LLMVerify

io.github.subodhkcv1.8.0更新于 Oct 9, 2026

Local-first MCP server for LLM output verification — risk signals, injection and PII checks.

已验证STDIO仅桌面AI & ML Security & Monitoring

概览

AI 生成的概览

本地 MCP 服务器，用于检查 LLM 输出的幻觉风险、提示注入和 PII，并可对敏感数据做脱敏。

功能: LLMVerify 通过 stdio 向兼容 MCP 的智能体暴露一个本地验证引擎。它提供六个工具：verify_llm_content、assess_hallucination_risk、check_prompt_injection、check_pii、redact_pii 和 get_llmverify_capabilities。检查基于确定性的模式匹配，覆盖幻觉与一致性信号、注入与越狱模式，以及邮箱、电话号码、SSN、信用卡和常见 API 密钥等标准 PII 格式。结果会附带明确的 limitations 或 notChecked 列表。
适用场景: 当助手需要处理不可信的用户输入或模型输出，并希望在内容到达用户或日志之前加一道本地防护时使用。适合对注入尝试做初步筛查、PII 检测与脱敏，不适合做事实核验或审批决策。
运行要求: 通过 npx 从 npm 包 llmverify 在本地运行；MCP 命令需要 Node.js 20 或更高版本。无需账号或 API 密钥，免费版不发起网络请求。可选环境变量用于配置审计、基线、日志和状态目录，以及输入输出大小上限和单工具超时。

安装前请注意

检测基于模式匹配：幻觉信号无法证明某个说法为假，经过混淆或编码的 PII 以及新型注入可能被漏掉。脱敏和验证结果不应替代人工审核。可选密钥 LLMVERIFY_AUDIT_HASH_KEY 用于在审计记录中启用带密钥的内容哈希；设置 LLMVERIFY_AUDIT_NO_CONTENT_HASH 可禁用内容哈希。另有本地 HTTP 服务器没有身份验证，将其暴露到 localhost 之外存在风险。

安装

在 SourceWeft 中

打开控制台中的 LLMVerify，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

llmverify

You shipped an AI feature. Your LLM hallucinated a citation, leaked a customer's email, and followed a prompt-injection buried in user input — on the same day. llmverify is the safety layer that sits between your LLM and your users.

Local-first verification, PII redaction, prompt-injection defense, and runtime monitoring for any LLM. One npm install. Zero telemetry. No API keys on the free tier.

[npm version] [CI] [License: MIT]

Last Updated: August 21, 2026 Version: 1.7.0 Node: >= 18.0.0 License: MIT

The problem

You build with GPT-4, Claude, Gemini, or any LLM. The model:

Hallucinates facts and citations that do not exist.
Leaks PII — emails, phone numbers, SSNs, API keys in responses.
Follows prompt injections — users trick it into ignoring your instructions.
Returns broken JSON that crashes your parser.
Drifts in quality over time, and nobody notices until a user complains.

You need a guardrail between the model and your users. That is llmverify.

Install

Everything runs locally. The free tier makes zero network requests and needs no API key. Free tier limit: 500 verification calls per day (tracked locally, never sent anywhere).

What you get

Function	One-liner	What it does
`verify(content)`	`await verify(aiResponse)`	Runs hallucination, consistency, safety, and CSM6 checks; returns a risk level and findings
`isInputSafe(input)`	`isInputSafe(userMessage)`	Blocks prompt injection, jailbreaks, and malicious input before it reaches the model
`redactPII(text)`	`redactPII(aiResponse)`	Masks emails, phones, SSNs, credit cards, and API keys
`containsPII(text)`	`containsPII(text)`	Returns true if PII is present
`detectAndRepairJson(...)`	`detectAndRepairJson(prompt, response)`	Detects and repairs broken JSON output
`monitorLLM(client)`	`monitorLLM(openaiClient)`	Wraps any LLM client; tracks latency, token drift, and behavioral changes
`sentinel.quick(...)`	`await sentinel.quick(client, model)`	Runs regression tests against your model before users see changes
`classify(...)`	`classify(prompt, response)`	Intent detection, hallucination signals, and instruction compliance
`auditLog(event)`	`auditLog({ ... })`	Appends a local, hash-only audit entry for SOC 2 / HIPAA / GDPR evidence
`run`, `prodVerify`, `ciVerify`	`await prodVerify(content)`	Preset pipelines for dev, prod, strict, fast, and CI use

