
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
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
在 SourceWeft 中
- 開啟 儀表板中的 Redact MCP,將其新增到工作區。
- 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
The server maintains a bidirectional mapping table. Every sensitive value gets a consistent, deterministic fake replacement that persists across the entire session:
Same real value always maps to the same fake value. obfuscate(text) -> deobfuscate(result) === text is guaranteed.
Install
Quick install via npx (recommended)
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:
As a Claude Code plugin (full features: hooks + skills)
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):
Environment Variables
What Gets Detected
Regex Patterns (19 types)
Zero configuration required. Detected automatically:
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:
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:
redact_proxy_request — The Key Tool
For pentest workflows, this is the critical tool. Claude calls it instead of curl/fetch:
- Claude provides URL/headers/body (may contain already-obfuscated values)
- Server deobfuscates the request (restores real hostnames/IPs/tokens)
- Makes the real HTTP request to the target
- Obfuscates the entire response (headers + body)
- 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 (
regexorner) - 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:
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)
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/WebFetchand the output contains known sensitive values (leak detection)
How the Pipeline Works
Detection
- Regex pass — 19 pattern types scanned synchronously via compiled RegExp
- NER pass —
Xenova/bert-base-NERruns in parallel (async), catches person/org/location entities - Merge — Results combined; regex matches win on overlapping spans (more precise for structured data)
- Deduplication — Overlapping NER results that cover the same span as a regex match are dropped
Mapping
- Each unique real value gets a deterministic fake via counter-based generation
- Fake values use safe ranges: TEST-NET-2 for IPv4, RFC 3849 for IPv6,
example.comfor domains,555prefix for phones, locally-administered range for MACs - Mappings persist to
${REDACT_DATA_DIR}/mappings.jsonwith debounced writes (500ms) - Longest-first replacement prevents partial match corruption (e.g.,
10.50.1.100before10.50.1.10)
Round-trip Guarantee
deobfuscate(obfuscate(text)) === text for all inputs. The bidirectional mapping table ensures lossless restoration.
Architecture
Runtime Flow
Development
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_logfor 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版本歷史
1- v2.0.4最新Sep 16, 2026
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