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Strengthening Ollama AI Inference APIs Against Unauthorized Access Through Model Endpoint Authentication and Inference Request Monitoring

Description

Modern organizations are increasingly deploying Edge AI and locally hosted Large Language Models (LLMs) to perform AI inference directly within their own infrastructure.

Local AI platforms allow organizations to run AI models without sending every inference request to an external cloud-based AI service. However, exposing AI inference APIs without appropriate authentication and access controls can allow unauthorized users or applications to interact with the AI model.

In this use case, Ollama is deployed as the controlled local AI inference application on an Ubuntu Linux virtual machine inside an isolated VirtualBox laboratory.

Ollama provides a local API through which applications can interact with locally hosted AI models.

A controlled unauthorized API-access attack is simulated from Kali Linux against the Ollama inference API.

The assessment evaluates whether an unauthorized client can access the AI inference endpoint and submit inference requests without proper authorization.

OWASP ZAP is used to inspect API communication, while cURL is used to perform controlled API requests. Wireshark is used for network analysis. Wazuh is used for security monitoring, and OpenSearch is used for centralized investigation.

After identifying the API-access weakness, authentication and endpoint-access controls are implemented. The same unauthorized-access test is repeated to verify that unauthorized inference requests are rejected while legitimate AI applications continue to use the Ollama service.

Existing Security Problem

Application: Ollama

Ollama is the real open-source application used in this project.

It allows locally hosted AI models to be executed and accessed through an API.

Existing Problem:

An AI inference API should not be exposed to unauthorized clients. If an Ollama API endpoint is accessible without appropriate network restrictions or authentication controls, an unauthorized user may be able to send inference requests to the locally hosted AI model.

The security problem is therefore:

Unauthorized Client → Ollama API → Insufficient Access Control → Inference Request → AI Model → Inference Response

Attack

Specific Attack: Unauthorized Access to Ollama AI Inference API

The controlled attack evaluates whether an unauthorized laboratory client can access the Ollama API and submit inference requests without the required authorization. The attack is performed exclusively against the locally deployed Ollama environment. No external AI services, production systems, or real organizational data are involved.

Attack Behavior:
Ollama AI Server
→
Local AI Inference API
→
API Endpoint Exposed
→
Unauthorized Kali Linux Client
→
Controlled API Request
→
Ollama API
→
Inference Request Accepted
→
AI Model Processes Request
→
Unauthorized Inference Access
→
Wazuh Detection
→
OpenSearch Investigation
→
API Authentication + Access-Control Remediation
→
Retesting
→
Unauthorized Request Rejected

Security Concept

AI Inference API Security and Access Control:

The primary security concept is secure access control for locally hosted AI inference APIs.

AI inference endpoints should accept requests only from authorized clients. The security assessment evaluates client identity, API endpoint exposure, authentication state, request source, inference requests, request timestamps, API responses, security events, and access-control decisions.

The secure processing flow is:

AI Client
→
Authentication
→
API Authorization
→
Request Validation
→
AI Model Inference
→
Response

Defensive Mechanism

API Authentication

Require authentication before allowing clients to access protected AI inference endpoints.

Purpose

Prevent unauthorized clients from directly interacting with the AI model.

API Access Control

Restrict access to the Ollama inference API to authorized clients.

Purpose

Prevent unauthorized applications or users from accessing the model endpoint.

Network-Level Access Restriction

Restrict access to the AI API to trusted systems or network segments.

Purpose

Reduce unnecessary exposure of the local inference service.

Inference Request Validation

Validate incoming inference requests before processing them.

Purpose

Prevent invalid or unauthorized requests from reaching the AI model.

Endpoint Exposure Reduction

Avoid exposing the Ollama API beyond the systems that require access.

Purpose

Reduce the attack surface of the local AI infrastructure.

Request Monitoring

Monitor inference requests and API activity.

Purpose

Detect unusual or unauthorized AI API usage.

Network Traffic Monitoring

Analyze network communication involving the AI inference service.

Purpose

Identify unexpected clients and abnormal API access patterns.

Security Event Logging

Record relevant API-access and authentication events.

Purpose

Support detection and investigation of unauthorized AI service access.

Centralized Security Investigation

Use Wazuh and OpenSearch to investigate monitored events.

Purpose

Correlate AI API activity and establish an incident timeline.

Post-Remediation Validation

Repeat the unauthorized API-access test after implementing security controls.

Purpose

Confirm that unauthorized clients can no longer access the protected inference endpoint.

Security Tools

Target Application: Ollama

Ollama is the real open-source local AI application used in this project.

Purpose
  • Host local AI models.
  • Provide AI inference functionality.
  • Expose the local inference API.
  • Process controlled inference requests.
  • Demonstrate Edge AI security.

API Security Testing Tool: OWASP ZAP

OWASP ZAP is used to inspect API communication.

