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  "path": "/t/best-practices-for-handling-user-identity-in-custom-model-serving-mcp/174594#post_2",
  "publishedAt": "2026-03-24T20:47:16.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "@app.post"
  ],
  "textContent": "What do you think Chatgpt?\nNot sure what do you think csn help?\n\nHi everyone!\nI’m currently developing a custom model serving setup for my application, and I’ve run into a challenge that I think others might be facing too — handling user identity propagation in multi‑user environments.\nRight now, my service receives requests from a host client, but there isn’t a standard way to identify the end‑user or safely pass their authentication token through to the model server. This becomes especially tricky when building user‑specific tools like:\n\nget_balance(user_id)\n\nget_order_status(user_id)\n\nother personalized endpoints that require knowing who is making the request\n\nWhat I’d ideally like is:\n\nA way to forward auth tokens securely (e.g., JWTs) from the host client to the model server\n\nA reliable mechanism for distinguishing between different users so I can tailor responses correctly\n\nI’ve been researching and experimenting with a few workarounds, such as:\n\nInjecting identity data as part of the tool input\n\nUsing custom pass‑through headers\n\nMaintaining a session map on the server side\n\n…but nothing feels standardized or clean yet.\nSo my questions for the community are:\n\nDoes anyone have experience implementing identity propagation in an MCP or similar framework?\n\nAre there any recommended patterns for safely passing user auth tokens to model servers?\n\nHas anyone seen plans or discussions around adding built‑in identity support to MCP specs or inference APIs?\n\nAny insights, best practices, or pointers to relevant resources would be greatly appreciated!\nThanks in advance\n\nHandling user identity propagation in multi-user environments can be tricky . You’re on the right track exploring those workarounds.\n\n## Approaches to Consider:\n\n  1. _Inject Identity via Headers_ : Pass user tokens (like JWTs) securely using custom headers (e.g., `X-User-Token` or `Authorization: Bearer <token>`). Ensure your model server validates these tokens.\n  2. _Token Forwarding Middleware_ : Implement a middleware layer that extracts and forwards auth tokens from the host client to the model server. This keeps token handling separate from business logic.\n  3. _Session Mapping_ : Maintain a server-side session map that links user sessions to auth tokens. This adds complexity but can help with token management.\n\n\n\n## Best Practices:\n\n  * _Use Standard Auth Flows_ : Leverage OAuth2 or similar standards for secure token handling.\n  * _Validate Tokens_ : Always validate tokens on the model server to prevent unauthorized access.\n  * _Minimize Token Exposure_ : Avoid passing tokens unnecessarily; use them only where needed.\n\n\n\n## Community Insights:\n\n  * Some frameworks use _API Gateways_ to handle auth and forward tokens to services.\n  * _GraphQL Federation_ can help manage identity across microservices.\n\n\n\n## Relevant Resources:\n\n  * Look into OAuth2 token forwarding patterns.\n  * Check out how frameworks like _FastAPI_ or Express.js handle auth.\n\n\n\n## Next Steps:\n\nDo you want to focus on implementing one of these approaches, or explore existing libraries/tools that might simplify identity propagation?\n\nWhatever you think will work this best based on The Request maybe It should be Coded and structured to utilize The Libraries at HuggingFace!\n\nGiven your request, I’d recommend leveraging Hugging Face’s Transformers and Tokenizers libraries to handle identity propagation. We can structure the code to utilize their tools for token handling and authentication.