Redact MCP

io.github.r3352由 GitHub 帳號 r3352 發布v2.0.4更新於 Oct 24, 2026

Auto-detects and obfuscates PII (IPs, emails, API keys, names) so Claude never sees real client data

已驗證STDIO僅桌面開發者工具安全與監控

概覽

AI 產生的概覽

在本機對文字、檔案與 HTTP 回應中的個人隱私與金鑰進行混淆,讓助理只看得到假的替代值。

可以做什麼
以 19 類正規表達式模式與 AI 命名實體辨識模型偵測敏感資料,涵蓋 IP、主機名稱、電子郵件、API 金鑰、權杖、私密金鑰、連線字串、卡號、地址,以及人名、組織名稱與地名。每個唯一值都會取得確定性的假替代值,並保存在雙向對應表中,因此混淆可無損還原。共提供八個工具,包括混淆、還原、讀取檔案並混淆、對應的新增/移除/檢視、稽核記錄檢視,以及一個 HTTP 代理工具:先還原請求、發出真實呼叫,再對回應做混淆。
適用情境
適合助理需要分析敏感素材(例如滲透測試資料或客戶紀錄),又不希望真實值暴露給模型或第三方時使用。也適用於最終報告在交付前需要還原為真實值的工作流程。
執行需求
以 stdio 方式在本機執行,通常透過 npx 從 npm 套件啟動,需要 Node.js。命名實體辨識模型(約 110MB)首次使用時下載並快取;若無法使用則退回僅正規表達式模式。選用環境變數:REDACT_DATA_DIR 指定對應與稽核儲存目錄,REDACT_AUDIT_LOG 啟用 JSONL 記錄。未宣告任何身分驗證。
安裝前請注意
稽核記錄會寫入完整輸入與輸出文字以及每個偵測到的真實值,因此依設計就包含敏感資料,必須妥善保護;記錄寫入 REDACT_DATA_DIR。對應也會持久化到該目錄。代理工具會向目標發出真實的外部 HTTP 請求,外掛鉤子還會檢查原始工具輸出。使用前請確認資料與記錄的存放位置。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Redact MCP,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

Redact MCP — Automatic PII Obfuscation for Claude Code

An MCP server (and Claude Code plugin) that automatically detects and obfuscates sensitive data before Claude ever sees it. Uses regex pattern matching and AI-powered Named Entity Recognition (NER) to catch IPs, hostnames, emails, API keys, person names, organization names, locations, private keys, connection strings, and more.

Built for penetration testers who need Claude's analysis capabilities without exposing client data to a third party.

How It Works

Raw data with real PII ──► Regex + NER detection ──► Claude sees only fake values                                                              │Final report for client ◄── Real values restored ◄── /redact:export

The server maintains a bidirectional mapping table. Every sensitive value gets a consistent, deterministic fake replacement that persists across the entire session:

Real ValueObfuscated AsDetection
10.50.1.100198.51.100.1regex
api.clientcorp.comtarget-1.example.comregex
[email protected][email protected]regex
AKIA3EXAMPLE...[REDACTED_AWS_KEY_1]regex
James WilsonPerson_A Person_BNER
MicrosoftOrg_ANER
SeattleCity_ANER
postgres://admin:pw@host/db[REDACTED_CONN_STRING_1]regex
-----BEGIN RSA PRIVATE KEY...[REDACTED_PRIVATE_KEY_1]regex

Same real value always maps to the same fake value. obfuscate(text) -> deobfuscate(result) === text is guaranteed.

Install

Quick install via npx (recommended)

bash
claude mcp add @mattzam/redact-mcp -- npx @mattzam/redact-mcp

That's it. Claude Code will launch the server via npx on each session. The NER model (~110MB) downloads automatically on first use and is cached for subsequent runs.

To enable audit logging:

bash
claude mcp add @mattzam/redact-mcp -e REDACT_AUDIT_LOG=true -- npx @mattzam/redact-mcp

As a Claude Code plugin (full features: hooks + skills)

bash
git clone https://github.com/r3352/redact-mcp.git redactcd redact/servernpm installnpm run buildcd ../..
# Load as a plugin (includes hooks for leak detection + slash commands)claude --plugin-dir ./redact

The plugin mode adds hooks (automatic leak detection on raw tool output) and skills (/redact:status, /redact:add, /redact:export) on top of the MCP server.

