Valca

Name: Valca
Author: PranLabs

作者：PranLabs版本 0.6.1i★ –i在你的电脑上运行

注册名称io.github.pranlabs/valca

安装

概览

作者提供的说明（英文原文）

Scans code, IaC and AI-agent config for security problems. Read-only — nothing can be edited.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 其他

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
注册中心没有列出需要填写的密钥。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "valca": {
      "command": "uvx",
      "args": [
        "valca"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add valca -- uvx valca

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "valca": {
      "command": "uvx",
      "args": [
        "valca"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "valca": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "valca"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 PranLabs 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

星标数多于此分类中其他 32545 个服务器的 0%
发布到注册中心的时间比此分类中其他 32545 个服务器的 57% 更新
基础检查结果等于或优于此分类中其他 32545 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Valca 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.6.1）于31天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Spec Driven Development, Remote Desktop Commander, Python MCP

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者：formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新

安装

Remote Desktop Commander

作者：desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新

安装

Python MCP

作者：Files-com

Local-only Python MCP server for the Files.com API.

在你的电脑上运行今天更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Valca

AI coding security co-pilot — blocks insecure code at the moment of generation.

Formerly published as `vigilsec`. The package is now valca. Both the valca and vigil

commands work, so existing hooks and scripts keep running unchanged.

Valca intercepts every file an AI coding assistant writes and blocks it if CRITICAL or HIGH security findings are detected — before the file hits disk. It's the only tool that operates at generation time rather than post-commit.

AI writes file → valca scan → exit 2 → Claude Code blocks the write

The Problem

AI coding assistants reproduce the most common patterns in their training data. The most common patterns are insecure defaults.

The clearest example: every existing IaC scanner (Checkov, Trivy, Snyk, Semgrep) misses the docker-compose port binding that exposes your database to the internet:

ports:

"5432:5432" # ← binds to 0.0.0.0, bypasses UFW, reachable from anywhere

The correct form is "127.0.0.1:5432:5432". Valca catches it. Nothing else does.

Install

pip install valca

Wire the Claude Code hook (one time):

valca init --global     # `valca init --global` also works

That's it. Every file Claude Code writes is now scanned before it saves. Reload Claude Code to activate.

Network access

Valca's core scanning is fully offline — the package has zero runtime dependencies and the engine never sends your code, file paths, or findings anywhere.

Three rules do reach the network, because checking whether a dependency is vulnerable or fabricated is impossible offline. When a manifest (requirements.txt, package.json, lockfiles) is scanned, these rules send package names and version strings only to:

Host	Used by	What is sent
`api.osv.dev`	VGL-PKG001 (known CVE in a pinned version)	package name + version
`pypi.org`	VGL-PKG002/003/004 (hallucinated, stale, or suspicious package)	package name
`registry.npmjs.org`	VGL-PKG002/003/004	package name

Your source code, file contents, file paths, and scan results are never transmitted. If your dependency inventory is itself sensitive, turn these rules off in .valcarc and Valca runs completely offline:

disabled_rules = ["VGL-PKG001", "VGL-PKG002", "VGL-PKG003", "VGL-PKG004"]

Usage

# Scan a single file

valca scan docker-compose.yml

Scan a directory

valca scan ./my-project/

JSON output (for CI / dashboards)

valca scan ./my-project/ --format json

SARIF output (for GitHub Advanced Security)

valca scan ./my-project/ --format sarif > results.sarif

Only report HIGH and above

valca scan ./my-project/ --severity HIGH

Open feedback & waitlist form

valca feedback

Review what has been caught over time. Both commands read the local scan history — nothing leaves your machine.

# Findings log — what was caught, where, and when

valca log # 20 most recent findings valca log --severity CRITICAL # only criticals valca log --project api --since 2026-09-01 valca log --limit 100 --format json # for dashboards

Aggregate stats — rule frequency, severity mix, precision per rule

valca stats valca stats --format json

valca stats reports how often each rule fires and how often you suppressed it, so rules with poor precision in *your* codebase are visible rather than guessed at.

Exit codes:

Code	Meaning
`0`	No findings — write proceeds
`1`	Advisory findings only (MEDIUM / LOW / INFO)
`2`	CRITICAL or HIGH found — Claude Code blocks the write

See It in Action

Blocking a vulnerable GitHub Actions workflow at write time:

In April 2026, researchers found that all three major AI coding agents (Claude Code, Gemini CLI, Copilot) could be hijacked to exfiltrate ANTHROPIC_API_KEY and GITHUB_TOKEN via a hidden HTML comment in a GitHub issue. CVSS 9.4. No special access required.