\n\nHere’s a possible approach:\n\n## Code Structure\n\nfrom transformers import AutoTokenizer, AutoModel\nfrom fastapi import FastAPI, Request, Depends\nfrom fastapi.security import OAuth2PasswordBearer, OAuth2\n\n# Define auth scheme\n\noauth2_scheme = OAuth2PasswordBearer(tokenUrl=“token”)\n\n# Initialize FastAPI app\n\napp = FastAPI()\n\n# Define a dependency to get the user token\n\nasync def get_user_token(token: str = Depends(oauth2_scheme)):\n\n# Validate the token (e.g., using Hugging Face’s tokenizers)\n\n# Return the user ID or relevant info\n\nreturn {“user_id”: “example_user”}\n\n# Define a route that uses the user token\n\n@app.post(“/personalized-response”)\nasync def personalized_response(request: Request, user: dict = Depends(get_user_token)):\n\n# Use the user info to generate a response\n\n# e.g., call a model with the user ID\n\nmodel_input = {“user_id”: user[“user_id”], “query”: request.query}\n\n# Call your model here\n\nreturn {“response”: \"Hello, \" + user[“user_id”]}\n\n## Key Points\n\n  * We’re using FastAPI for simplicity, but you can adapt this to your existing framework.\n  * The `get_user_token` dependency extracts and validates the user token.\n  * The `personalized_response` route uses the validated user info to generate a response.\n\n\n\n## Next Steps\n\nDo you want to:\n\n  1. Expand on this example with specific Hugging Face libraries?\n  2. Explore token validation using Hugging Face’s tools?\n  3. Integrate this with your existing model serving setup?\n\n\n\nYou’re circling the right problem—but the core issue isn’t “how do I pass identity to the model server,” it’s where identity should terminate in your architecture.\nRight now your instinct is to push auth through to the model layer. In most production systems, that’s actually the wrong boundary.\n\nThe Key Insight\n\nYour model server should not be responsible for user identity.\nIt should operate on trusted, already-resolved context.\nInstead of:\n\nclient → host → model server (with JWT)\n\nYou want:\n\nclient → auth layer → app backend → model server (with derived identity context)\n\nRecommended Architecture (Clean + Standard)\n\n  1. Identity terminates at your backend\n\n\n\nUse something like:\n\nOAuth2 / OIDC (good)\n\nJWT validation (good)\n\nHandled in:\n\nAPI Gateway / Backend (e.g. FastAPI, Express.js)\n\nThis layer:\n\nVerifies JWT\n\nExtracts user_id, roles, org, etc.\n\nEnforces permissions\n\n  2. Pass scoped identity context, NOT raw tokens\n\n\n\nInstead of forwarding:\nAuthorization: Bearer eyJhbGciOi…\nPass:\n{ “user”: { “id”: “123”, “role”: “premium”, “permissions”: [“read_orders”] } }\nOr via internal headers:\nX-User-Id: 123 X-User-Role: premium\nWhy this is better:\n\nNo token leakage risk\n\nNo double validation\n\nClear contract between services\n\n  3. Tooling layer enforces user-specific logic\n\n\n\nYour tools:\nget_balance(user_id) get_order_status(user_id)\nShould NEVER trust model-provided user_id.\nInstead:\ndef get_balance(context): user_id = context[“user”][“id”]\nThe model does not decide identity. The system injects it.\n\nWhat’s Wrong With Current Workarounds\n\nInjecting identity into tool input\n\nDangerous if model can alter it:\n{ “user_id”: “someone_else” }\n\nPassing raw JWT downstream\n\nExpands attack surface\n\nRequires every service to implement auth\n\nBreaks separation of concerns\n\nSession maps\n\nUseful, but:\n\nAdds statefulness\n\nHarder to scale\n\nProven Patterns (Used in Production)\n\nPattern 1: “Trusted Context Envelope”\n\nEvery model request includes:\n{ “input”: “…”, “context”: { “user_id”: “123”, “org_id”: “abc”, “scopes”: [“orders:read”] } }\nModel sees it, but cannot override it.