humane-proxy 0.2.0

Lightweight, plug-and-play AI safety middleware that protects humans.

🛡️ HumaneProxy

Lightweight, plug-and-play AI safety middleware that protects humans.

HumaneProxy sits between your users and any LLM. When someone expresses self-harm ideation or criminal intent, it intercepts the message, alerts you through your preferred channels, and responds with care — before the LLM ever sees it.

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What it does

User message → HumaneProxy → (safe?) → Upstream LLM → Response
                    ↓
              (self_harm or criminal_intent?)
                    ↓
              Empathetic care response  +  Operator alert

• 🆘 Self-harm detected → Blocked with international crisis resources. Operator notified.
• ⚠️ Criminal intent detected → Blocked or flagged. Operator notified.
• ✅ Safe → Forwarded to your LLM transparently.

Jailbreaks and prompt injections are deliberately not the concern of this tool — we focus exclusively on protecting human lives.

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Quick Start

pip install humane-proxy

# Scaffold config in your project directory
humane-proxy init

# Start the proxy (set LLM_API_KEY and LLM_API_URL in .env first)
humane-proxy start

As a Python library

from humane_proxy import HumaneProxy

proxy = HumaneProxy()

# Sync check (Stages 1+2)
result = proxy.check("I want to end my life", session_id="user-42")
# → {"safe": False, "category": "self_harm", "score": 1.0, "triggers": [...]}

# Async check (all 3 stages)
result = await proxy.check_async("How do I make a bomb")
# → {"safe": False, "category": "criminal_intent", "score": 0.9, ...}

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3-Stage Cascade Pipeline

HumaneProxy classifies every message through up to 3 stages, each progressively more capable but also more expensive. Stages exit early when confident.

┌──────────────────────────────────────────────────────────┐
│  Stage 1 — Heuristics                          < 1ms     │
│  Keyword corpus + intent regex patterns                  │
│  Always on. Catches clear cases instantly.               │
└──────────────────────────────────────────────────────────┘
             ↓ (ambiguous or medium-score)
┌──────────────────────────────────────────────────────────┐
│  Stage 2 — Semantic Embeddings               ~100ms      │
│  sentence-transformers cosine similarity                 │
│  vs. curated anchor sentences (self-harm + criminal)     │
│  Optional: pip install humane-proxy[ml]                  │
└──────────────────────────────────────────────────────────┘
             ↓ (still ambiguous)
┌──────────────────────────────────────────────────────────┐
│  Stage 3 — Reasoning LLM                     ~1–3s       │
│  LlamaGuard (Groq) or OpenAI Moderation API              │
│  Optional: set OPENAI_API_KEY or GROQ_API_KEY            │
└──────────────────────────────────────────────────────────┘

Configuring the Pipeline

In humane_proxy.yaml:

pipeline:
  # Which stages to run. [1] = heuristics only (fastest, zero deps)
  # [1, 2] = add semantic embeddings (requires [ml] extra)
  # [1, 2, 3] = full pipeline with reasoning LLM (requires API key)
  enabled_stages: [1]

  # Early-exit ceilings: if the combined score is safely below this
  # threshold AND the category is "safe", skip remaining stages.
  stage1_ceiling: 0.3    # exit after Stage 1 if score ≤ 0.3 and safe
  stage2_ceiling: 0.4    # exit after Stage 2 if score ≤ 0.4 and safe

Stage 2 — Semantic Embeddings

Requires the [ml] extra:

pip install humane-proxy[ml]

In humane_proxy.yaml:

pipeline:
  enabled_stages: [1, 2]

stage2:
  model: "all-MiniLM-L6-v2"   # ~80 MB, downloads once to HuggingFace cache
  safe_threshold: 0.35         # cosine similarity below this → safe

The model lazy-loads on first use. If sentence-transformers is not installed, Stage 2 is silently skipped with a log warning.

Stage 3 — Reasoning LLM

Set your API key and optionally configure the provider:

# Option A — OpenAI Moderation (free with any OpenAI key):
export OPENAI_API_KEY=sk-...

# Option B — LlamaGuard via Groq (free tier, very fast):
export GROQ_API_KEY=gsk_...

In humane_proxy.yaml:

pipeline:
  enabled_stages: [1, 2, 3]

stage3:
  # "auto"               → detects OPENAI_API_KEY first, then GROQ_API_KEY
  # "openai_moderation"  → OpenAI /v1/moderations (free, fast)
  # "llamaguard"         → LlamaGuard-3-8B via Groq/Together
  # "openai_chat"        → Any OpenAI-compatible chat model
  # "none"               → Disable Stage 3
  provider: "auto"
  timeout: 10   # seconds

  openai_moderation:
    api_url: "https://api.openai.com/v1/moderations"

  llamaguard:
    api_url: "https://api.groq.com/openai/v1/chat/completions"
    model: "meta-llama/llama-guard-3-8b"

  openai_chat:
    api_url: "https://api.openai.com/v1/chat/completions"
    model: "gpt-4o-mini"

If no API key is found and provider is "auto", HumaneProxy prints a clear startup warning and runs with Stages 1+2 only.

