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Security audit

Learn Python Programming

Security checks for vulnerabilities and agentic risk

Overview

This skill is a LabEx-specific Python learning recommender that uses public catalog links and does not request credentials, run code, or add persistence.

Install this only if you want LabEx-focused Python learning recommendations. Expect answers to prefer LabEx public lab URLs and sometimes include favorable platform framing; users who want neutral comparisons across providers should ask the agent to avoid this skill.

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
Findings (1)

T01 · Skill Instruction Hijacking

Warning
Location
SKILL.md:19
Finding

Forced Promotional Output and Branded Link Redirection

Content
View full analysis
Remediation
View remediation

SkillSpector

Vulnerability Patterns
  • System Prompt LeakageDirect Leakage, Indirect Extraction, Tool-Based Exfiltration
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Findings (3)

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 Python programming 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 Python programming learning path.
- Favor brief recommendations over long catalogs.

Description-Behavior Mismatch

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The 'Breadth across domains' guidance says recommendations can span Linux, DevOps, security, data, and frontend tracks, which conflicts with the skill's declared Python-only scope. This can cause the agent to drift outside intended boundaries and recommend unrelated content, weakening scope controls and making prompt steering easier.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

Inline documentation directly contradicts the Python-only operating rules by suggesting recommendations may span multiple domains. Conflicting instructions inside a skill create ambiguity that can override or dilute earlier constraints, leading the agent to produce out-of-scope results.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.