Back to skill

Security audit

Vibe Codebase Audit

Security checks across malware telemetry and agentic risk

Overview

The skill is a real security-audit tool, but it can send project contents to external AI services and includes broad auto-run examples without enough consent controls.

Review this before installing in sensitive environments. Use the local scanner or a local model for private repositories unless your organization has approved sending code to OpenRouter, OpenAI-compatible providers, or Anthropic. Disable automatic pre-commit/pre-publish triggers unless they are scoped to approved repositories, consider turning off caching for sensitive projects, and run dependency scans in a sandbox for untrusted codebases.

SkillSpector

By NVIDIA
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • Behavioral ASTexec() Call, eval() Call, Dynamic Import
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Findings (34)

subprocess module call

Medium
Category
Dangerous Code Execution
Content
package_json = project_path / "package.json"
        if package_json.exists():
            try:
                result = subprocess.run(
                    ["npm", "audit", "--json"],
                    cwd=project_path,
                    capture_output=True,
Confidence
62% confidence
Finding
result = subprocess.run( ["npm", "audit", "--json"], cwd=project_path, capture_output=True, text=True,

Missing User Warnings

Medium
Confidence
91% confidence
Finding
The installation and usage section encourages users to run a codebase auditing tool with external provider support, including OpenAI, Claude, DeepSeek, and Qwen, but does not warn that project files or snippets may be transmitted off-host during analysis. Because the report also emphasizes '无需API key' and 'SAFE', users may incorrectly assume there are no privacy implications, increasing the risk of unintended disclosure of proprietary source code, secrets, or regulated data.

Missing User Warnings

Medium
Confidence
91% confidence
Finding
The document actively encourages installation and use of a codebase auditing skill, including cloud LLM providers, but does not warn that source code, configuration, dependency manifests, or secrets may be transmitted to external services during analysis. In a security tool context, this omission is risky because users may assume local-only processing and inadvertently expose proprietary or sensitive data.

Missing User Warnings

Medium
Confidence
94% confidence
Finding
The README instructs users to set an OpenRouter API key and run a multi-model audit over a project path, which strongly implies project contents may be transmitted to an external service. Because the documentation does not clearly warn that source code, secrets, or personal data could leave the local environment, users may unknowingly expose sensitive repositories during security analysis.

Missing User Warnings

Medium
Confidence
94% confidence
Finding
The README promotes agent-native and third-party provider-based auditing of an entire codebase but does not warn users that proprietary source code, secrets, or sensitive configuration may be sent to external LLM services. In a security-audit tool, this omission is significant because users may reasonably assume analysis is local or privacy-preserving, leading to unintended data disclosure during use.

Missing User Warnings

Medium
Confidence
93% confidence
Finding
La documentación describe el uso de proveedores externos como OpenAI, Claude, DeepSeek y Ollama, pero no advierte que una auditoría de código puede transmitir fragmentos de código, rutas, dependencias o metadatos del proyecto a esos servicios. En un contexto de auditoría de seguridad, esta omisión puede llevar a exponer información sensible sin consentimiento explícito del usuario.

Missing User Warnings

Medium
Confidence
96% confidence
Finding
La opción recomendada "agent_llm" se presenta como "sin configuración" y "sin clave API", lo que puede hacer que el usuario asuma erróneamente que no hay transferencia de datos fuera del proyecto o del entorno local. En realidad, el LLM del agente podría tener acceso amplio al contenido auditado o reenviar datos a infraestructura remota, creando un riesgo de confidencialidad no documentado.

Missing User Warnings

Medium
Confidence
92% confidence
Finding
La documentation présente l'utilisation de fournisseurs externes (OpenAI, Claude, DeepSeek, Ollama) mais n'indique pas clairement que l'audit peut transmettre du code, des secrets ou d'autres données sensibles du projet à ces services. Dans un outil d'audit de sécurité, cette omission peut conduire les utilisateurs à envoyer involontairement du contenu confidentiel vers des tiers, ce qui crée un risque réel de confidentialité et de conformité.

Missing User Warnings

Medium
Confidence
95% confidence
Finding
La section "Audit Natif d'Agent" met en avant l'absence de clé API et la simplicité d'usage, mais ne précise pas que le code analysé peut tout de même être partagé via la connexion LLM de l'agent courant. Cela est particulièrement sensible car la formulation "sans configuration" peut donner une impression trompeuse de traitement purement local ou sans exposition externe.

Missing User Warnings

Medium
Confidence
91% confidence
Finding
The documentation recommends executing a remote install script via 'curl ... | sh' without any integrity, provenance, or review warning. This pattern is dangerous because users may run unverified code directly in their shell, enabling supply-chain compromise or arbitrary command execution if the source is malicious or tampered with.

Missing User Warnings

Medium
Confidence
92% confidence
Finding
O README promove o uso de `agent_llm` diretamente no projeto sem qualquer aviso de que código-fonte, segredos ou outros dados sensíveis do repositório podem ser enviados ao modelo/infraestrutura do agente atual. Em um contexto de auditoria de segurança, essa omissão é relevante porque pode levar usuários a expor inadvertidamente conteúdo privado a um provedor local, remoto ou a uma integração do agente que eles não compreendem totalmente.

