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No API keys on the free tier.\n\n[![npm version](https://badge.fury.io/js/llmverify.svg)](https://www.npmjs.com/package/llmverify)\n[![CI](https://github.com/subodhkc/llmverify-npm/actions/workflows/ci.yml/badge.svg)](https://github.com/subodhkc/llmverify-npm/actions/workflows/ci.yml)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n**Last Updated:** August 21, 2026\n**Version:** 1.6.1\n**Node:** >= 18.0.0\n**License:** MIT\n\n---\n\n## Links\n\n- **Product page:** [haiec.com/llmverify](https://www.haiec.com/llmverify)\n- **npm:** [npmjs.com/package/llmverify](https://www.npmjs.com/package/llmverify)\n- **GitHub:** [github.com/subodhkc/llmverify-npm](https://github.com/subodhkc/llmverify-npm)\n- **Python (placeholder):** [pypi.org/project/llmverify](https://pypi.org/project/llmverify/) — namespace reservation, not a port of this package\n- **Author:** [Subodh Kc](https://subodhkc.com)\n\n---\n\n## The problem\n\nYou build with GPT-4, Claude, Gemini, or any LLM. The model:\n\n- **Hallucinates** facts and citations that do not exist.\n- **Leaks PII** — emails, phone numbers, SSNs, API keys in responses.\n- **Follows prompt injections** — users trick it into ignoring your instructions.\n- **Returns broken JSON** that crashes your parser.\n- **Drifts** in quality over time, and nobody notices until a user complains.\n\nYou need a guardrail between the model and your users. That is llmverify.\n\n---\n\n## Install\n\n```bash\nnpm install llmverify\n```\n\nEverything 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).\n\n---\n\n## What you get\n\n| Function | One-liner | What it does |\n|----------|-----------|--------------|\n| `verify(content)` | `await verify(aiResponse)` | Runs hallucination, consistency, safety, and CSM6 checks; returns a risk level and findings |\n| `isInputSafe(input)` | `isInputSafe(userMessage)` | Blocks prompt injection, jailbreaks, and malicious input before it reaches the model |\n| `redactPII(text)` | `redactPII(aiResponse)` | Masks emails, phones, SSNs, credit cards, and API keys |\n| `containsPII(text)` | `containsPII(text)` | Returns true if PII is present |\n| `detectAndRepairJson(...)` | `detectAndRepairJson(prompt, response)` | Detects and repairs broken JSON output |\n| `monitorLLM(client)` | `monitorLLM(openaiClient)` | Wraps any LLM client; tracks latency, token drift, and behavioral changes |\n| `sentinel.quick(...)` | `await sentinel.quick(client, model)` | Runs regression tests against your model before users see changes |\n| `classify(...)` | `classify(prompt, response)` | Intent detection, hallucination signals, and instruction compliance |\n| `auditLog(event)` | `auditLog({ ... })` | Appends a local, hash-only audit entry for SOC 2 / HIPAA / GDPR evidence |\n| `run`, `prodVerify`, `ciVerify` | `await prodVerify(content)` | Preset pipelines for dev, prod, strict, fast, and CI use |\n\n---\n\n## Quick start (30 seconds)\n\n```javascript\nconst { verify, isInputSafe, redactPII } = require('llmverify');\n\n// 1. Block prompt injection before it reaches the model.\nif (!isInputSafe(userMessage)) {\n  return { error: 'Invalid input detected' };\n}\n\n// 2. Verify the model's output.\nconst aiResponse = await yourLLM.generate(userMessage);\nconst result = await verify(aiResponse);\n\nif (result.risk.level === 'critical') {\n  return { error: 'Response failed safety check' };\n}\n\n// 3. Strip PII before the response reaches a user or a log.\nconst { redacted } = redactPII(aiResponse);\nconsole.log(redacted);\n```\n\nThree lines of safety between your LLM and your users. No config file required. No API key required.\n\n---\n\n## How it works\n\nllmverify 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.\n\n**Framework alignment (baseline mapping only — not certification):**\n- OWASP LLM Top 10\n- NIST AI RMF\n- EU AI Act\n- ISO 42001\n- CSM6 (HAIEC's 38-rule control set)\n\n---\n\n## CLI\n\n```bash\n# Verify a string from the terminal.\nnpx llmverify verify \"The capital of France is London.\"\n\n# Start a local HTTP API for IDE / tool integration (localhost only by default).\nnpx llmverify-serve --port=9009\n\n# Expose to the network only on a trusted network. There is no auth on the API.\nnpx llmverify-serve --host=0.0.0.0 --port=9009\n```\n\nThe 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).\n\n---\n\n## Limitations\n\nllmverify is a triage tool, not a truth oracle. Be honest with yourself about what it can and cannot do:\n\n- **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.\n- **It does not replace human review.** Use it to triage, not to approve.\n- **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.\n- **Prompt-injection detection is pattern-based.** Novel or obfuscated injections can evade it.\n- **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.\n\nIf a claim matters, verify it yourself. llmverify narrows the risk surface; it does not eliminate it.\n\n---\n\n## Documentation\n\n- [Quick Start](QUICK-START.md)\n- [Integration Guide](docs/INTEGRATION-GUIDE.md)\n- [API Reference](docs/API-REFERENCE.md)\n- [Risk Levels](docs/RISK-LEVELS.md)\n- [CLI Reference](docs/CLI-REFERENCE.md)\n- [Limitations](docs/LIMITATIONS.md)\n\n---\n\n## Part of HAIEC\n\nllmverify is part of the [HAIEC](https://www.haiec.com) AI governance platform. Use it alongside the [AI Security Scanner](https://www.haiec.com/dashboard/ai-security), the [CI/CD pipeline integration](https://www.haiec.com/dashboard/ai-security/ci-setup), and [Runtime Injection Testing](https://www.haiec.com/dashboard/runtime-security).\n\n---\n\n## Support\n\n- **Issues:** [GitHub Issues](https://github.com/subodhkc/llmverify-npm/issues)\n- **Docs:** [Full documentation](docs/)\n\n---\n\n## License\n\nMIT — see [LICENSE](LICENSE).\n\n---\n\n**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.\n","readmeFilename":"README.md"}