Breaking Zero Trust Assumptions in AI Workloads: Unauthorized Access to Model APIs

1. Overview / Summary

While reviewing the security of an AI-powered application, we came across a common but risky assumption: internal network traffic was treated as trusted. In this case, AI workloads were accessible to other internal services without strong identity checks, creating a gap in the application’s Zero Trust design.

Because access decisions were based on network location rather than verified workload identity, any internal service including a compromised one  could call AI model inference APIs. That means an attacker who gained a foothold inside the environment wouldn’t need to break additional controls to interact with the models.

The potential impact goes beyond simple misuse. An attacker could extract sensitive inference data, trigger expensive model operations, or interfere with business-critical AI workflows. In cloud-native environments where AI services scale quickly and costs add up fast, this kind of exposure can turn into both a security and financial problem.

This issue is a good reminder that Zero Trust isn’t just about protecting external entry points. AI systems need the same identity-first access controls internally as any other critical service — especially when they power core business functions.

2. Affected Application / Environment

• Application Type: AI-powered service (LLM-based inference API)
• Platform: Cloud-native (Kubernetes + REST APIs)
• AI Components Affected:
  • Model inference endpoint
  • Feature store access
  • Internal AI service APIs
• Authentication Context:
  • Internal services implicitly trusted
  • No service-to-service authentication
• Testing Tools Used:
  • Kubernetes RBAC review
  • Cloud IAM policy analysis
  • API testing using Postman
  • Threat modeling workshops

3. Steps to Reproduce

1. Deploy an AI inference service inside a Kubernetes cluster.
2. Configure the model API to allow access from any internal network source.
3. Do not enforce:
   • Service identity verification
   • Mutual TLS or signed tokens
4. Compromise or gain access to a low-privilege internal pod or service.
5. From the compromised workload, send a direct request to the AI model API.
6. Observe that the request is successfully processed without authentication or authorization.
7. Repeat requests to:
   • Extract inference results
   • Trigger restricted or high-cost AI operations

4. Technical Analysis

Root Cause

The vulnerability occurs due to implicit trust based on network location, violating Zero Trust principles. AI workloads assumed that any internal request was legitimate, without validating who was making the request or whether it was authorized.

Key Contributing Factors

• Missing service-to-service authentication
• Over-privileged or shared service accounts
• Flat network access to AI resources
• Lack of continuous verification

Attack Vector

• Compromised internal workload
• Abused service account or leaked token
• Direct access to AI model APIs

This behavior contradicts NIST Zero Trust Architecture (SP 800-207) and aligns with insecure design patterns highlighted in OWASP Top 10 for LLM Applications – LLM02: Insecure Design.

5. Impact

If exploited, this vulnerability could allow an attacker to:

• Access AI Models Without Authorization
  • Abuse inference endpoints
  • Perform model extraction attempts
• Expose Sensitive Data
  • Inference outputs
  • Training or feature data
• Escalate Privileges
  • Trigger administrative AI operations
• Cause Financial Damage
  • Excessive AI API usage
  • Increased cloud and compute costs
• Business Risk
  • Loss of AI intellectual property
  • Compliance violations
  • Erosion of trust in AI-driven decisions

6. Mitigation / Recommendation

• Enforce Strong Workload Identity
  • Assign unique identities to every AI service and pipeline
  • Use short-lived credentials instead of static secrets
• Authenticate Every AI Request
  • Require authentication for all model inference calls
  • Implement mutual TLS (mTLS) or signed service tokens
• Apply Fine-Grained Authorization
  • Restrict access per model, dataset, and action
  • Enforce least-privilege policies
• Microsegment AI Infrastructure
  • Isolate training, inference, and data layers
  • Prevent lateral movement between workloads
• Continuous Verification & Monitoring
  • Log all AI model access
  • Detect anomalous usage and cost spikes

AI model APIs should be treated as Tier-0 assets, similar to identity or payment systems.

7. References

• NIST – SP 800-207: Zero Trust Architecture
• OWASP – Top 10 for LLM Applications
• Cloud Security Alliance – Zero Trust Guidance
• Google BeyondCorp Zero Trust Model

8. Closing Note

AI workloads dramatically expand the attack surface when they rely on implicit trust. Once an internal system is compromised, poorly enforced Zero Trust controls turn AI platforms into high-impact attack amplifiers.

Zero Trust for AI is not about perimeter hardening — it’s about continuous identity verification, least privilege, and enforcement at every inference call.