Manual MCP configuration

Add to your Claude Code MCP config (~/.claude/mcp.json or project .mcp.json):

json
{  "mcpServers": {    "redact-server": {      "command": "npx",      "args": ["redact-mcp"],      "env": {        "REDACT_DATA_DIR": "/path/to/data/directory",        "REDACT_AUDIT_LOG": "true"      }    }  }}

Environment Variables

VariableDefaultDescription
REDACT_DATA_DIR./dataDirectory for mapping state and audit logs
REDACT_AUDIT_LOGfalseSet to true to enable JSONL audit logging

What Gets Detected

Regex Patterns (19 types)

Zero configuration required. Detected automatically:

CategoryTypes
NetworkIPv4 (private + public), IPv6, hostnames with valid TLDs, MAC addresses
IdentityEmails, person names (JSON context), phone numbers (US + international), SSNs
SecretsAWS access keys, JWTs, Bearer tokens, API keys (context-aware), generic secrets (password=, secret=), private keys (PEM format), connection strings (postgres/mysql/redis/mongodb/amqp/mssql URIs)
FinancialCredit card numbers (Luhn-validated)
PhysicalStreet addresses (123 Main Street, 456 Oak Ave, etc.)

NER Detection (AI-powered)

When @huggingface/transformers is installed (included by default), the server loads Xenova/bert-base-NER (~110MB ONNX model, downloaded on first use) to detect:

Entity TypeExampleObfuscated As
Person namesJames WilsonPerson_A Person_B
OrganizationsMicrosoft, Acme CorpOrg_A, Org_B
LocationsSeattle, New YorkCity_A, City_B

NER catches entities that regex misses — names and organizations outside of JSON context, arbitrary location names, etc. Regex results always take priority when both detect the same span (regex is more precise for structured patterns).

Graceful fallback: If the model fails to load or the package is missing, the server continues in regex-only mode with no errors.

Smart Passthrough

These values are never obfuscated:

  • Loopback/reserved: localhost, 127.0.0.1, ::1, 0.0.0.0
  • RFC documentation ranges: 192.0.2.x (TEST-NET-1), 198.51.100.x (TEST-NET-2), 203.0.113.x (TEST-NET-3), 2001:db8:: (IPv6 docs)
  • Example domains: example.com, example.org, example.net
  • Dev domains: github.com, npmjs.com, nodejs.org, googleapis.com, etc.
  • Security testing: burpcollaborator.net, oastify.com
  • Code patterns: console.log, process.env, package.json, webpack.config, jest.config, tailwind.config, and 30+ other common false positives
  • Broadcast MACs: FF:FF:FF:FF:FF:FF, 00:00:00:00:00:00

MCP Tools

The server registers 8 tools via MCP:

ToolDescription
redact_obfuscateAuto-detect and replace all PII in text (regex + NER)
redact_deobfuscateReverse all replacements back to real values
redact_proxy_requestHTTP proxy — deobfuscates request, makes real call, obfuscates response
redact_read_fileRead a file and return obfuscated content
redact_add_mappingManually add a real-to-fake mapping
redact_remove_mappingRemove a mapping (fix false positives)
redact_show_mappingsShow all current mappings grouped by type
redact_audit_logView recent audit log entries (requires REDACT_AUDIT_LOG=true)

redact_proxy_request — The Key Tool

For pentest workflows, this is the critical tool. Claude calls it instead of curl/fetch:

  1. Claude provides URL/headers/body (may contain already-obfuscated values)
  2. Server deobfuscates the request (restores real hostnames/IPs/tokens)
  3. Makes the real HTTP request to the target
  4. Obfuscates the entire response (headers + body)
  5. Returns sanitized response to Claude

Claude never sees the real response data. All deobfuscation and obfuscation steps are audit-logged.

redact_audit_log — Compliance Audit Trail

When REDACT_AUDIT_LOG=true, every obfuscation and deobfuscation operation is logged to ${REDACT_DATA_DIR}/audit.jsonl. Each entry records:

  • Timestamp and operation type (obfuscate, deobfuscate, proxy_request, file_read)
  • Full input text (raw data before transformation)
  • All detections with type, real value, fake replacement, and source (regex or ner)
  • Full output text (transformed result)

This provides a complete audit trail: what went in, what was modified, and what came out.

View entries via the redact_audit_log tool or read audit.jsonl directly:

bash
# Last 5 entries, pretty-printedtail -5 data/audit.jsonl | python3 -m json.tool

Security note: The audit log contains real sensitive data by design (that's its purpose — proving what was redacted). Protect it accordingly.