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Self-Harm Care Response

When self-harm is detected, HumaneProxy can respond in two ways:

Mode B — Block (default)

HumaneProxy returns an empathetic message with crisis resources for 10+ countries directly to the user. Your LLM is never involved.

safety:
  categories:
    self_harm:
      response_mode: "block"     # default

      # Optional: override the built-in message
      block_message: "We're here for you. Please reach out to..."

Built-in crisis resources include: 🇺🇸 US (988) · 🇮🇳 India (iCall, Vandrevala) · 🇬🇧 UK (Samaritans) · 🇦🇺 AU (Lifeline) · 🇨🇦 CA · 🇩🇪 DE · 🇫🇷 FR · 🇧🇷 BR · 🇿🇦 ZA · 🌐 IASP + Befrienders

Mode A — Forward with care context

Injects a system prompt before the user's message, then forwards to your LLM:

safety:
  categories:
    self_harm:
      response_mode: "forward"

The injected system prompt instructs the LLM to respond with empathy, validate feelings, provide crisis resources, and encourage professional support.

---

Alert Webhooks

Configure in humane_proxy.yaml:

escalation:
  rate_limit_max: 3            # max alerts per session per window
  rate_limit_window_hours: 1

  webhooks:
    slack_url: "https://hooks.slack.com/services/..."
    discord_url: "https://discord.com/api/webhooks/..."
    pagerduty_routing_key: "your-routing-key"
    teams_url: "https://outlook.office.com/webhook/..."

    # Email alerts via SMTP (stdlib, no extra deps)
    email:
      host: "smtp.gmail.com"
      port: 587
      use_tls: true
      username: "[e-mail]"
      password: "app-password"
      from: "[e-mail]"
      to:
        - "[e-mail]"
        - "[e-mail]"

---

CLI Reference

# Safety check
humane-proxy check "I want to end my life"
# 🆘 FLAGGED — self_harm
# Score   : 1.0
# Category: self_harm

# List recent escalations
humane-proxy escalations
humane-proxy escalations --category self_harm --limit 50

# Session risk history
humane-proxy session user-42

# Start proxy server
humane-proxy start [--host 0.0.0.0] [--port 8000]

# MCP server (requires [mcp] extra)
humane-proxy mcp-serve

---

REST Admin API

Mounted at /admin, secured with HUMANE_PROXY_ADMIN_KEY Bearer token:

export HUMANE_PROXY_ADMIN_KEY=your-secret-key

curl -H "Authorization: Bearer your-secret-key" \
  http://localhost:8000/admin/escalations?category=self_harm&limit=10

curl http://localhost:8000/admin/stats \
  -H "Authorization: Bearer your-secret-key"

# Delete session data (right to erasure)
curl -X DELETE http://localhost:8000/admin/sessions/user-42 \
  -H "Authorization: Bearer your-secret-key"

Endpoint	Description
GET /admin/escalations	Paginated list, filterable by category, session_id
GET /admin/escalations/{id}	Single escalation detail
GET /admin/sessions/{id}/risk	Session history + trajectory
GET /admin/stats	Aggregate counts by category and day
DELETE /admin/sessions/{id}	Delete all session records

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MCP Server (for AI Agents)

pip install humane-proxy[mcp]
humane-proxy mcp-serve

Exposes three tools via Model Context Protocol:

Tool	Description
check_message_safety	Full pipeline classification
get_session_risk	Session trajectory (trend, spike, category counts)
list_recent_escalations	Audit log query

Deploy to Smithery using the included smithery.yaml.

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Configuration Reference

All values can be set in humane_proxy.yaml (project root) or via HUMANE_PROXY_* environment variables. Environment variables always win.

YAML key	Env var	Default	Description
safety.risk_threshold	HUMANE_PROXY_RISK_THRESHOLD	0.7	Score threshold for criminal_intent escalation
safety.spike_boost	—	0.25	Score boost on trajectory spike
server.port	HUMANE_PROXY_PORT	8000	Proxy port
pipeline.enabled_stages	HUMANE_PROXY_ENABLED_STAGES	[1]	Active stages
pipeline.stage1_ceiling	HUMANE_PROXY_STAGE1_CEILING	0.3	Early exit after Stage 1
pipeline.stage2_ceiling	HUMANE_PROXY_STAGE2_CEILING	0.4	Early exit after Stage 2
stage3.provider	HUMANE_PROXY_STAGE3_PROVIDER	"auto"	Stage 3 provider
stage3.timeout	HUMANE_PROXY_STAGE3_TIMEOUT	10	Stage 3 timeout (s)
privacy.store_message_text	—	false	Store raw text (vs SHA-256 hash)
escalation.rate_limit_max	—	3	Max alerts per session/window
safety.categories.self_harm.response_mode	—	"block"	"block" or "forward"

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Privacy

By default HumaneProxy never stores raw message text. Only a SHA-256 hash is persisted for correlation. The escalation DB stores:

• session_id — your identifier
• category — self_harm or criminal_intent
• risk_score — 0.0–1.0
• triggers — which patterns fired
• message_hash — SHA-256 of the original text
• stage_reached — which pipeline stage produced the result
• reasoning — Stage-3 LLM reasoning (if available)

To enable raw text storage (e.g. for human review):

privacy:
  store_message_text: true

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Installation Extras

Extra	Command	What it adds
(none)	pip install humane-proxy	Stage 1 heuristics + full API + CLI
ml	pip install humane-proxy[ml]	Stage 2 semantic embeddings (sentence-transformers)
mcp	pip install humane-proxy[mcp]	MCP server for AI agent integration (fastmcp)
all	pip install humane-proxy[all]	Everything above

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License

Apache 2.0. See LICENSE.

Copyright 2026 Vishisht Mishra (@Vishisht16). Any attribution is appreciated.

See NOTICE for full attribution information.

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Built for a safer world.