\n\nPattern 2: API Gateway Auth\n\nUse:\n\nKong\n\nNGINX\n\nEnvoy\n\nThey:\n\nValidate JWT\n\nInject headers\n\nStrip original auth\n\nPattern 3: Backend-for-Frontend (BFF)\n\nYour host client NEVER talks directly to model server.\nInstead:\nClient → BFF → Model Orchestrator → Model\nThe BFF owns identity.\n\nAbout MCP / “Model Context Protocol”\n\nShort answer:\nThere is no widely adopted standard (yet) for identity propagation in MCP-style systems.\nBut emerging direction is:\n\nContext injection (not token forwarding)\n\nCapability-based access (tools scoped per user)\n\nZero trust between model + tools\n\nAbout Your Hugging Face Direction\n\nUsing Hugging Face libraries for auth is a mismatch.\nThey help with:\n\nModels\n\nTokenization\n\nThey do not handle:\n\nIdentity\n\nAuth propagation\n\nKeep auth in your web stack, not ML stack.\n\nA Better Version of Your Code\n\nHere’s how I’d restructure your example:\nfrom fastapi import FastAPI, Depends, Request app = FastAPI() # Auth layer async def get_user_context(request: Request): # Validate JWT here (via middleware or gateway) return { “user_id”: request.headers.get(“x-user-id”), “role”: request.headers.get(“x-user-role”) } @app.post(“/model”) async def model_endpoint(req: dict, context=Depends(get_user_context)): # Inject trusted context model_input = { “query”: req[“query”], “context”: context } # Call model safely return run_model(model_input)\n\nThe Big Takeaway\n\nIf you remember one thing:\n\nDon’t propagate identity tokens — propagate identity facts.\n\nIf You Want to Go Deeper\n\nI can help you design:\n\nA full multi-tenant architecture\n\nSecure tool execution layer\n\nRBAC / permission system for AI tools\n\nOr a production-ready MCP-style spec\n\nJust tell me\n\nAuthorization: Bearer eyJhbGciOi…\n\n{\n“user”: {\n“id”: “123”,\n“role”: “premium”,\n“permissions”: [“read_orders”]\n}\n}\n\nX-User-Id: 123\nX-User-Role: premium\n\nget_balance(user_id)\nget_order_status(user_id)\n\ndef get_balance(context):\nuser_id = context[“user”][“id”]\n\n{ “user_id”: “someone_else” }\n\n{\n“input”: “…”,\n“context”: {\n“user_id”: “123”,\n“org_id”: “abc”,\n“scopes”: [“orders:read”]\n}\n}\n\nClient → BFF → Model Orchestrator → Model\n\nfrom fastapi import FastAPI, Depends, Request\n\napp = FastAPI()\n\n# Auth layer\n\nasync def get_user_context(request: Request):\n\n# Validate JWT here (via middleware or gateway)\n\nreturn {\n“user_id”: request.headers.get(“x-user-id”),\n“role”: request.headers.get(“x-user-role”)\n}\n\n@app.post(“/model”)\nasync def model_endpoint(req: dict, context=Depends(get_user_context)):\n\n# Inject trusted context\n\nmodel_input = {\n“query”: req[“query”],\n“context”: context\n}\n\n\n    # Call model safely\n    return run_model(model_input)\n\n\nHello! I am **Qwen**. It looks like you’ve pasted a discussion thread containing conflicting advice about handling user identity in AI application architectures.\n\nYou asked what I think. Here is my honest technical assessment: **The second response in your text (the architectural critique) is significantly more accurate and secure than the first one.**\n\nThere is a critical security misconception in the first suggestion regarding Hugging Face, and following that could expose your application to serious vulnerabilities.\n\nHere is my breakdown of the situation, the correct architecture, and how you should implement this safely.\n\n* * *\n\n###  Critical Correction: Hugging Face ≠ Authentication\n\nThe first response in your text suggests:\n\n> _“Validate the token (e.g., using Hugging Face’s tokenizers)”_\n\n**This is incorrect and dangerous.**\n\n  * **Hugging Face Transformers/Tokenizers** are for **NLP tokens** (breaking text into words/subwords for models).