# Securing Your Own API: Authentication and Authorization

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# Securing your own API: authentication and authorization for your own users

 By Ivo Donker — compiled with AI assistance (Claude & Gemini)

 Much of the technical documentation on language models focuses exclusively on securely storing upstream provider keys for platforms like OpenAI, Anthropic, or Mistral. But anyone deploying an AI-driven application to production almost always builds their own backend or gateway that is called by internal microservices, mobile apps, or external SaaS clients. For upstream communication, read the anchor article on [how to securely manage API keys for LLMs](https://api.llmnet.nl/en/api-sleutels-veilig-beheren), but on the downstream side, a fundamentally different challenge emerges: how do you verify who the caller is, and how do you restrict what that user is allowed to consume?

 An LLM endpoint differs fundamentally from a traditional REST endpoint. Whereas a regular CRUD operation requires predictable server compute, an uncontrolled prompt directly incurs variable costs, asynchronous compute time, and potential data leaks. Authentication and authorization therefore do not merely serve as a security layer for data access, but also function as the financial and operational brake on your system. In this article, we cover the architecture, token structures, fine-grained permission models, and practical middleware patterns to make your downstream LLM API watertight.

 
## The architectural separation: upstream versus downstream

 In a robust AI architecture, there are always two strictly separated trust domains. On the upstream side, the backend communicates with the foundation model providers. This communication relies on secret, long-lived API keys that must never be exposed to clients under any circumstances. On the downstream side, browsers, mobile applications, and external integration partners talk to your own API gateway. Here, short-lived sessions, strict identity checks, and fine-grained permissions apply.

 If a client were to call an upstream provider directly using a shared key, you would lose all visibility into individual user behavior, and a malicious actor could completely exhaust your token quota. Your own API acts as a protective intermediate layer (the reverse proxy or gateway) that inspects, validates, enriches, and authorizes incoming requests before a single token is ever sent to an external model.

 
 
 
 
 Property | 
 Upstream authentication (Provider) | 
 Downstream authentication (Own API) | 
 

 
 
 
 Purpose | 
 Access to raw model GPUs and endpoints | 
 Access to application logic, tools, and context | 
 

 
 Identity | 
 Single central organization or service account | 
 Individual end user, tenant, or API consumer | 
 

 
 Token lifetime | 
 Long-lived (months until rotation) | 
 Short-lived (minutes to hours via JWT/OIDC) | 
 

 
 Control mechanism | 
 Static bearer tokens or cloud IAM | 
 Dynamic scopes, RBAC, ABAC, and budget caps | 
 

 
 
 

 
## Authentication mechanisms: API keys, OAuth2, and JWTs

 The choice of authentication mechanism depends on the type of client connecting. For machine-to-machine (M2M) communication, such as background services or external developers integrating your AI pipeline via code, hashed API keys are the industry standard. For interactive web applications and mobile clients, a token-based model via OpenID Connect (OIDC) and OAuth2 with JSON Web Tokens (JWT) is the recommended path.

 When issuing custom API keys for developers, the key must never be stored in readable text (plaintext) in the database. Store only a cryptographic hash (such as SHA-256) and display the secret key only once upon creation. Give the key an identifiable prefix (for example llm_live_...) so that automated secret leak scanners can immediately identify and block the key.

 For user sessions, a signed JWT format offers major advantages because the gateway can cryptographically verify whether the token is valid without querying the central authentication database on every incoming streaming chunk. Ensure that asymmetric algorithms such as RS256 or EdDSA are used, so verification servers only require the public key.

 
## Fine-grained authorization: from RBAC to Scopes and ABAC

 Authentication determines who the caller is; authorization determines what they are permitted to do. Within LLM applications, a traditional role-based model (RBAC) with simple roles like 'admin' and 'user' is rarely sufficient. The variation in compute power, costs, and tool sensitivity requires fine-grained permissions (scopes) or attribute-based access control (ABAC).

 Define scopes that explicitly indicate which actions a token is allowed to initiate. Typical examples include:

 
 
- models:cheap:invoke: Access to fast, cost-effective models for routine tasks.
 
- models:reasoning:invoke: Access to expensive reasoning models with extensive chain-of-thought capabilities.
 
- tools:database:read: Permission for the LLM to call functions that read internal data sources.
 
