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Security audit

AI Reliability Layer

Security checks for vulnerabilities and agentic risk

Overview

This skill is a disclosed paid API helper for repairing and validating AI output, with no local code execution or persistence, but users should avoid sending sensitive content to the services.

Use this for non-sensitive AI output repair and validation. Before sending proprietary prompts, personal data, secrets, regulated data, or confidential business content, verify who operates the service, how requests are logged or retained, and the payment details for x402 USDC on Base.

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 (1)

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The skill explicitly sends prompts, schemas, and model outputs to external paid micro-services, but it does not warn users that potentially sensitive data will leave the local agent boundary and be transmitted to third-party infrastructure. This can lead to unintentional disclosure of secrets, personal data, proprietary prompts, or regulated information, especially because the skill is framed as a generic reliability layer likely to be used on arbitrary LLM output.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.