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Security audit

体检报告解读话术台

Security checks for vulnerabilities and agentic risk

Overview

This is a plain Markdown medical-report explanation skill with disclosed limits and no executable behavior, though users should be careful because it handles sensitive health information.

Install only if you are comfortable sharing health-report details in the conversation. Treat outputs as preparation for a doctor visit, not as a diagnosis or medication plan, and be explicit when invoking the skill so generic health discussion is not accidentally interpreted as a report-analysis request.

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
91% confidence
Finding

The trigger keywords are broad, generic phrases like '指标偏高' and '体检异常' that are likely to appear in ordinary medical discussions. This can cause unintended invocation of the skill in unrelated conversations involving sensitive health content, increasing the chance of overreach or accidental processing of medical information.

Content

No source excerpt is available for this finding.

Vague Triggers

Low
Category
Not specified by scanner
Confidence
82% confidence
Finding

The natural-language examples are open-ended and conversational, which may encourage activation from loosely related follow-up messages such as '按上面的结果改成更简短的版本'. In a medical context, unintended continuation is risky because it can keep the skill engaged with health-related content without a fresh, explicit user request.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.