
Redact MCP
io.github.r3352由 GitHub 账号 r3352 发布v2.0.4更新于 Oct 24, 2026
Auto-detects and obfuscates PII (IPs, emails, API keys, names) so Claude never sees real client data
概览
在本地对文本、文件和 HTTP 响应中的个人隐私与密钥做混淆,让助手只看到虚假替换值。
- 可以做什么
- 通过 19 类正则模式和 AI 命名实体识别模型检测敏感数据,覆盖 IP、主机名、邮箱、API 密钥、令牌、私钥、连接字符串、卡号、地址以及人名、组织名和地名。每个唯一值都会获得确定性的虚假替换值,并保存在双向映射表中,因此混淆可以无损还原。共提供八个工具,包括混淆、还原、读取文件并混淆、映射的添加/删除/查看、审计日志查看,以及一个 HTTP 代理工具:先还原请求、发出真实调用,再对响应做混淆。
- 适用场景
- 适合助手需要分析敏感材料(例如渗透测试数据或客户记录),又不希望真实值暴露给模型或第三方时使用。也适用于最终报告在交付前需要还原为真实值的工作流。
- 运行要求
- 以 stdio 方式在本地运行,通常通过 npx 从 npm 包启动,需要 Node.js。命名实体识别模型(约 110MB)首次使用时下载并缓存;若不可用则退回到仅正则模式。可选环境变量:REDACT_DATA_DIR 指定映射与审计存储目录,REDACT_AUDIT_LOG 启用 JSONL 日志。未声明任何身份验证。
安装
在 SourceWeft 中
- 打开 控制台中的 Redact MCP,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
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
来源:README.md,提交 36b0fe8
工具
0版本历史
1- v2.0.4最新Sep 16, 2026
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