Valca catches the vulnerable workflow (issues: trigger + AI agent + API key in env) before it reaches git — the only tool that does.

→ Full writeup: The Attack That Steals Your API Keys Through a GitHub Issue Comment

Rules

116 rules across 27 categories. All built-in, stdlib-only, zero runtime dependencies.

This catalogue is generated from the rule registry — it cannot drift from the shipped engine.

Secrets & Credential Exposure (14 rules)

Rule	Severity	What it catches
VGL-I001	CRITICAL	eval() or exec() — code injection risk
VGL-I002	HIGH	subprocess with shell=True
VGL-I003	HIGH	os.system() call
VGL-S001	CRITICAL	AWS access key hardcoded
VGL-S002	CRITICAL	Hardcoded password
VGL-S003	CRITICAL	Hardcoded API key
VGL-S004	CRITICAL	Hardcoded bearer token
VGL-S005	CRITICAL	Hardcoded JWT secret
VGL-S006	CRITICAL	PEM private key in source
VGL-S007	CRITICAL	Database URL with embedded credentials
VGL-S008	CRITICAL	Stripe live secret key
VGL-S009	CRITICAL	Slack token hardcoded
VGL-S010	CRITICAL	Provider API key hardcoded (OpenAI / GitHub / GitLab / Google)
VGL-S012	HIGH	Insecure placeholder default for security-critical config

GitHub Actions — AI Agent Surface (13 rules)

Rule	Severity	What it catches
VGL-GHA001	CRITICAL	Pwn Request — pull_request_target with attacker-controlled checkout ref
VGL-GHA002	CRITICAL	Script injection — user-controlled context expression in run: step
VGL-GHA004	HIGH	Secret directly interpolated in run: step (visible in process table)
VGL-GHA005	MEDIUM	Workflow missing explicit permissions block (implicit GITHUB_TOKEN scope)
VGL-GHA006	HIGH	Cache usage in pull_request workflow (cache poisoning attack vector)
VGL-GHA007	HIGH	Self-hosted runner with pull_request trigger (persistent runner risk)
VGL-GHA008	HIGH	workflow_run trigger without ref/repo validation
VGL-GHA009	CRITICAL	AI agent wired to untrusted-input trigger (issues/pull_request_target) with API key in env
VGL-GHA010	HIGH	AI agent on pull_request_target without fork origin guard
VGL-GHA011	CRITICAL	Untrusted GitHub event data interpolated into AI agent prompt
VGL-GHA012	HIGH	AI agent on untrusted trigger with no --allowedTools restriction
VGL-GHA013	MEDIUM	AI agent on untrusted trigger with no --max-turns limit
VGL-GHA014	HIGH	contents: write permission with AI agent on untrusted trigger

AI Agent — Prompt Injection (9 rules)

Rule	Severity	What it catches
VGL-PI001	CRITICAL	User input interpolated into the LLM system prompt
VGL-PI002	HIGH	Raw HTTP request body used as LLM message content
VGL-PI003	HIGH	String formatting used to build prompts with user data
VGL-PI004	MEDIUM	Tool output appended to the conversation without sanitization
VGL-PI005	CRITICAL	HTTP request data flows into an LLM prompt unsanitised
VGL-PI006	CRITICAL	LLM response flows into subprocess, exec or eval
VGL-PI007	HIGH	Webhook payload flows directly into an AI agent
VGL-PI008	HIGH	Database content interpolated into a prompt — second-order injection
VGL-PI009	HIGH	User input written into a vector store — memory poisoning

Dockerfile Hardening (8 rules)

Rule	Severity	What it catches
VGL-DF001	HIGH	Dockerfile runs as root — no USER directive
VGL-DF002	MEDIUM	Dockerfile uses unpinned :latest base image
VGL-DF003	HIGH	Dockerfile bakes secret into ENV or ARG layer
VGL-DF004	HIGH	curl	bash or wget	sh pipe in Dockerfile RUN instruction
VGL-DF005	HIGH	TLS verification disabled in Dockerfile RUN fetch
VGL-DF006	MEDIUM	ADD used for local files — use COPY instead
VGL-DF007	HIGH	COPY . without .dockerignore — risks leaking .git, .env, credentials
VGL-DF008	MEDIUM	World-writable permissions set in Dockerfile (chmod 777)

