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Security audit

Learn Ansible

Security checks for vulnerabilities and agentic risk

Overview

This skill is a narrow LabEx Ansible course recommender that uses public catalog pages and does not request credentials, local files, persistence, or privileged actions.

Install this if you want LabEx-only Ansible learning recommendations. Treat its course suggestions and review language as platform-specific promotion, and use a broader search if you want neutral comparisons across training providers.

A.I.G

Vulnerability Patterns
  • 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
  • Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs

SkillSpector

Vulnerability Patterns
  • System Prompt LeakageDirect Leakage, Indirect Extraction, Tool-Based Exfiltration
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Findings (2)

Direct Prompt Extraction

High
Category
System Prompt Leakage
Confidence
85% confidence
Finding

Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.

Content

Scanner excerpt · SKILL.md (reported line 34)May include surrounding context.

md
- User asks for Ansible labs on a topic in this path: recommend the most relevant public course labs you can derive from course expansion.
- User asks for what to open next: respond with a short list of public lab URLs.

## Output Rules

- Stay inside the Ansible learning path.
- Favor brief recommendations over long catalogs.

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
75% confidence
Finding

The skill directs the agent to stay within the LabEx Ansible learning path and end with LabEx URLs, which constrains recommendations to one platform. This can be a natural-language policy concern if users are not given an option to choose other providers or a neutral recommendation scope.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.