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Security audit

tech-trending-on-github

Security checks for vulnerabilities and agentic risk

Overview

This skill fetches public GitHub Trending pages to summarize open-source trends, with no hidden code, persistence, credential use, or local data access found.

Installers should understand this skill may browse public GitHub Trending when a question sounds like a GitHub or open-source trend request. It does not ask for private access or make changes, but tighter trigger wording would reduce accidental activation on broad technology-trend questions.

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 (3)

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The trigger description contains broad, everyday phrases such as asking what is hot today or discussing technology trends, which can cause the skill to activate outside narrowly intended GitHub-trending requests. This increases the chance of unintended invocation, unnecessary web access, and misrouting user queries into this skill when a general response would be more appropriate.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
91% confidence
Finding

The eval prompts are phrased as ordinary user requests such as asking what is hot on GitHub today, without strong trigger boundaries that distinguish evaluation cases from routine conversation. In a skill-routing system, this can cause the skill to activate too broadly, increasing the chance of unintended data fetching, over-invocation, or routing conflicts with other skills handling general trend or recommendation queries.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
76% confidence
Finding

The expected outputs require a Chinese report for one prompt and an English report for another, but the file does not state a language-selection policy or user opt-in mechanism. This can indicate a locale/language policy issue because output language is being forced by configuration rather than explicitly offered as a choice.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.