Missing User Warnings

Medium
Confidence
95% confidence
Finding
A documentação mostra configuração com OpenAI/Claude/outros provedores externos sem alertar que o conteúdo auditado pode ser enviado a terceiros. Isso cria risco de vazamento de código proprietário, segredos e dados regulados, especialmente porque o texto incentiva uso simples e direto sem qualquer ressalva de consentimento, retenção ou conformidade.

Missing User Warnings

Medium
Confidence
94% confidence
Finding
The README strongly promotes `agent_llm` and third-party providers for auditing project code, but it does not clearly warn users that source code, secrets, and other sensitive repository contents may be sent to an external model or through an agent-connected LLM. In a security-audit skill, that omission is materially risky because users may assume analysis is fully local or privacy-preserving when it may involve data exfiltration to outside services.

Missing User Warnings

Medium
Confidence
93% confidence
Finding
文档鼓励使用 `agent_llm`、OpenAI、Claude 等外部提供者对代码库进行审计,但未明确提醒用户:被审计的源代码、配置、依赖信息乃至潜在密钥片段可能会被发送到第三方模型服务。这会导致用户在不知情的情况下上传敏感代码或凭据,尤其在企业/私有仓库场景中风险更高。

Vague Triggers

Medium
Confidence
88% confidence
Finding
The manifest-style trigger examples include broad activation terms like pre_commit and pre_publish without any scoping conditions, exclusions, or user-confirmation requirements. In an agent ecosystem, ambiguous triggers can cause the skill to run automatically in more contexts than intended, including on sensitive repositories or during routine workflows, increasing the chance of unnecessary data access or unintended external transmission when the multi-model feature is used.

Vague Triggers

Medium
Confidence
91% confidence
Finding
The OpenCode integration example uses auto_invoke: pre_publish without defining what events qualify, what files are included, or whether the user must approve execution. This ambiguity is dangerous because the skill supports sending code to external AI providers, so an overly broad auto-invocation path could trigger scans and exfiltrate source code or secrets at publish time without sufficiently explicit consent boundaries.

Missing User Warnings

Medium
Confidence
92% confidence
Finding
This example encourages use of external AI providers (OpenAI/Claude fallback) to audit the current project but does not clearly warn users that repository contents may be transmitted to third-party services. In a security-audit tool, source code and configuration often contain sensitive material, so silent or implicit off-host transfer creates a real confidentiality risk.

Missing User Warnings

Medium
Confidence
95% confidence
Finding
The multi-model example uses an API-backed service and only checks for an API key, without warning that repository data may be shared with third-party model providers. Because this is framed as a security-review workflow, users may run it on sensitive codebases and unintentionally disclose proprietary or secret-bearing content.

Missing User Warnings

High
Confidence
96% confidence
Finding
The multi-model audit tool states that it uses Claude, GPT-4, and Gemini via OpenRouter, but it does not explicitly warn that repository contents may be transmitted to third-party external AI services. In a security-audit context, scanned projects can contain secrets, proprietary code, internal paths, and personal data, so omission of this disclosure can cause unintentional exfiltration of sensitive material.

Missing User Warnings

High
Confidence
97% confidence
Finding
The full workflow tool chains an automated scan with a multi-model audit but does not disclose that the second stage may transmit repository data to external AI providers. Because this tool is positioned as a recommended pre-publish workflow, users may assume it is a local security process and unknowingly expose sensitive source code, credentials, or private data during the audit.

Missing User Warnings

Medium
Confidence
94% confidence
Finding
The manifest promotes multi-model auditing with external providers and API-key usage but does not disclose that source code, secrets, or other project contents may be sent to third-party services during analysis. In a security-audit skill, this omission is especially risky because users may scan sensitive repositories under the assumption processing is local or agent-contained.

Missing User Warnings

Medium
Confidence
96% confidence
Finding
The recommended 'vibe_audit_enhanced' flow emphasizes agent-native integration and multiple providers, which can make remote processing seem seamless and safe, but it still lacks a clear warning that scanned repository contents may leave the local environment. Because this tool is positioned as the recommended production workflow, the missing disclosure increases the likelihood of accidental exfiltration of proprietary code or embedded secrets.

Missing User Warnings

Medium
Confidence
91% confidence
Finding
The provider sends prompts to an external OpenAI-compatible API without any explicit user consent, redaction, or egress guardrails. In an audit tool, prompts may contain source code, secrets, or proprietary material, so silent transmission to third parties creates a meaningful confidentiality risk.

Missing User Warnings

Medium
Confidence
91% confidence
Finding
This code transmits audit prompts to Anthropic's API without a disclosure or consent step. Because audit prompts are likely to include file contents and findings from user repositories, this can leak sensitive code or secrets to an external provider.

Missing User Warnings

Low
Confidence
67% confidence
Finding
Invoking npm audit over an arbitrary project without explicit warning can expose users to unexpected network activity and package-manager processing of untrusted repository state. In an agent/skill setting, hidden execution of ecosystem tooling against attacker-supplied codebases increases operational risk.

VirusTotal

VirusTotal findings are pending for this skill version.

View on VirusTotal

Static analysis

No suspicious patterns detected.