- tools:system:execute: Highly restrictive scope for actions that make external changes (mutations, sending emails, executing code).
 

 With Attribute-Based Access Control (ABAC), you can enforce additional dynamic rules. For instance, a junior employee might have the scope to generate a summary, but the authorization filter will reject the call as soon as the context document is labeled 'confidential' or when the call occurs outside of office hours.

 
## Multi-tenant isolation and context injection at the gateway level

 In a SaaS environment where multiple customers run on the same infrastructure, data isolation is crucial. Without strict separation, there is a risk that embeddings or system instructions from one tenant could be inadvertently injected into another user's prompt. For a deeper dive into architectural data separation, see the article on [building multi-tenant LLM applications](https://api.llmnet.nl/en/multi-tenant-llm-apps), which covers tenant isolation and data partitioning in detail.

 The authentication middleware must, with every call, tenant_id extracting it from the validated claims of the token. This identifier is then hard-bound to the context of the request. This guarantees that all downstream search queries across vector indices, document storage, and conversation history are automatically filtered by the respective tenant, without the client being able to independently manipulate this parameter.

# Voorbeeld van generieke middleware-autorisatie in Python (FastAPI/Starlette stijl)
import time
import hmac
import hashlib
from typing import Optional, Set
from dataclasses import dataclass

@dataclass
class AuthContext:
 user_id: str
 tenant_id: str
 scopes: Set[str]
 max_cost_limit_cents: int

class LLMAuthorizationError(Exception):
 """Foutmelding bij ontoereikende rechten of budgetoverschrijding."""
 pass

def authorize_llm_request(
 auth: AuthContext,
 required_model_tier: str,
 requested_tools: list[str],
 estimated_cost_cents: int
) -> None:
 # 1. Controleer modelpermissie
 required_scope = f"models:{required_model_tier}:invoke"
 if required_scope not in auth.scopes and "admin:all" not in auth.scopes:
 raise LLMAuthorizationError(
 f"Geen toegang tot modeltier '{required_model_tier}'. Vereist: {required_scope}"
 )

 # 2. Controleer tool-permissies
 for tool in requested_tools:
 tool_scope = f"tools:{tool}:execute"
 if tool_scope not in auth.scopes and "admin:all" not in auth.scopes:
 raise LLMAuthorizationError(f"Geen toestemming voor tool: {tool}")

 # 3. Controleer harde financiële limiet per request
 if estimated_cost_cents > auth.max_cost_limit_cents:
 raise LLMAuthorizationError("Aanvraag overschrijdt maximaal toegestane transactiekosten.")

 
## Token budgeting and per-user quota enforcement

 A crucial aspect of authorization in AI backends is budget management. While standard web APIs express rate limits in 'requests per second' (RPS), LLM systems require limits based on consumed tokens and incurred costs. A user sending ten short prompts of 50 tokens each places significantly less load on the system than a user processing a single document of 100,000 tokens.

 Therefore, implement a two-tiered control system:

 
 
- Pre-flight estimation: Before the prompt is sent to the upstream provider, a local tokenizer calculates the size of the input plus the maximum number of reserved output tokens (max_tokens). The authorization layer checks a fast in-memory store (such as Redis) to ensure the user's balance is sufficient.
 
- Post-execution reconciliation: As soon as the LLM call completes or the stream terminates, the gateway reads the actual reported usagemetrics, and the exact consumption is immediately reconciled in the records.
 

 If a user exceeds their quota, the API returns an explicit 429 Too Many Requests or 402 Payment Required status code, including a header indicating when the allowance will be replenished.

 
## Input validation and session hygiene

 Authentication keeps unauthorized users out, but it does not protect against malicious instructions from authenticated accounts. Prompt injection and data exfiltration attempts can infiltrate through authorized channels. For additional context, read the overview on [input validation and output filtering for LLM integrations](https://api.llmnet.nl/en/invoervalidatie-en-outputfiltering) to see how to structure prompts and sanitize model output.

 Additionally, tie authentication status to context restriction. A verified session should never overwrite system prompts unfiltered. Always securely store your application's core instructions server-side and combine them via strict templates with validated user input. This prevents an authenticated attacker from bypassing the internal system prompt by manipulating headers or parameters.

