Pywayne Llm Chat Ollama Gradio

• Outcome: Review
• Latest audit: Sep 10, 2026 · 7:53 PM UTC
• Version: 0.1.0

Overview

The skill is a coherent Ollama Gradio chat helper, but its documented default can expose an unauthenticated chat server to the network.

Install only if you are comfortable running a local web chat UI for Ollama. For normal local use, set server_name to 127.0.0.1 or localhost, avoid exposing port 7870 publicly, and add authentication or a trusted reverse proxy before allowing remote access.

A.I.G

Vulnerability Patterns

• Insecure Skill Coding Practices: Finds exploitable flaws such as hardcoded secrets or command injection
• Skill Instruction Hijacking: Alters the agent's session goals or safety constraints when the skill loads
• Agent Memory Poisoning: Writes attacker-controlled rules into memory that affect later sessions
• Remote Payload Retrieval and Execution: Fetches external code whose behavior can change after review
• Embedded Malicious Code: Ships malicious scripts inside the skill and executes them locally

Findings (1)

T09 · Insecure Skill Coding Practices

Warning

• Location: SKILL.md:16

Finding

Gradio Interface Defaults to Unauthenticated Exposure on All Network Interfaces

Content

View full analysis

Vulnerability Details

File Location: SKILL.md, lines 16-28
Vulnerability Type: Insecure network binding and missing access-control guidance
Risk Level: Medium

The documented configuration sets server_name to 0.0.0.0, and the quick-start example launches the application without overriding that default or configuring authentication.

python

app = OllamaChatGradio(
    base_url="http://localhost:11434/v1",
    server_port=7870
)
app.launch()

markdown

| Parameter | Default | Description |
|-----------|---------|-------------|
| `base_url` | `"http://localhost:11434/v1"` | Ollama API base URL |
| `server_name` | `"0.0.0.0"` | Server host name |

Technical Analysis

Binding a service to 0.0.0.0 makes it listen on every available IPv4 network interface rather than limiting access to the local host. The quick-start workflow does not specify a loopback-only address, application authentication, or an authenticated reverse proxy.

If the host firewall or surrounding network permits access to port 7870, other network users can reach the Gradio interface. Because the audited project contains only documentation, the underlying implementation and any framework-level protections could not be verified. The finding therefore applies specifically to the insecure deployment configuration promoted by the skill documentation.

Attack Path

1. An operator follows the documented quick-start example.
2. The application uses the documented default server_name="0.0.0.0" and listens on port 7870 across all IPv4 interfaces.
3. The host firewall, container port mapping, cloud security group, or local network permits access to that port.
4. A network-adjacent or remotely routed attacker discovers the exposed service.
5. The attacker connects to the Gradio interface without a documented authentication requirement.
6. The attacker submits model requests and consumes local Ollama compute re ...[truncated 727 chars]

Remediation

View remediation

Remediation Suggestions

1. Change the default bind address to the loopback interface:

   python

   app = OllamaChatGradio(
       base_url="http://localhost:11434/v1",
       server_name="127.0.0.1",
       server_port=7870
   )
   app.launch()
   

2. Require users to opt in explicitly before binding to 0.0.0.0.

3. If remote access is required, place the application behind an authenticated reverse proxy with TLS.

4. Configure host firewalls, cloud security groups, and container port mappings to allow access only from trusted networks.

5. Add application-level authentication and authorization rather than relying exclusively on network location.

6. Document the security consequences of public binding and provide separate secure examples for local-only and remote deployments.

7. Verify that chat sessions are isolated per authenticated user and cannot be enumerated or switched by unrelated clients.

8. Apply request rate limits and resource quotas to reduce denial-of-service and unauthorized compute-consumption risks.

SkillSpector

Vulnerability Patterns

• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep

Findings (1)

Natural-Language Policy Violations

Low

• Category: Not specified by scanner
• Confidence: 90% confidence

Finding

The markdown uses Chinese UI strings such as "新建会话" and "历史会话" in the usage instructions, which implies a fixed interface language. The file does not state that the skill is Chinese-only, region-specific, or that users can select a preferred language.

Content

No source excerpt is available for this finding.