Docker Compose (7 rules)

Rule	Severity	What it catches
VGL-D001	CRITICAL	Docker public port binding — bypasses UFW, exposes service to internet
VGL-D002	HIGH	docker-compose environment block contains hardcoded secret
VGL-D003	CRITICAL	Docker container runs in privileged mode
VGL-D004	HIGH	Docker container uses host network mode
VGL-D005	CRITICAL	Docker socket mounted into container — full container escape vector
VGL-D006	HIGH	Sensitive host path mounted into container
VGL-D007	HIGH	AWS credentials directory (~/.aws) mounted into container — exposes all profiles

Terraform (7 rules)

Rule	Severity	What it catches
VGL-TF001	CRITICAL	Terraform hardcoded secret value
VGL-TF002	HIGH	Terraform resource with public access enabled
VGL-TF003	HIGH	Terraform encryption explicitly disabled
VGL-TF004	CRITICAL	IMDSv1 enabled on EC2 instance — vulnerable to SSRF metadata theft
VGL-TF005	HIGH	Terraform S3 state backend stored without encryption
VGL-TF006	MEDIUM	Deletion protection disabled on managed resource
VGL-TF007	MEDIUM	Audit logging disabled on Terraform-managed resource

Deserialization & Path Traversal (5 rules)

Rule	Severity	What it catches
VGL-DESER001	CRITICAL	Insecure deserialization — pickle.loads / pickle.load
VGL-DESER002	HIGH	Insecure YAML deserialization — yaml.load() without SafeLoader
VGL-DESER003	HIGH	Insecure deserialization — marshal.loads with untrusted data
VGL-PATH001	HIGH	Path traversal — user input passed to file open or path join without validation
VGL-SSTI001	CRITICAL	Server-Side Template Injection — user input rendered as Jinja2 template

MCP Server Security (5 rules)

Rule	Severity	What it catches
VGL-MCP001	CRITICAL	Prompt injection embedded in MCP tool description
VGL-MCP002	HIGH	MCP tool description built from user-controlled data
VGL-MCP003	HIGH	Shell execution in an MCP tool handler without a sandbox
VGL-MCP004	HIGH	MCP server config uses an unpinned package or plaintext HTTP endpoint
VGL-MCP005	HIGH	MCP fetch tool takes an agent-controlled URL with no allowlist

Web Application Security (5 rules)

Rule	Severity	What it catches
VGL-CORS001	HIGH	CORS wildcard — allow_origins=['*']
VGL-SQL001	CRITICAL	SQL injection — query built with f-string or string concatenation
VGL-SQL002	CRITICAL	SQL injection — ORM raw query with f-string
VGL-SSL001	HIGH	SSL verification disabled
VGL-SSRF001	CRITICAL	SSRF — HTTP call with user-controlled URL

AI Agent — Excessive Agency (4 rules)

Rule	Severity	What it catches
VGL-A001	CRITICAL	LLM output piped into shell execution
VGL-A002	HIGH	Hardcoded auto-approval disables human-in-the-loop
VGL-A003	HIGH	Unbounded agent loop with no iteration limit
VGL-A004	HIGH	LLM response written to disk without validation

Authentication & Session (4 rules)

Rule	Severity	What it catches

只显示了开头部分；其余内容请在仓库中阅读

Valca

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“其他”中的其他服务器比较i

常见问题

替代选择

Spec Driven Development

Remote Desktop Commander

Python MCP

作者的 README

Valca

The Problem

Install

Network access

Usage

Scan a directory

JSON output (for CI / dashboards)

SARIF output (for GitHub Advanced Security)

Only report HIGH and above

Open feedback & waitlist form

Aggregate stats — rule frequency, severity mix, precision per rule

See It in Action

Rules

Secrets & Credential Exposure (14 rules)

GitHub Actions — AI Agent Surface (13 rules)

AI Agent — Prompt Injection (9 rules)

Dockerfile Hardening (8 rules)

Docker Compose (7 rules)

Terraform (7 rules)

Deserialization & Path Traversal (5 rules)

MCP Server Security (5 rules)

Web Application Security (5 rules)

AI Agent — Excessive Agency (4 rules)

Authentication & Session (4 rules)