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Security audit

就医准备清单台

Security checks for vulnerabilities and agentic risk

Overview

This is a simple medical-visit preparation checklist skill with no code, network access, persistence, or hidden data handling.

Before installing, users should understand that this skill is for organizing information before a medical visit, not for diagnosis. Because it may handle sensitive health details, share only what is necessary and avoid entering identifying information unless needed for the conversation.

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
Vulnerability Patterns
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • 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)

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The trigger keywords are broad, common-language phrases such as '看病前准备' and '怎么跟医生说' that could match ordinary user conversation and activate the skill unintentionally. In a medical-context skill, accidental activation can cause users to disclose sensitive health information to the wrong workflow or receive prep-oriented output when they did not intend to invoke this skill.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
88% confidence
Finding

The natural-language examples are open-ended and underspecified, for example '帮我看病前把该带的、该说的、该问的准备好' and '把这些整理成一张表', which could overlap with many normal chat requests. This increases the chance of unintended routing and, in this healthcare context, may expose private medical details or produce advice-like outputs without a clearly scoped request.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.