.
+- **Hallucination filtering.** **3,899** rows flagged `has_future_cve` (forward-dated/fabricated CVE identifiers) were dropped.
+- **Prompt diversification.** The single dominant system prompt (previously ~57% of rows) was rotated across equivalent paraphrases; top-prompt share is now **~9%**.
+- **Length isolation.** 257 rows whose full sequence exceeds ~4096 tokens were held out of the splits (not included here) to avoid silent truncation during training.
+- **Gap-fill.** A small, validated set of records was added to introduce three otherwise-absent shapes: `<think>` reasoning traces, multi-turn agentic tool-calling trajectories, and negative / "not-a-vulnerability" / false-positive-rejection records. These carry `training_source: gapfill_synthetic`.
+
+## Data format
+
+Each `.jsonl` line is a JSON object with:
+
+- `messages`: a chat conversation of role/content pairs (`system`, `user`, `assistant`, and occasionally `tool`).
+- `metadata`: labels and auxiliary fields, including `class`, `cwe`, `cvss_score`, `severity`, `cve_ids`, `training_source`, quality/completeness indicators, and flags such as `is_negative`, `class_backfilled`, and `signal`.
+- Tool-calling records additionally carry a top-level `tools` array and `tool_calls` on the relevant assistant turn.
+
+Example (abridged):
+
+```json
+{
+  "messages": [
+    {"role": "system", "content": "You explain web security weaknesses using CWE and CVSS terminology correctly."},
+    {"role": "user", "content": "Summarize CVE-2026-9160."},
+    {"role": "assistant", "content": "..."}
+  ],
+  "metadata": {
+    "class": "ssti",
+    "cwe": "CWE-1336",
+    "cvss_score": 8.0,
+    "severity": "high",
+    "cve_ids": ["CVE-2026-9160"],
+    "training_source": "qwen_cve"
+  }
+}
+```
+
+## Labels
+
+There are 35 vulnerability/report classes, including `access_control`, `auth`, `business_logic`, `cmdi`, `deser`, `info_disclosure`, `pathtraver`, `sqli`, `ssrf`, `ssti`, `xss`, and `xxe`; `invalid_report` is also present. The largest classes are `xss` (8,290), `cmdi` (7,243), `access_control` (6,758), `info_disclosure` (6,232), `auth` (5,820), and `pathtraver` (4,967). About **2,834** records remain unlabeled (no `class` and no mappable CWE). Many tail classes have fewer than 100 records, and several (e.g. `ldapi`, `misconfig`, `oauth`, `cache_poisoning`) have only a handful. Class frequencies are imbalanced; consult `manifest.json` for exact counts by split, and prefer macro-averaged metrics and per-class results when evaluating classifiers.
+
+## Scope and domain note
+
+This dataset is **web / application-security and bug-bounty oriented** (OWASP-style classes: injection, access control, auth, XSS, SSRF, etc.). It is **not** a memory-safety / binary-exploitation corpus: memory-safety examples are ~0.7% of records, and only ~3% of answers contain code. It is therefore **not aligned with execution-based vulnerability-reproduction benchmarks** such as CyberGym (which targets C/C++ OSS-Fuzz crashes verified by sanitizers). Use it for web-appsec reasoning, explanation, triage, and instruction tuning — not as training data for binary PoC reproduction.
+
+## Benchmarking
+
+No model scores or benchmark results are included. The following is a recommended evaluation protocol, not a claim of measured performance.
+
+### Task A: vulnerability class prediction
+
+Given an example's `messages`, predict `metadata.class`. Use the provided splits. For a generative model, ask for exactly one label from the class names, normalize the output, and score the parsed prediction; for a classifier, train on train and select on validation only. Report accuracy, macro-F1, weighted-F1, and per-class precision/recall/F1 with support counts and a confusion matrix — not accuracy alone. Exclude or separately report `class_backfilled` rows if you need human-verified labels.
+
+### Task B: security answer generation
+
+Use each example's conversation as an instruction-tuning target, with the assistant message as the reference. Report the prompt template, model/version, decoding settings, and whether metadata was exposed to the model. Check CVE/CWE identifiers and numeric CVSS values against a dated authoritative source; text-overlap scores alone do not establish correctness.
+
+### Split and leakage cautions
+
+- Keep the test split untouched for final evaluation; tune on validation.
+- Grouping covers shared CVE IDs, normalized HackerOne report IDs, and exact conversation hashes, but does not guarantee independence by product, vendor, or vulnerability family.
+- Near-duplicate answers are capped but not eliminated; audit if your benchmark requires strict example independence.
+- Report results by `training_source` as well as overall, so the dominant CVE/NVD portion does not obscure performance on the offensive/HackerOne/lab examples.
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("PentesterFlow/OffsecCyber", data_files={
+    "train": "train.jsonl",
+    "validation": "valid.jsonl",
+    "test": "test.jsonl",
+})
+```
+
+## Data quality and use considerations
+
+- This is a curated/assembled dataset, not a guarantee that every statement is correct, current, or suitable as production security advice. Verify vulnerability details against authoritative advisories before relying on them.