Purpose
  • Inspect API requests.
  • Analyze API responses.
  • Review authentication-related behavior.
  • Identify exposed endpoints.
  • Support controlled API security testing.

API Request Tool: cURL

cURL is used from Kali Linux to perform controlled API requests.

Purpose
  • Send legitimate API requests.
  • Send controlled unauthorized requests.
  • Test endpoint accessibility.
  • Compare authorized and unauthorized behavior.
  • Validate post-remediation access controls.

Network Analysis Tool: Wireshark

Wireshark is used to analyze network communication involving the Ollama API.

Purpose
  • Capture laboratory traffic.
  • Identify API communication.
  • Observe client-server interaction.
  • Compare authorized and unauthorized activity.
  • Support post-remediation validation.

Security Monitoring Tool: Wazuh

Wazuh is used for security monitoring.

Purpose
  • Monitor Ubuntu activity.
  • Monitor relevant application activity.
  • Collect security events.
  • Detect suspicious API access.
  • Generate security alerts.

Investigation Platform: OpenSearch

OpenSearch is used to investigate security events collected through Wazuh.

Purpose
  • Search security events.
  • Correlate API-access activity.
  • Review timestamps.
  • Investigate unauthorized requests.
  • Establish an attack timeline.

Security Testing Platform: Kali Linux

Kali Linux is used as the authorized security-testing environment.

Purpose
  • Run cURL.
  • Run OWASP ZAP.
  • Perform controlled API testing.
  • Capture network traffic.
  • Validate remediation.

Target Platform: Ubuntu Linux

Ubuntu hosts the Ollama environment.

Purpose
  • Run Ollama.
  • Host the local AI model.
  • Maintain API configuration.
  • Generate application activity.
  • Support security monitoring.

Virtualization Platform: VirtualBox

VirtualBox provides the isolated cybersecurity laboratory.

Purpose
  • Host Ubuntu.
  • Host Kali Linux.
  • Provide isolated networking.
  • Maintain a reproducible AI security environment.

Process

STEP 01

Prepare the Isolated AI Security Laboratory

  • Install VirtualBox.
  • Create an Ubuntu virtual machine.
  • Create a Kali Linux virtual machine.
  • Configure an isolated virtual network.
  • Assign laboratory IP addresses.
  • Verify connectivity between the virtual machines.
  • Ensure the environment is isolated from production systems.
Tools: VirtualBox + Ubuntu + Kali Linux
STEP 02

Deploy Ollama

  • Install Ollama on Ubuntu.
  • Start the Ollama service.
  • Verify that the service is running.
  • Confirm that the Ollama API is available locally.
  • Verify communication from the authorized laboratory system.
Tools: Ollama
STEP 03

Deploy a Local AI Model

  • Download an appropriate open-source model supported by Ollama.
  • Load the model into the Ollama environment.
  • Verify that the model can process a normal inference request.
  • Record the normal inference behavior.
Tools: Ollama
STEP 04

Establish Normal AI API Communication

  • Use cURL from Kali Linux.
  • Send a legitimate inference request to the Ollama API.
  • Verify that Ollama receives the request.
  • Verify that the AI model generates an inference response.
  • Record the normal API behavior.
Tools: Kali Linux + cURL + Ollama
STEP 05

Identify the Inference API Endpoint

  • Identify the Ollama API endpoint used for inference.
  • Determine the normal request structure.
  • Identify the required request parameters.
  • Record the legitimate API communication flow.
  • Establish the API security-testing baseline.
Tools: Ollama + cURL
STEP 06

Capture Normal API Traffic

  • Start Wireshark on the laboratory network interface.
  • Generate legitimate inference requests.
  • Capture the communication between Kali Linux and Ubuntu.
  • Stop the capture.
  • Preserve the packet capture for comparison.
Tools: Wireshark + Ollama
STEP 07

Inspect API Communication

  • Configure OWASP ZAP for the laboratory API traffic.
  • Start OWASP ZAP.
  • Access the Ollama API through the controlled environment.
  • Inspect API requests and responses.
  • Identify authentication and access-control behavior.
  • Record the normal API communication.
Tools: OWASP ZAP
STEP 08

Introduce the Controlled API-Access Weakness

  • Within the isolated laboratory, temporarily configure the Ollama service to permit broader network access than intended.
  • Record the secure configuration before modification.
  • Record the intentionally weakened configuration.
  • Verify that the test client can reach the exposed endpoint.
  • Keep the environment isolated.
  • The purpose is to safely reproduce an AI inference API exposure scenario.
Tools: Ollama
STEP 09

Perform the Controlled Unauthorized API-Access Test

  • Using the unauthorized laboratory client, attempt to connect to the Ollama inference endpoint.
  • Send a controlled inference request.
  • Observe whether the request is accepted.
  • Record the response.
  • Determine whether the unauthorized client can interact with the AI model.
Tools: Kali Linux + cURL + Ollama
STEP 10

