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README.md
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---
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'4': ssti
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'5': lfi
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'6': traversal
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splits:
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- name: train
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num_bytes: 3088380
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num_examples: 4900
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- name: validation
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num_bytes: 443142
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num_examples: 700
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- name: test
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num_bytes: 878744
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num_examples: 1400
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download_size: 1076968
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dataset_size: 4410266
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: validation
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path: data/validation-*
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- split: test
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path: data/test-*
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---
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---
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license: cc-by-4.0
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task_categories:
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- text-classification
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language:
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- en
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tags:
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- security
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- web-attacks
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- http
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- waf
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- payloads
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pretty_name: HTTP Attack Requests (multi-class)
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---
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# HTTP Attack Requests — multi-class
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Real HTTP requests labelled with the web-attack class carried in the request,
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for training and evaluating request/payload classifiers (WAF / DAST style).
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## Classes (7)
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`normal`, `sqli`, `xss`, `ssrf`, `ssti`, `lfi`, `traversal`
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(IDOR is intentionally excluded — it's an access-control flaw with no payload signature.)
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## How it was built (and why it's shortcut-resistant)
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Every example is a full HTTP request built on the **same real envelopes** (CSIC 2010
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normal requests). For attack classes, **one query-param value is replaced** with a real
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attack payload; `normal` keeps its real benign value. The **`Host`/domain is randomised**
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on every request. So neither request structure nor domain can be used as a shortcut —
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the only signal is the injected value. This is a deliberate guard against
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[shortcut learning](https://www.nature.com/articles/s42256-020-00257-z).
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## Provenance
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| Class | Source |
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|-------|--------|
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| envelopes + `normal` values | CSIC 2010 (`bridge4/CSIC2010_dataset_classification`) — real |
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| `sqli`, `xss`, `lfi`, `traversal` | PayloadsAllTheThings — real payload lists |
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| `ssrf`, `ssti` | templated (no clean public payload file) |
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## Splits
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| split | rows |
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|-------|------|
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| train | 4900 |
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| validation | 700 |
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| test | 1400 |
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Balanced: 1000 per class across all splits combined.
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## Usage
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```python
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from datasets import load_dataset
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ds = load_dataset("SecureAI-SE/http-attack-requests")
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print(ds["train"][0]) # {'request': 'GET /...', 'label': 2}
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ds["train"].features["label"].names # ['normal','sqli','xss','ssrf','ssti','lfi','traversal']
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```
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## Intended use & ethics
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For **authorised** security research, WAF/IDS training, and education. It contains real
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attack payloads; do not use them against systems you do not own or have permission to test.
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Built for the *Fine-Tuning LLMs for Security Engineers* course (Secure AI).
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