+- A large portion of records is synthetic (`qwen_cve`, `gapfill_synthetic`). Treat `cvss_score`, `severity`, and `class` as dataset annotations, not authoritative ratings or ground truth about exploitability.
+- Forward-dated CVE rows were filtered from this build, but confirm publication dates before making temporal evaluation claims.
+- The dataset contains dual-use cybersecurity information and should be used for defensive, educational, or research purposes with appropriate safeguards.
+
+## Sources and processing
+
+Raw merged input: 100,789 records. After the enhancement pipeline (near-duplicate capping, forward-CVE filtering, label backfill, prompt diversification, length isolation, and gap-fill), **60,517** records remain across the three splits, with 257 over-length rows held out. See `manifest.json` for the complete per-split counts and processing report.
+
+## License
+
+This dataset is distributed under the Apache License 2.0. See [`LICENSE`](LICENSE) for the full text.

manifest.json

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+{
+  "name": "Enhanced security SFT (issues 1-6 fixed)",
+  "seed": 240517,
+  "ratios": {
+    "train": 0.9,
+    "valid": 0.05,
+    "test": 0.05
+  },
+  "fixes": {
+    "input": 100789,
+    "after_exact_dedup": 100789,
+    "cwe_map_size": 183,
+    "dropped_future": 3899,
+    "class_backfilled": 58524,
+    "sysprompt_rotated": 42997,
+    "overflow_isolated": 257,
+    "near_dup_templates_capped": 36137,
+    "after_all_fixes": 60496
+  },
+  "classes": [
+    "API Breaches & Bug Bounties",
+    "API Insecurities (OWASP API Top 10)",
+    "API Security Testing",
+    "API Security Testing Prep",
+    "API hacking toolkit setup",
+    "API path discovery concepts",
+    "API testing",
+    "API testing checklist: authz, mass assignment, injection",
+    "API testing checklist: endpoints and auth",
+    "API testing checklist: rate limits and evasion",
+    "API testing checklist: reconnaissance",
+    "Access Control",
+    "Additional lab targets and platforms",
+    "Android Application Security",
+    "Application Server",
+    "Attack Surface Mapping",
+    "Attacking Application Architecture",
+    "Automating Customized Attacks",
+    "Back-End Components",
+    "Billion laughs and quadratic blowup",
+    "Browser Security",
+    "Browser-Side Scripts",
+    "Browser/Web Security",
+    "Bug Bounty Basics",
+    "Bug Bounty Methodology",
+    "Bug Bounty Program Selection",
+    "Burp Intruder attack types",
+    "Burp Suite modules overview",
+    "Burp extensions for API testing",
+    "Business Logic & Broken Access Control",
+    "CMS/WordPress",
+    "CSS Security",
+    "CSV Injection",
+    "CVE Exploits",
+    "Case study: WordPress XML-RPC blowup",
+    "Client-Side Controls",
+    "Client-Side Vulnerabilities Interview",
+    "Crafting API requests in Postman",
+    "Cross-Site Request Forgery (CSRF)",
+    "Cross-Site Scripting (XSS)",
+    "DNS Rebinding",
+    "Denial of Service",
+    "Dependency Confusion",
+    "Domain-Wise AppSec Interview",
+    "Don\u2019t Be an Accessory",
+    "Encoding Transformations",
+    "Essential skills",
+    "Fuzzing & Automated Discovery",
+    "Fuzzing APIs with Wfuzz",
+    "Google Web Toolkit",
+    "HTML Security",
+    "HTTP Fundamentals",
+    "HTTP Host header attacks",
+    "HTTP Parameter Pollution",
+    "Headless Browser",
+    "Host Header Injection",
+    "Injection",
+    "Insecure Direct Object Reference (IDOR)",
+    "Insecure Source Code Management",
+    "Installing Docker for a lab",
+    "Integrating Postman with Burp Suite",
+    "JavaScript & Web Security Basics",
+    "JavaScript Security Fundamentals",
+    "Lab architecture best practices",
+    "Lab: discovering APIs on a local network",
+    "Learning Resources",
+    "Metasploit for Web",
+    "Methodology",
+    "Methodology and Resources",
+    "Native/Binary in Web Context",
+    "Open Redirect",
+    "Postman environments and collections",
+    "Prompt Injection",
+    "Proxy configuration for API testing",
+    "Reconnaissance",
+    "Reconnaissance with DevTools",
+    "Remote Code Execution (RCE)",
+    "Report Writing",
+    "Reporting",
+    "SQL Injection",
+    "Secure Coding",
+    "Security Tools",
+    "Server Side Include Injection",
+    "Server-Side Request Forgery (SSRF)",
+    "Server-Side Vulnerabilities Interview",
+    "Source Code Review",
+    "Subdomain Takeover",