Quick start (30 seconds)

javascript

const { verify, isInputSafe, redactPII } = require('llmverify');
// 1. Block prompt injection before it reaches the model.if (!isInputSafe(userMessage)) {  return { error: 'Invalid input detected' };}
// 2. Verify the model's output.const aiResponse = await yourLLM.generate(userMessage);const result = await verify(aiResponse);
if (result.risk.level === 'critical') {  return { error: 'Response failed safety check' };}
// 3. Strip PII before the response reaches a user or a log.const { redacted } = redactPII(aiResponse);console.log(redacted);

const { verify, isInputSafe, redactPII } = require('llmverify');
// 1. Block prompt injection before it reaches the model.if (!isInputSafe(userMessage)) {  return { error: 'Invalid input detected' };}
// 2. Verify the model's output.const aiResponse = await yourLLM.generate(userMessage);const result = await verify(aiResponse);
if (result.risk.level === 'critical') {  return { error: 'Response failed safety check' };}
// 3. Strip PII before the response reaches a user or a log.const { redacted } = redactPII(aiResponse);console.log(redacted);

Three lines of safety between your LLM and your users. No config file required. No API key required.

How it works

llmverify runs deterministic, pattern-based engines locally — no model calls, no network on the free tier. Same input plus same rules equals same result. Every result carries an explicit limitations array stating what was and was not checked, so you never mistake a clean score for a guarantee.

Framework alignment (baseline mapping only — not certification):

OWASP LLM Top 10
NIST AI RMF
EU AI Act
ISO 42001
CSM6 (HAIEC's 38-rule control set)

CLI

bash

# Verify a string from the terminal.npx llmverify verify "The capital of France is London."
# Start a local HTTP API for IDE / tool integration (localhost only by default).npx llmverify-serve --port=9009
# Expose to the network only on a trusted network. There is no auth on the API.npx llmverify-serve --host=0.0.0.0 --port=9009

# Verify a string from the terminal.npx llmverify verify "The capital of France is London."
# Start a local HTTP API for IDE / tool integration (localhost only by default).npx llmverify-serve --port=9009
# Expose to the network only on a trusted network. There is no auth on the API.npx llmverify-serve --host=0.0.0.0 --port=9009

The server binds to 127.0.0.1 by default, restricts CORS to localhost origins, and rate-limits clients (100 requests / 60s). It requires express (an optional dependency that installs by default).

MCP server

llmverify ships a built-in Model Context Protocol server — the same engine, exposed to MCP-compatible agents and IDEs over stdio:

jsonc

// MCP client config{  "mcpServers": {    "llmverify": {      "command": "npx",      "args": ["-y", "llmverify", "mcp"]    }  }}

// MCP client config{  "mcpServers": {    "llmverify": {      "command": "npx",      "args": ["-y", "llmverify", "mcp"]    }  }}

Six tools: verify_llm_content, assess_hallucination_risk, check_prompt_injection, check_pii, redact_pii, get_llmverify_capabilities. Stdio-only, zero outbound network, bounded inputs/outputs, PII-filtered responses, honest notChecked/audit semantics.

Requires Node.js ≥ 20 (the MCP SDK's floor; the rest of the package supports ≥ 18). The MCP SDK and zod are regular dependencies — the mcp command lazy-loads them so other commands pay no startup cost.

See docs/MCP.md for the full tool reference and security model.

Limitations

llmverify is a triage tool, not a truth oracle. Be honest with yourself about what it can and cannot do:

It cannot definitively prove hallucinations. Hallucination signals are pattern-based. "The capital of France is London" scores low because the text looks internally consistent. Ground-truth verification requires a source document you provide.
It does not replace human review. Use it to triage, not to approve.
PII detection is regex-based. It catches standard formats (emails, US phones, SSNs, credit cards, common API keys). It misses obfuscated, image-embedded, or encoded PII. Accuracy is roughly 90% for standard formats, lower for variations.
Prompt-injection detection is pattern-based. Novel or obfuscated injections can evade it.
Free tier is 100% local. ML-enhanced features require a paid tier and an explicit API key; the free tier never makes network requests and never sends data anywhere.

If a claim matters, verify it yourself. llmverify narrows the risk surface; it does not eliminate it.

Documentation

Part of HAIEC

llmverify is part of the HAIEC (Human AI Evidence Company) AI governance platform. Use it alongside the AI Security Scanner, the CI/CD pipeline integration, and Runtime Injection Testing.

Support

Issues: GitHub Issues
Docs: Full documentation

License

MIT — see LICENSE.

Recommendation (not legal advice): Run verify() on every model output that reaches a user, and isInputSafe() on every user input that reaches a model. Treat the risk level as a triage signal, not an approval.

来源：README.md，提交 2d130f6

工具

工具元数据尚未被收录。

版本历史

v1.8.0最新Oct 9, 2026