Security researchers at Unit 42, Palo Alto Networks’ threat intelligence division, have disclosed a critical vulnerability in Google Cloud’s Vertex AI Agent Engine that allowed a misconfigured agent to operate as a “double agent” — appearing to perform its intended function while simultaneously exfiltrating customer data and Google’s own internal source code.

The flaw was confirmed across multiple independent security sources and represents one of the most tangible examples yet of what happens when least-privilege principles are abandoned in the rush to deploy agentic AI infrastructure.

The Flaw: Overprivileged Default Permissions

The root cause isn’t a complex zero-day exploit. It’s something far more embarrassing and far more preventable: Vertex AI Agent Engine ships with default agent permissions that are far too broad.

According to Unit 42’s research, an agent deployed in Vertex AI’s managed environment is granted permissions that, if the agent is compromised or misconfigured, allow it to:

  • Read customer data stored in connected Google Cloud buckets and databases
  • Access Google’s internal operational code — including code not meant to be exposed to tenants
  • Continue appearing to serve its legitimate purpose while running the exfiltration in parallel

The “double agent” framing is precise: this isn’t an agent that stops working. It’s an agent that works exactly as expected and also does something it shouldn’t. That combination makes detection harder, not easier.

Why Least Privilege Is Non-Negotiable in Agentic AI

The principle of least privilege — giving any system component only the permissions it needs to do its job, and nothing more — is a foundational concept in security architecture. It predates AI by decades. And yet AI deployment pipelines are violating it at scale.

The reason is speed and convenience. Cloud platforms like Vertex AI make it trivially easy to spin up an agent with broad access. Scoping permissions down requires understanding exactly what an agent needs, which requires either careful design upfront or laborious post-deployment auditing. Most teams skip this.

Unit 42’s finding demonstrates that skipping it in Vertex AI had real consequences: data that should never have been accessible to tenant-deployed agents was, in fact, accessible — and some of that data belonged to Google itself.

The Attack Scenario

The double-agent attack surface works roughly like this:

  1. A misconfigured or intentionally weaponized agent is deployed into Vertex AI Agent Engine
  2. The agent leverages overprivileged default permissions to scan connected storage and internal resources
  3. It exfiltrates target data to an attacker-controlled endpoint while continuing to return normal responses to legitimate queries
  4. Because the agent is still “working,” routine monitoring doesn’t flag it as compromised

This is a supply chain attack scenario made easy. An attacker who can influence the deployment of a Vertex AI agent — through a compromised developer account, a poisoned dependency, or a malicious third-party plugin — gains access to whatever permissions the platform granted by default.

What Enterprise Teams Should Do

Unit 42’s disclosure is a forcing function for teams running Vertex AI in production. Specific actions:

1. Audit all deployed Vertex AI agents immediately Review what IAM roles and service account permissions each agent carries. If you deployed with defaults, assume the surface is over-broad.

2. Scope to minimum required permissions For each agent, enumerate exactly what resources it needs to read and write. Strip everything else. Vertex AI supports custom IAM bindings per service account.

3. Enable audit logging on connected storage Google Cloud Storage and BigQuery both support detailed access logs. If you’re not capturing agent-generated API calls to data resources, you have no visibility into exfiltration.

4. Treat agent service accounts as untrusted identities Don’t give agent service accounts access to other agents’ resources, internal infrastructure, or cross-project data. Segment aggressively.

5. Review third-party agents before deployment If you’re installing pre-built agents from the Vertex AI marketplace or third-party vendors, treat them as untrusted code until audited.

Google has not yet published a formal advisory or timeline for tightening default permissions — which means the current defaults remain in place for any team that hasn’t manually scoped down their agent IAM configurations.

Sources

  1. TechRadar — Vertex AI ‘double agent’ flaw exposes customer data and Google’s internal code
  2. Unit 42 / Palo Alto Networks — Double Agents: Vertex AI Research
  3. The Hacker News — Vertex AI double agent vulnerability
  4. CyberSecurityNews.com — Unit 42 Vertex AI disclosure

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260401-2000

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