Security audit

Automated Tweet Scheduler

Security checks for vulnerabilities and agentic risk

Overview

This is a clearly described Twitter/X scheduling skill with expected public-posting and media-upload behavior, but users should confirm account access, posting approvals, and storage details before use.

Before installing, confirm which Twitter/X account the skill can access, whether each scheduled post or media upload requires approval, where uploaded media and analytics are stored, and how to cancel scheduled posts or delete stored content.

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

SkillSpector

Vulnerability Patterns

Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep

Findings (2)

Missing User Warnings

Medium

Confidence: 91% confidence
Finding: The skill describes scheduling tweets, uploading media, and using external services like Twitter API and AWS S3, but it does not warn users that their content will be transmitted to third-party platforms and may be published automatically. This can lead to unintended disclosure, accidental posting, or user surprise about where data and media are sent, which is a real safety and privacy issue for a social-posting automation skill.

Natural-Language Policy Violations

Low

Confidence: 73% confidence
Finding: The file presents the skill in a mixed Chinese-English locale throughout, but does not indicate that users can choose their preferred language or locale. Under the stated policy, forcing a specific language presentation without opt-in can be a natural-language policy concern.

Static analysis

No suspicious patterns detected.