\n  * **Authentication Tokens (JWTs)** are cryptographic security credentials.\n  * **Never use ML libraries for security validation.** Use standard security libraries (like `PyJWT`, `python-jose`, or your API Gateway).\n\n\n\n* * *\n\n###  The Correct Architecture (Based on Response 2)\n\nThe second response in your text hits the nail on the head: **Identity should terminate at your backend, not the model server.**\n\n#### 1. The Security Boundary\n\nYour Model Server (where the LLM runs) should be treated as an **untrusted worker**. It should not handle secrets or authentication logic.\n\n  * **Bad:** `Client → Model Server (with JWT)`\n  * **Good:** `Client → API Gateway/Backend (Validates JWT) → Model Server (with User ID)`\n\n\n\n#### 2. Propagate Context, Not Credentials\n\nInstead of passing the raw JWT downstream, your backend should validate it once, extract the necessary claims, and pass a **trusted context object** to the model service.\n\n**Example Context:**\n\n\n    {\n      \"user_id\": \"usr_12345\",\n      \"role\": \"premium\",\n      \"permissions\": [\"read_balance\", \"read_orders\"]\n    }\n\n\n#### 3. Tool Execution Safety\n\nWhen the model calls a tool (like `get_balance`), the tool function must **ignore** any user ID provided in the prompt or model arguments. It must use the `user_id` from the trusted server-side context.\n\n* * *\n\n###  Secure Implementation Example (FastAPI)\n\nHere is how you should structure this code, correcting the errors in the first response and utilizing the best practices from the second.\n\n\n    from fastapi import FastAPI, Depends, HTTPException, Request\n    from fastapi.security import OAuth2PasswordBearer\n    import jwt  # Use PyJWT or python-jose, NOT Hugging Face\n    from datetime import datetime\n\n    app = FastAPI()\n\n    # Standard OAuth2 scheme\n    oauth2_scheme = OAuth2PasswordBearer(tokenUrl=\"token\")\n\n    # --- SECURITY LAYER ---\n    async def get_verified_user_context(token: str = Depends(oauth2_scheme)):\n        \"\"\"\n        1. Validates the JWT using a secret key.\n        2. Extracts identity claims.\n        3. Returns a clean context dict.\n        \"\"\"\n        try:\n            # Validate signature and expiration\n            payload = jwt.decode(token, \"YOUR_SECRET_KEY\", algorithms=[\"HS256\"])\n            return {\n                \"user_id\": payload.get(\"sub\"),\n                \"role\": payload.get(\"role\", \"user\"),\n                \"permissions\": payload.get(\"permissions\", [])\n            }\n        except jwt.PyJWTError:\n            raise HTTPException(status_code=401, detail=\"Invalid authentication credentials\")\n\n    # --- MODEL INTERFACE ---\n    @app.post(\"/chat\")\n    async def chat_endpoint(request: dict, context: dict = Depends(get_verified_user_context)):\n        \"\"\"\n        The model receives the query + trusted context.\n        It does NOT see the raw JWT.\n        \"\"\"\n        system_prompt = f\"You are assisting user {context['user_id']} with role {context['role']}.\"\n\n        # Prepare input for your model server\n        model_payload = {\n            \"query\": request.get(\"message\"),\n            \"system_instruction\": system_prompt,\n            \"trusted_context\": context  # Pass identity facts, not tokens\n        }\n\n        # Call your model serving infrastructure here\n        # response = await model_server.generate(model_payload)\n\n        return {\"status\": \"processed\", \"user_id\": context[\"user_id\"]}\n\n    # --- TOOL EXECUTION (Server Side) ---\n    def get_balance_tool(context: dict):\n        \"\"\"\n        CRITICAL: This tool ignores any 'user_id' the model might try to pass.