Skills (Slash Commands)

CommandDescription
/redact:statusShow current redaction mappings grouped by type
/redact:add <real> <fake>Manually add a mapping (e.g., /redact:add clientcorp.com target.example.com)
/redact:export <file> [output]Deobfuscate a file for client delivery (defaults to ~/Desktop/)

Hooks

The plugin uses two hooks (automatic, no user interaction):

  • SessionStart — Injects instructions telling Claude to route all data through redact tools
  • PostToolUse — Warns if Claude uses raw Bash/Read/Grep/WebFetch and the output contains known sensitive values (leak detection)

How the Pipeline Works

Detection

  1. Regex pass — 19 pattern types scanned synchronously via compiled RegExp
  2. NER pass — Xenova/bert-base-NER runs in parallel (async), catches person/org/location entities
  3. Merge — Results combined; regex matches win on overlapping spans (more precise for structured data)
  4. Deduplication — Overlapping NER results that cover the same span as a regex match are dropped

Mapping

  1. Each unique real value gets a deterministic fake via counter-based generation
  2. Fake values use safe ranges: TEST-NET-2 for IPv4, RFC 3849 for IPv6, example.com for domains, 555 prefix for phones, locally-administered range for MACs
  3. Mappings persist to ${REDACT_DATA_DIR}/mappings.json with debounced writes (500ms)
  4. Longest-first replacement prevents partial match corruption (e.g., 10.50.1.100 before 10.50.1.10)

Round-trip Guarantee

deobfuscate(obfuscate(text)) === text for all inputs. The bidirectional mapping table ensures lossless restoration.

Architecture

redact/├── .claude-plugin/plugin.json     # Plugin manifest├── .mcp.json                      # MCP server config (stdio transport)├── hooks/│   ├── hooks.json                 # Hook definitions│   └── scripts/│       ├── session-start.sh       # Injects redaction context on session start│       └── post-tool-scan.py      # Leak detection on raw tool output├── skills/│   ├── status/SKILL.md            # /redact:status│   ├── add/SKILL.md               # /redact:add│   └── export/SKILL.md            # /redact:export└── server/    ├── package.json               # v2.0.0, deps: @modelcontextprotocol/sdk, @huggingface/transformers    ├── tsconfig.json              # ES2022, Node16 modules, strict    └── src/        ├── index.ts               # MCP server — 8 tools, server instructions        ├── mapping-engine.ts      # Bidirectional mapping, async obfuscate/deobfuscate, audit integration        ├── pattern-detector.ts    # 19 regex patterns + async NER merge        ├── fake-generator.ts      # Deterministic counter-based fake value generation        ├── ner-detector.ts        # Lazy-loaded HuggingFace NER with graceful fallback        ├── audit-logger.ts        # JSONL append logger, serialized write queue        └── persistence.ts         # JSON state file with debounced writes

Runtime Flow

Claude Code ──stdio──► MCP Server (index.ts)                            │                     CallToolRequest                            │                    ┌───────┴───────┐                    │ MappingEngine │                    └───────┬───────┘                            │              ┌─────────────┼─────────────┐              │             │             │        detectPatterns   NER detect   AuditLogger        (regex, sync)   (async)      (JSONL, async)              │             │             │              └─────────────┼─────────────┘                            │                     merge + dedupe                            │                     apply mappings                     (longest-first)                            │                     return to Claude

Development

bash
cd server
# Install dependencies (~110MB for NER model on first run)npm install
# Build TypeScriptnpm run build
# Test MCP server starts and lists toolsecho '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}' | node dist/index.js
# Quick obfuscation testnode -e 'const { MappingEngine } = await import("./dist/mapping-engine.js");const engine = new MappingEngine("/tmp/redact-dev");await engine.init();console.log(await engine.obfuscate("Email [email protected] from 10.0.0.1"));'

Changelog

v2.0.0

  • NER detection — AI-powered entity recognition via @huggingface/transformers + Xenova/bert-base-NER. Catches person names, organizations, and locations outside structured JSON context.
  • Audit logging — Opt-in JSONL audit trail (REDACT_AUDIT_LOG=true) records full input/output text, all detections with sources, and timestamps for every obfuscation and deobfuscation operation.
  • 7 new pattern types — organization, location, private_key, connection_string, generic_secret, mac_address, street_address
  • 8th tool — redact_audit_log for viewing audit entries
  • Improved phone detection — International format support (+CC-XXXX-XXXX)
  • Expanded false positive list — webpack.config, jest.config, tailwind.config, and 9 other config file patterns

v1.0.0

  • Initial release with regex-only detection (12 pattern types), 7 MCP tools, bidirectional mapping, persistence, hooks, and skills.

License

MIT

來源:README.md,提交 36b0fe8

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v2.0.4最新Sep 16, 2026

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