Security audit

Voice Cloning

Security checks for vulnerabilities and agentic risk

Overview

The skill is a transparent voice-cloning helper with consent checks, but users should treat voice samples as sensitive data before uploading them to model providers.

Install only if you are comfortable sending voice samples to the referenced model provider workflow. Use recordings you own or have explicit permission to clone, keep samples short and clean, and check the provider's retention and deletion terms before uploading sensitive or third-party voice data.

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: 92% confidence
Finding: The skill explicitly instructs users to upload reference voice recordings to third-party model services but does not require any privacy notice, retention warning, or minimization guidance for biometric voice data. Voice samples are sensitive personal data, and omission of data-handling disclosures can lead to unauthorized processing, retention, or transfer of a user's or third party's biometric information.

Natural-Language Policy Violations

Medium

Confidence: 79% confidence
Finding: The input guidance asks for a voice description covering gender and age, and later quality criteria require matching those traits. Framing generation around sensitive personal attributes can violate organizational policy depending on deployment context, especially when no limitation or caution is provided.

Static analysis

No suspicious patterns detected.