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Security audit

Ollama Toolkit Free

Security checks for vulnerabilities and agentic risk

Overview

This Ollama helper is mostly purpose-aligned, but it includes under-warned commands that can expose an unauthenticated local AI service to the network and delete installed models.

Install only if you intend to let the agent run local Ollama commands. Do not use the LAN exposure command unless you have already restricted access with firewall rules or an authenticated proxy, and require explicit confirmation before deleting models or starting a network-accessible service.

Vulnerability Patterns
  • Insecure Skill Coding PracticesFinds exploitable flaws such as hardcoded secrets or command injection
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
  • Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
  • Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
  • Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
Findings (1)

T09 · Insecure Skill Coding Practices

Error
Location
SKILL.md:191
Finding

Unauthenticated Ollama API Exposed on All Network Interfaces

Content
View full analysis

Vulnerability Details

File Location: SKILL.md, lines 191–194
Vulnerability Type: Unauthenticated network service exposure
Risk Level: High

Vulnerable Code Snippet:

bash
# Check whether the service is operating normally through the API
curl http://localhost:11434/api/tags
# ...
# Configure the service listening address to allow LAN access
OLLAMA_HOST=0.0.0.0:11434 ollama serve

The security-relevant context at line 252 states that the Ollama service requires no authentication by default:

text
The Ollama service listens on localhost:11434 by default and requires no authentication.

Technical Analysis

The Skill recommends setting OLLAMA_HOST=0.0.0.0:11434, which causes Ollama to listen on every available network interface instead of only the loopback interface. The document also acknowledges that the service has no authentication by default.

Combining an all-interface bind address with an unauthenticated API creates an externally reachable service whenever host or network firewall rules permit traffic to TCP port 11434. Although the Skill later recommends restricting access with firewall rules, the demonstrated command does not require those controls to be established before exposure occurs and does not place the service behind authentication or transport encryption.

A remote party capable of reaching port 11434 may submit requests to the API functions supported by the installed Ollama version. This can permit model enumeration, inference requests, resource-intensive operations, and interaction with available model-management endpoints.

Attack Path

  1. A user follows the service-management example and runs:
    bash
    OLLAMA_HOST=0.0.0.0:11434 ollama serve
    
  2. Ollama begins listening on port 11434 across all host interfaces without application-level authentication.
  3. The host is connected to a LAN or another network where an untrusted party can reach that ...[truncated 1491 chars]
Remediation
View remediation

Remediation Suggestions

  1. Keep Ollama bound to the loopback interface by default:

    bash
    OLLAMA_HOST=127.0.0.1:11434 ollama serve
    
  2. Remove the unconditional 0.0.0.0 example or mark it as unsafe unless all prerequisite controls have already been applied.

  3. If remote access is required, place Ollama behind a hardened reverse proxy that provides:

    • Strong authentication and authorization.
    • TLS encryption.
    • Request-size and rate limits.
    • Connection and execution timeouts.
    • Access logging and monitoring.
    • Restrictions on model-management endpoints.
  4. Configure host and network firewalls before changing the bind address. Permit TCP port 11434 only from explicitly trusted source addresses or private management networks.

  5. Require explicit user confirmation before the Agent starts a network-accessible service, and clearly report the selected bind address and lack of native authentication.

  6. Run Ollama under a dedicated, unprivileged operating-system account with limited filesystem access and resource quotas.

  7. Prefer a VPN or authenticated tunnel rather than exposing the API directly to a LAN or the public Internet.

  8. Add verification steps after startup to confirm that the service is not reachable from unauthorized interfaces or hosts.

Vulnerability Patterns
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Findings (7)

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The activation guidance is very broad, covering generic AI model use, smart dialogue, orchestration, and LLM applications. Overly broad triggers increase the chance the skill is auto-selected in contexts where exec-enabled local command execution is unnecessary or risky, expanding the attack surface.

Content

No source excerpt is available for this finding.

Description-Behavior Mismatch

Medium
Category
Not specified by scanner
Confidence
90% confidence
Finding

The description frames the free skill as limited to model query, download, and basic dialogue, but later includes commands for model deletion and service startup/exposure. That mismatch broadens effective authority beyond user expectations, increasing the chance an agent performs destructive or system-altering actions under a seemingly limited skill.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The skill states batch inference is unsupported, but also recommends pipeline-based bulk text processing. This inconsistency can cause agents to rely on unsupported behavior, potentially processing larger or more sensitive data sets than intended without the safeguards expected for batch operations.

Content

No source excerpt is available for this finding.

Description-Behavior Mismatch

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The skill claims the free edition does not support REST/API service, yet it documents direct HTTP API access and shows how to bind the Ollama service to 0.0.0.0 for LAN exposure. This inconsistency can mislead an agent or user into enabling unintended network access and invoking capabilities outside the stated safety boundary.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The keyword coverage section defines an excessively broad trigger scope with many generic terms, making accidental invocation likely. In an exec-capable skill, broad activation materially increases risk because unrelated prompts may lead the agent to run local commands or alter local AI service state.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The skill includes a destructive command to remove installed models without any explicit warning or confirmation requirement. In an agent setting, this can cause unintended data deletion, bandwidth waste from re-downloading models, and service disruption if a needed model is removed.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The documented command binds Ollama to 0.0.0.0, exposing the service to the local network without a prominent security warning. Since the skill also notes the service has no authentication by default, this can create unauthorized access to local model inference and potentially expose sensitive prompts or outputs to other hosts on the LAN.

Content

No source excerpt is available for this finding.