When Low Privilege Goes High in Vertex AI
Key Takeaways
- Vertex AI allowed low-privilege users to escalate into powerful service agent roles
- Exploitation relied on insecure defaults and excessive permissions
- Service agent tokens enabled broad access to AI workloads and cloud data
- The incident highlights systemic risks in managed AI infrastructure security
When AI Turns Rogue: The Google Vertex AI Security Breach and What It Means for the Future of AI Trust
In mid-2024, security researchers disclosed serious privilege escalation flaws within Google Vertex AI. The issues allowed users with limited permissions to access highly privileged service agent identities. This incident underscores how AI platforms can amplify traditional cloud security risks when AI-specific components inherit overly permissive defaults.
What Happened: Incident Overview
Researchers identified multiple privilege escalation paths within Google Cloud’s Vertex AI platform during routine security analysis. The vulnerabilities affected Vertex AI Agent Engine and Ray on Vertex AI, two services used to deploy and manage AI workloads at scale. The findings were publicly disclosed in June 2024 after responsible reporting.
The core issue stemmed from how Vertex AI provisions and manages service agents. These agents are designed to automate AI operations but are often granted broad project-level permissions by default. Researchers demonstrated that users with relatively low-level IAM permissions could inject code or access interactive shells tied to AI infrastructure. From there, they were able to extract service agent credentials from the cloud metadata service.
Once compromised, these service agent tokens enabled access to a wide range of cloud resources, including AI models, training data, logs, and storage. Google acknowledged the behavior, stating that some aspects were operating as designed, placing responsibility on customers to harden configurations.
Sources include reporting from GBHackers and independent cloud security researchers.
How the Breach Happened
The breach was driven by a combination of AI-specific architecture decisions and traditional cloud security weaknesses. In one scenario, users with permission to update reasoning engine components could inject malicious Python code into AI tool execution paths. This code executed within managed AI infrastructure and accessed the instance metadata server to retrieve service agent tokens.
In another case, Ray on Vertex AI exposed an interactive shell on the head node. Users with basic viewer permissions could access this shell through the Google Cloud Console. Because the node ran with elevated privileges, attackers could obtain service agent credentials directly from the environment.
AI amplified the impact by abstracting infrastructure details away from users and administrators. Service agents acted autonomously across training, inference, and orchestration tasks, meaning a single compromised identity could traverse multiple AI lifecycle stages. The complexity of AI pipelines and the reliance on automated agents made excessive permissions harder to detect and easier to exploit.
Impact: Why It Matters
The immediate risk was unauthorized access to sensitive AI workloads and cloud data. Compromised service agents could read or modify training datasets, access stored prompts and model outputs, and interact with downstream services such as BigQuery and Cloud Storage. This creates both data exposure and model integrity risks.
From a business perspective, affected organizations face increased exposure to intellectual property theft, compliance violations, and operational disruption. AI workloads often process regulated or proprietary data, raising concerns related to privacy frameworks and emerging AI governance requirements.
More broadly, the incident highlights a systemic challenge in AI security. Managed AI platforms frequently introduce new privileged components without adequate visibility or guardrails. As regulations like the EU AI Act and frameworks such as NIST AI RMF emphasize accountability and risk management, incidents like this demonstrate how infrastructure misconfigurations can undermine trustworthy AI adoption.
PointGuard AI Perspective
This incident illustrates why AI security requires more than traditional cloud controls. PointGuard AI addresses these risks by providing continuous visibility into AI service identities, permissions, and execution paths across the AI lifecycle.
PointGuard AI maps AI service agents and their effective permissions, exposing excessive access before it becomes exploitable. Continuous monitoring detects anomalous behavior such as unexpected tool execution, metadata access, or credential usage tied to AI workloads. Policy enforcement capabilities help ensure least-privilege access for AI components, reducing the blast radius if a single identity is compromised.
Unlike traditional cloud security tools, PointGuard AI is purpose-built to understand AI workflows, including training pipelines, inference services, and agent-driven automation. This allows organizations to identify hidden trust relationships introduced by managed AI platforms.
As enterprises accelerate AI adoption, proactive AI security controls become essential. By enforcing guardrails around AI identities, execution environments, and data access, PointGuard AI helps organizations move forward with confidence and build AI systems that are secure by design.
Incident Scorecard Details
Total AISSI Score: 7.6/10
Criticality = 8.0
Privilege escalation enabled broad access to production AI and cloud resources.
Propagation = 7.0
Compromised service agents could access multiple services across a project.
Exploitability = 7.5
Attack paths required limited permissions and relied on default configurations.
Supply Chain = 6.5
Risk stemmed from managed AI infrastructure rather than third-party code.
Business Impact = 8.0
Exposure of AI workloads and sensitive data posed significant operational and compliance risk.
Sources
AI Security Severity Index (AISSI)
0/10
Threat Level
Criticality
8
Propagation
7
Exploitability
7.5
Supply Chain
6.5
Business Impact
8
Scoring Methodology
Category
Description
weight
Criticality
Importance and sensitivity of theaffected assets and data.
25%
PROPAGATION
How easily can the issue escalate or spread to other resources.
20%
EXPLOITABILITY
Is the threat actively being exploited or just lab demonstrated.
15%
SUPPLY CHAIN
Did the threat originate with orwas amplified by third-partyvendors.
15%
BUSINESS IMPACT
Operational, financial, andreputational consequences.
25%
Watch Incident Video
