README.md

---
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
tags:
- security
- web-attacks
- http
- waf
- payloads
pretty_name: HTTP Attack Requests (multi-class)
---

# HTTP Attack Requests — multi-class

Real HTTP requests labelled with the web-attack class carried in the request,
for training and evaluating request/payload classifiers (WAF / DAST style).

## Classes (7)

`normal`, `sqli`, `xss`, `ssrf`, `ssti`, `lfi`, `traversal`
(IDOR is intentionally excluded — it's an access-control flaw with no payload signature.)

## How it was built (and why it's shortcut-resistant)

Every example is a full HTTP request built on the **same real envelopes** (CSIC 2010
normal requests). For attack classes, **one query-param value is replaced** with a real
attack payload; `normal` keeps its real benign value. The **`Host`/domain is randomised**
on every request. So neither request structure nor domain can be used as a shortcut —
the only signal is the injected value. This is a deliberate guard against
[shortcut learning](https://www.nature.com/articles/s42256-020-00257-z).

## Provenance

| Class | Source |
|-------|--------|
| envelopes + `normal` values | CSIC 2010 (`bridge4/CSIC2010_dataset_classification`) — real |
| `sqli`, `xss`, `lfi`, `traversal` | PayloadsAllTheThings — real payload lists |
| `ssrf`, `ssti` | templated (no clean public payload file) |

## Splits

| split | rows |
|-------|------|
| train | 4900 |
| validation | 700 |
| test | 1400 |

Balanced: 1000 per class across all splits combined.

## Usage

```python
from datasets import load_dataset
ds = load_dataset("SecureAI-SE/http-attack-requests")
print(ds["train"][0])   # {'request': 'GET /...', 'label': 2}
ds["train"].features["label"].names   # ['normal','sqli','xss','ssrf','ssti','lfi','traversal']
```

## Intended use & ethics

For **authorised** security research, WAF/IDS training, and education. It contains real
attack payloads; do not use them against systems you do not own or have permission to test.

Built for the *Fine-Tuning LLMs for Security Engineers* course (Secure AI).