SecureAI-SE/http-attack-requests
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… See the full description on the dataset page: https://huggingface.co/datasets/SecureAI-SE/http-attack-requests.
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.
Provenance
Splits
Balanced: 1000 per class across all splits combined.
Usage
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).