Analyze Unauthorized API Traffic

  • Capture the unauthorized API request using Wireshark.
  • Identify the client-server communication.
  • Compare unauthorized traffic with normal authorized traffic.
  • Document the observed behavior.
  • Preserve the laboratory packet capture.
Tools: Wireshark
STEP 11

Analyze API Behavior

  • Using OWASP ZAP, review the unauthorized API request.
  • Analyze the API response.
  • Compare authorized and unauthorized requests.
  • Determine whether access control is enforced.
  • Document the security finding.
Tools: OWASP ZAP
STEP 12

Configure Security Monitoring

  • Identify relevant Ubuntu and Ollama activity.
  • Configure Wazuh monitoring.
  • Generate normal inference activity.
  • Generate the controlled unauthorized API request.
  • Verify that Wazuh receives relevant telemetry.
  • Review the collected security events.
Tools: Wazuh
STEP 13

Detect Unauthorized AI API Access

  • Review Wazuh events.
  • Identify the unauthorized API activity where observable.
  • Review the event timestamp.
  • Identify the affected Ubuntu system.
  • Correlate related events.
  • Preserve the security evidence.
Tools: Wazuh
STEP 14

Investigate the Attack Activity

  • Open relevant Wazuh events in OpenSearch.
  • Search for Ollama-related API activity.
  • Review timestamps.
  • Correlate API and system events.
  • Identify the sequence of unauthorized API requests.
  • Establish the attack timeline.
Tools: Wazuh + OpenSearch
STEP 15

Assess the Security Risk

  • Evaluate Ollama API exposure.
  • Evaluate authentication requirements.
  • Evaluate network access controls.
  • Evaluate unauthorized inference behavior.
  • Evaluate client authorization.
  • Evaluate monitoring visibility.
  • Evaluate potential AI-service misuse.
  • Evaluate potential resource-consumption impact.
  • Evaluate remediation requirements.
  • Document the security finding and assign an appropriate risk level.
Tools: Ollama + OWASP ZAP + Wireshark + Wazuh + OpenSearch
STEP 16

Remediate AI Inference API Access

  • Restore the secure Ollama configuration.
  • Restrict API exposure to the required interface or trusted systems.
  • Apply appropriate network access restrictions.
  • Implement authentication or an authenticated gateway where required by the deployment architecture.
  • Restrict access to authorized clients.
  • Validate the corrected configuration.
Tools: Ollama + Ubuntu
STEP 17

Retest Unauthorized and Authorized API Access

  • Unauthorized Access Retest: Use the unauthorized laboratory client.
  • Attempt to access the Ollama inference endpoint.
  • Send a controlled inference request.
  • Verify that unauthorized access is rejected or blocked.
  • Record the result.
  • Authorized Access Retest: Use the authorized laboratory client.
  • Send a legitimate inference request.
  • Verify that Ollama accepts the request.
  • Confirm that the AI model continues to generate the expected response.
Tools: Kali Linux + cURL + Ollama
STEP 18

Perform Final AI Security Validation

  • Review the original Ollama configuration.
  • Review the controlled unauthorized API-access evidence.
  • Review OWASP ZAP results.
  • Review Wireshark traffic analysis.
  • Review Wazuh security events.
  • Review OpenSearch investigation results.
  • Verify the corrected API-access configuration.
  • Confirm unauthorized API requests are rejected or blocked.
  • Confirm authorized AI inference requests continue to function.
  • Document the final Emerging Technology Security assessment.
Tools: Ollama + OWASP ZAP + cURL + Wireshark + Wazuh + OpenSearch + Kali Linux

Outcome

  1. Ollama is successfully deployed as a real open-source local AI inference application within an isolated Ubuntu-based laboratory.
  2. A local open-source AI model is successfully deployed through Ollama and accessed through its inference API.
  3. A controlled unauthorized access to the Ollama AI inference API scenario is successfully simulated using cURL from Kali Linux.
  4. OWASP ZAP successfully provides API-layer visibility into the Ollama inference requests and responses.
  5. Wireshark successfully captures and analyzes authorized and unauthorized API communication within the controlled environment.
  6. Wazuh successfully monitors relevant Ubuntu and AI-service activity and provides security-event visibility.
  7. OpenSearch successfully supports centralized investigation and correlation of monitored AI API-access events.
  8. The identified AI inference API exposure is remediated through appropriate API access controls, network restrictions, and authentication mechanisms where required.
  9. Post-remediation testing confirms that unauthorized clients cannot access the protected Ollama inference API, while authorized clients can continue to perform legitimate AI inference.
  10. The complete Edge AI security assessment, unauthorized API-access simulation, detection, investigation, API access-control remediation, and post-remediation validation is successfully demonstrated as an Emerging Technology Security use case.
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