f

src/pentesting-cloud/gcp-security/gcp-post-exploitation/gcp-dataflow-post-exploitation.md

@@ -46,4 +46,10 @@ Dataflow templates exist to export BigQuery data. Use the appropriate template f
 
 Streaming pipelines can read from Pub/Sub (or other sources) and write to GCS. Launch a job with a template that reads from the target Pub/Sub subscription and writes to your controlled bucket.
 
+## References
+
+- [Dataflow templates](https://cloud.google.com/dataflow/docs/guides/templates/provided-templates)
+- [Control access with IAM (Dataflow)](https://cloud.google.com/dataflow/docs/concepts/security-and-permissions)
+- [GCP - Bigtable Post Exploitation](gcp-bigtable-post-exploitation.md)
+
 {{#include ../../../banners/hacktricks-training.md}}

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

@@ -37,7 +37,7 @@ gcloud dataflow jobs list --region=<region>
 gcloud dataflow jobs list --project=<PROJECT_ID>
 
 # Describe a job to get template GCS path, staging location, and any UDF/template references
-gcloud dataflow jobs describe <JOB_ID> --region=<region> --format="yaml"
+gcloud dataflow jobs describe <JOB_ID> --region=<region> --full --format="yaml"
 # Look for: currentState, createTime, jobMetadata, type (JOB_TYPE_STREAMING or JOB_TYPE_BATCH)
 # Pipeline options often include: tempLocation, stagingLocation, templateLocation, or flexTemplateGcsPath
 ```
@@ -99,8 +99,10 @@ def _malicious_func():
     coordination_file = "/tmp/pwnd.txt"
     if os.path.exists(coordination_file):
         return
-    
+
     # malicous code goes here
+    with open(coordination_file, "w", encoding="utf-8") as f:
+        f.write("done")
 
 def transform(line):
     # Malicous code entry point - runs per line but coordination ensures once per worker
@@ -140,10 +142,15 @@ Add the cleanup step (`drop: [malicious_step]`) so the pipeline still writes val
               from datetime import datetime
               coordination_file = "/tmp/pwnd.txt"
               if os.path.exists(coordination_file):
-                  return 
+                  return True
               try:
                   import urllib.request
-                 # malicious code goes here
+                  # malicious code goes here
+                  with open(coordination_file, "w", encoding="utf-8") as f:
+                      f.write("done")
+              except Exception:
+                  pass
+              return True
     append: true
 - name: CleanupTransform
   type: MapToFields
@@ -169,5 +176,9 @@ For exploitation details, see:
 ## References
 
 - [Dataflow Rider: How Attackers can Abuse Shadow Resources in Google Cloud Dataflow](https://www.varonis.com/blog/dataflow-rider)
+- [Control access with IAM (Dataflow)](https://cloud.google.com/dataflow/docs/concepts/security-and-permissions)
+- [gcloud dataflow jobs describe](https://cloud.google.com/sdk/gcloud/reference/dataflow/jobs/describe)
+- [Apache Beam YAML: User-defined functions](https://beam.apache.org/documentation/sdks/yaml-udf/)
+- [Apache Beam YAML Transform Reference](https://beam.apache.org/releases/yamldoc/current/)
 
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src/pentesting-cloud/gcp-security/gcp-services/gcp-dataflow-enum.md

@@ -12,7 +12,7 @@ A Dataflow pipeline typically includes:
 
 **Template:** YAML or JSON definitions (and Python/Java code for flex templates) stored in GCS that define the pipeline structure and steps.
 
-**Launcher:** A short-lived Compute Engine instance that validates the template and prepares containers before the job runs.
+**Launcher (Flex Templates):** A short-lived Compute Engine instance may be used for Flex Template launches to validate the template and prepare containers before the job runs.
 
 **Workers:** Compute Engine VMs that execute the actual data processing tasks, pulling UDFs and instructions from the template.
 
@@ -76,4 +76,10 @@ gcloud storage ls gs://<bucket>/**
 ../gcp-persistence/gcp-dataflow-persistence.md
 {{#endref}}
 
+## References
+
+- [Dataflow overview](https://cloud.google.com/dataflow)
+- [Pipeline workflow execution in Dataflow](https://cloud.google.com/dataflow/docs/guides/pipeline-workflows)
+- [Troubleshoot templates](https://cloud.google.com/dataflow/docs/guides/troubleshoot-templates)
+
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