+    "Summing Up",
+    "Supplemental API testing tools",
+    "Tabnabbing",
+    "Taint Analysis",
+    "Third-Party Code",
+    "Tooling",
+    "Tooling and Environment Setup",
+    "URL/HTTP",
+    "Variant Analysis",
+    "Vulnerability Report Writing",
+    "Vulnerability Research Intro",
+    "Vulnerable API lab targets",
+    "Vulnerable lab safety",
+    "Web API Anatomy",
+    "Web Application (In)security",
+    "Web Application Fundamentals",
+    "Web Cache Deception",
+    "Web Fundamentals",
+    "Web Hacking Intro",
+    "Web Security Interview",
+    "Web Servers",
+    "Web3/Smart Contract Web Angle",
+    "XML Attacks",
+    "XML fundamentals",
+    "XPATH Injection",
+    "XSS",
+    "access_control",
+    "attack_surface",
+    "auth",
+    "authentication",
+    "automation",
+    "bash_fundamentals",
+    "box",
+    "business_logic",
+    "c2_rat",
+    "c_language",
+    "cache_poisoning",
+    "case_studies",
+    "checklist",
+    "clickjacking",
+    "cmdi",
+    "code_audit",
+    "codeinj",
+    "cors",
+    "crypto",
+    "crypto_weak",
+    "csrf",
+    "database_mining",
+    "deser",
+    "design_review",
+    "dns",
+    "dns_server",
+    "exploit_dev",
+    "firewalls",
+    "forensics",
+    "format_string",
+    "go_fundamentals",
+    "go_plugins",
+    "graphql",
+    "graphql_basics",
+    "graphql_dos",
+    "hardening",
+    "http_client",
+    "http_server",
+    "info_disclosure",
+    "information_disclosure",
+    "input_validation",
+    "insecure_design",
+    "invalid_report",
+    "kernel",
+    "lab_setup",
+    "ldapi",
+    "long-term methodology",
+    "lua_plugins",
+    "memory_safety",
+    "metasploit_rpc",
+    "methodology",
+    "mindset",
+    "misc",
+    "misconfig",
+    "mitigation",
+    "network_app_protocols",
+    "network_protocols",
+    "note taking",
+    "note-taking / wordlists",
+    "note-taking / workflow",
+    "oauth",
+    "operational_review",
+    "packet_capture",
+    "parameter discovery",
+    "parameter testing / automation",
+    "parsing",
+    "pathtraver",
+    "post_exploitation",
+    "program selection",
+    "proto_pollution",
+    "pwn",
+    "race",
+    "recon",
+    "recon / content discovery",
+    "recon / dorking",
+    "recon_tooling",
+    "redirect",
+    "reporting",
+    "request_forgery",
+    "resource",
+    "resources",
+    "rev",
+    "reverse_engineering",
+    "scanning",
+    "secrets",
+    "smuggling",
+    "source review / JS analysis",
+    "sqli",
+    "ssrf",
+    "ssti",
+    "strings_metacharacters",
+    "supply_chain",
+    "tcp_proxy",
+    "tcp_scanning",
+    "threat_modeling",
+    "tls_transport",
+    "tooling",
+    "type_conversion",
+    "unknown",
+    "upload",
+    "vulnerability_fundamentals",
+    "web",
+    "web_applications",
+    "web_technologies",
+    "xss",
+    "xxe"
+  ],
+  "splits": {
+    "train": {
+      "records": 54484,
+      "sources": {
+        "qwen_cve": 27665,
+        "NVD CVE 2.0 annual feed": 19304,
+        "web_offensive_elite_v2_sft": 5140,
+        "h1": 2093,
+        "portswigger_labs_sft": 249,
+        "Official CVE Program record (CVEProject/cvelistV5)": 16
+      },
+      "labeled": 51877
+    },
+    "valid": {
+      "records": 3017,
+      "sources": {
+        "qwen_cve": 1614,
+        "NVD CVE 2.0 annual feed": 996,
+        "web_offensive_elite_v2_sft": 264,
+        "h1": 129,
+        "portswigger_labs_sft": 12
+      },
+      "labeled": 2893
+    },
+    "test": {
+      "records": 3016,
+      "sources": {
+        "qwen_cve": 1677,
+        "NVD CVE 2.0 annual feed": 928,
+        "web_offensive_elite_v2_sft": 269,
+        "h1": 128,
+        "portswigger_labs_sft": 12
+      },
+      "labeled": 2892
+    }
+  },
+  "gapfill_merged": {
+    "records": 21,
+    "shapes": {
+      "A_reasoning": 15,
+      "B_tool_trajectory": 6
+    },
+    "gaps_filled": {
+      "think_traces": 10,
+      "tool_trajectories": 6,
+      "negatives": 5
+    },
+    "source": "gapfill_synthetic (gen_gapfill.py)",
+    "distribution": {
+      "train": 17,
+      "valid": 2,
+      "test": 2
+    }
+  },
+  "coverage_after_gapfill": {
+    "think_total": 21,
+    "tool_total": 48,
+    "negative_total": 93,
+    "note": "quality seed covering all 3 shapes in train+valid+test; scale via /pentesterflow-dataset for volume"
+  }
+}

train.jsonl

@@ -0,0 +1,3 @@
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