 
## Session revocation, key rotation, and zero-trust validation

 In distributed systems, using stateless JWTs introduces a known risk: revoking a session before the token expires is difficult. Because LLM calls are financially costly, a compromised token can cause significant damage within minutes. Therefore, use very short token lifetimes (for example, 5 to 15 minutes) combined with a centralized revocation list (or blocklist) in a high-speed key-value store.

 For developer API keys, automated rotation is essential. Always support a transitional period where both the old and new keys are active simultaneously (a dual-key window), enabling integration partners to update their systems without downtime.

 Additionally, internal communication between your gateway and backend workers must be designed according to the zero-trust principle. Do not rely on a request being legitimate purely because it originates from an internal IP address. Implement mutual TLS authentication (mTLS) between internal components and grant each service only the minimum required upstream permissions.

 If you opt for strict data minimization and do not want to leave any data with upstream providers, read the guide on [configuring zero data retention for LLM APIs](https://api.llmnet.nl/en/zero-data-retention-api-configuraties) to learn how to disable data retention both contractually and technically.

 
## Privacy and compliance in user verification

 Logging authentication events creates friction with privacy regulations. While detecting abuse requires knowing which user submitted which request, storing full prompts linked to identifiable personal data introduces substantial GDPR risks. For legal frameworks and privacy considerations, consult the article on [AI models and privacy within GDPR compliance](https://hub.llmnet.nl/en/ai-modellen-en-privacy-avg-compliance) on the hub subdomain.

 In your own API's audit logging, maintain a strict separation between identity data and payload content. Log the user_id, tenant_id, timestamp, token usage, and HTTP status in a secure audit database. Mask or hash sensitive identifiers, and only retain prompt content when strictly necessary for compliance, backed by an automated retention policy.

 
## Failure modes, measurement methods, and operational trade-offs

 Every security mechanism introduces potential points of failure and overhead. It is essential to recognize these failure modes, measure them continuously, and implement targeted mitigations.

 1. Latency overhead from verification:

 
 
- Failure mode: Complex authorization checks (such as fetching user profiles from a relational database and calculating balances) add tens of milliseconds to the time-to-first-token (TTFT).
 
- Detection: Measure middleware duration separately using OpenTelemetry spans before the upstream request is dispatched.
 
- Mitigation: Validate cryptographic JWT signatures locally in memory and use distributed in-memory caches for rate-limit counters.
 
- Cost: Requires additional RAM capacity on gateway nodes and introduces potential synchronization delays of a few milliseconds between nodes.
 

 2. Race conditions in streaming and budget depletion:

 
 
- Failure mode: A user initiates ten parallel streaming requests. Because the exact costs are only known upon completion, the user significantly exceeds their budget before the counters are updated.
 
- Detection: Periodically reconcile the recorded budget balance against actual billed provider usage per user.
 
- Mitigation: Pre-allocate a fixed token amount (such as the value of max_tokens) against the balance and release unused tokens immediately once the stream closes. Terminate active SSE streams right away if the balance reaches zero during streaming.
 
- Cost: Increased complexity in the gateway's state machine and the potential for premature rejection of legitimate requests if users set an excessively high max_tokens .
 

 3. Cascading failures during authentication provider outages:

 
 
- Failure mode: The central identity provider (IdP) is slow or unreachable, causing all incoming LLM calls to stall on timeout errors.
 
- Detection: Monitor the error rate on authentication endpoints (HTTP 500/504) separately from upstream model errors.
 
- Mitigation: Apply aggressive caching to public key sets (JWKS) and configure circuit breakers that return controlled error messages instead of letting queues overflow.
 
- Cost / Trade-off: A short delay in propagating revoked keys as long as the JWKS cache remains valid.
 

 
## Conclusion and Implementation Order

 Securing your own LLM API requires more than simply shielding a URL with a password. By creating a clear separation between upstream provider keys and downstream user tokens, you build a controlled environment where costs, data access, and functionality are meticulously managed.

 When implementing, always start with robust JWT or API key validation combined with strict tenant isolation at the gateway level. Next, add fine-grained scopes for model tiers and tools, and conclude with dynamic token budgeting and real-time streaming cancellation. This not only protects your intellectual property and user data, but also keeps the operational costs of your AI infrastructure fully under control.