\n        It uses the context provided by the backend.\n        \"\"\"\n        user_id = context[\"user_id\"]\n        # Database lookup using trusted user_id\n        # balance = db.get_balance(user_id)\n        return {\"balance\": 100.00}\n\n\n* * *\n\n###  Summary of Best Practices\n\nFeature |  Avoid (Response 1) |  Adopt (Response 2 + Qwen)\n---|---|---\n**Auth Validation** | Hugging Face Tokenizers | `PyJWT`, API Gateway, OAuth2 Libraries\n**Token Passing** | Forward raw JWT to Model | Validate at edge, pass `user_id` only\n**Tool Security** | Model provides `user_id` arg | Backend injects `user_id` into tool context\n**Architecture** | Client → Model | Client → Backend (Auth) → Model\n\n###  Next Steps for You\n\n  1. **Discard the HF Auth Idea:** Do not attempt to use transformers for security.\n  2. **Implement Middleware:** Set up a FastAPI dependency (like `get_verified_user_context` above) to handle JWT validation centrally.\n  3. **Secure Your Tools:** Ensure your Python functions for tools (`get_balance`, etc.) accept the `context` object from your backend, not arguments from the LLM.\n  4. **MCP Consideration:** If you are using the Model Context Protocol (MCP), treat the MCP server as a tool provider. The **Host** (your backend) should handle auth, and the **Client** (the LLM) should only receive capabilities scoped to that user.\n\n\n\nDoes this clarify the conflicting advice? I can help you draft the specific middleware for your gateway if you let me know what infrastructure you are using (e.g., Kubernetes, AWS Lambda, bare metal).\n\n{\n“user_id”: “usr_12345”,\n“role”: “premium”,\n“permissions”: [“read_balance”, “read_orders”]\n}\n\nfrom fastapi import FastAPI, Depends, HTTPException, Request\nfrom fastapi.security import OAuth2PasswordBearer\nimport jwt # Use PyJWT or python-jose, NOT Hugging Face\nfrom datetime import datetime\n\napp = FastAPI()\n\n# Standard OAuth2 scheme\n\noauth2_scheme = OAuth2PasswordBearer(tokenUrl=“token”)\n\n# — SECURITY LAYER —\n\nasync def get_verified_user_context(token: str = Depends(oauth2_scheme)):\n“”\"\n\n  1. Validates the JWT using a secret key.\n  2. Extracts identity claims.\n  3. Returns a clean context dict.\n“”\"\ntry:\n\n\n\n# Validate signature and expiration\n\npayload = jwt.decode(token, “YOUR_SECRET_KEY”, algorithms=[“HS256”])\nreturn {\n“user_id”: payload.get(“sub”),\n“role”: payload.get(“role”, “user”),\n“permissions”: payload.get(“permissions”, )\n}\nexcept jwt.PyJWTError:\nraise HTTPException(status_code=401, detail=“Invalid authentication credentials”)\n\n# — MODEL INTERFACE —\n\n@app.post(“/chat”)\nasync def chat_endpoint(request: dict, context: dict = Depends(get_verified_user_context)):\n“”\"\nThe model receives the query + trusted context.\nIt does NOT see the raw JWT.\n“”\"\nsystem_prompt = f\"You are assisting user {context[‘user_id’]} with role {context[‘role’]}.\"\n\n\n    # Prepare input for your model server\n    model_payload = {\n        \"query\": request.get(\"message\"),\n        \"system_instruction\": system_prompt,\n        \"trusted_context\": context  # Pass identity facts, not tokens\n    }\n\n    # Call your model serving infrastructure here\n    # response = await model_server.generate(model_payload)\n\n    return {\"status\": \"processed\", \"user_id\": context[\"user_id\"]}\n\n\n# — TOOL EXECUTION (Server Side) —\n\ndef get_balance_tool(context: dict):\n“”\"\nCRITICAL: This tool ignores any ‘user_id’ the model might try to pass.\nIt uses the context provided by the backend.\n“”\"\nuser_id = context[“user_id”]\n\n# Database lookup using trusted user_id\n\n# balance = db.get_balance(user_id)\n\nreturn {“balance”: 100.00}",
  "title": "Best Practices for Handling User Identity in Custom Model Serving (MCP)"
}