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Security audit

AI提示词工程专家

Security checks for vulnerabilities and agentic risk

Overview

This is a Markdown-only prompt-engineering guidance skill with broad but disclosed activation language and no code, network access, persistence, or hidden data handling.

Install this if you want Chinese-language prompt-engineering help. Be aware it may activate for many prompt-related requests and tends to answer in Chinese, so users who need another language or very narrow routing may want to adjust those preferences.

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
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • 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 (4)

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The trigger list is very broad and includes common phrases about getting better AI answers, prompt writing, and output formatting. This can cause the skill to activate for routine requests outside its intended scope, leading to prompt hijacking of normal conversations and reducing routing precision.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The matching conditions rely on ambiguous high-level concepts such as 'AI提问', 'AI回答不好', and '控制输出格式' without strong activation boundaries. In practice this increases accidental invocation risk, allowing the skill to intercept broad categories of harmless user requests and override more appropriate specialized skills.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

Forcing Chinese output regardless of user preference can cause instruction conflict, user confusion, and unreliable downstream behavior in multilingual environments. While not directly a security exploit, it can degrade control alignment and produce incorrect handling when another language is required for safety, compliance, or interoperability.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

Line L846 recommends fixing multilingual issues by explicitly requiring the model to use Chinese or English throughout. This is a natural-language locale policy constraint that appears to prescribe a fixed language choice rather than offering the user a choice or documenting a justified locale limitation.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.