LiteLLM alternatives expose the access-control gap in LLM gateways

At a glance

What this is: This is a guide to LiteLLM alternatives that frames the central problem as access control, auditability, and governance at the LLM gateway layer.

Why it matters: It matters because IAM, PAM, and NHI teams need to decide where identities, policies, and logs live when LLM traffic crosses multiple providers and internal gateways.

By the numbers:

• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
• 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so.
• When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes.

👉 Read Pomerium’s comparison of LiteLLM alternatives for secure LLM access

Context

LLM gateways sit at the control point where identity, policy, and telemetry either become enforceable or remain fragmented. In practice, the gateway is where teams decide whether model access is just a routing problem or part of broader identity governance across human users, service accounts, and AI-driven workflows.

Pomerium’s article argues that LiteLLM’s open-source version leaves gaps around authentication, audit logging, and policy controls, which is why some teams are evaluating alternatives. That is a governance problem as much as a tooling decision, because LLM traffic often moves faster than conventional access review and logging practices can comfortably support.

Key questions

Q: How should security teams govern access to LLM gateways?

A: They should treat the gateway as the enforcement point for authentication, authorization, and audit logging. That means identifying the human, application, and non-human identities behind each request, applying policy centrally, and retaining complete request evidence for review. If governance sits only in app code, it will not scale across multiple providers.

Q: Why do LLM gateways create an NHI security problem?

A: Because many gateway paths rely on service accounts, API keys, and backend tokens that act as non-human identities. Those credentials can carry broad provider access, so a weak lifecycle or missing audit trail turns model access into a larger governance and exposure problem than a simple routing layer would suggest.

Q: What do teams get wrong about open-source LLM gateways?

A: They often assume that API compatibility implies security completeness. In practice, open-source gateways may still require separate work for authentication, policy enforcement, logging, and credential management. If those controls are not designed in, the organisation gets abstraction without reliable governance.

Q: When should organisations add identity-aware controls to their LLM stack?

A: They should add them before model usage becomes multi-provider, multi-team, or production-critical. Once requests cross several applications and credentials are shared across workflows, the audit and access problem becomes harder to retrofit. Early control placement is cheaper than reconstructing governance later.

Technical breakdown

Why access control belongs at the LLM gateway

An LLM gateway is the enforcement layer between users or applications and one or more model providers. If it only normalises APIs, it does not govern who can call which model, under what conditions, or with what evidence. Identity-aware gateways add authentication, policy checks, request logging, and credential handling before traffic reaches the model endpoint. That makes the gateway the place where access can be constrained by role, device, location, or request context. Without that layer, teams often discover that model abstraction has outpaced governance.

Practical implication: place authorization and logging at the gateway, not in scattered application code.

Audit logging and policy controls for multi-model environments

Multi-provider LLM estates create a familiar identity problem: many paths in, inconsistent controls out. A request may originate from a human user, an internal application, or an automation pipeline, yet each can traverse the same model gateway. Without full request logs, teams cannot reconstruct who accessed what, which prompt went where, or whether the request complied with policy. Policy engines matter because they turn abstract governance into enforceable constraints. In regulated environments, this is the difference between demonstrable control and post-incident guesswork.

Practical implication: require immutable request logs and policy decisions for every model call.

Credential rotation and token leakage risk in LLM stacks

LLM gateways often depend on API keys, service tokens, and backend credentials to connect to providers and internal systems. Those secrets become high-value NHIs because they sit on the path between users and models and can grant broad downstream access. Automated rotation reduces the exposure window, but only if teams know where each credential is used and can revoke it without breaking service. If the gateway is loosely governed, a leaked token can be enough to let an attacker use model access as an entry point into broader data exposure.

Practical implication: inventory gateway secrets, map their scope, and rotate them on a defined lifecycle.

NHI Mgmt Group analysis

LLM gateways are becoming identity control points, not just routing layers. The article’s core signal is that model abstraction alone does not solve governance. Once a gateway handles traffic for multiple providers, it becomes the enforcement point for authentication, policy, and audit evidence across human users and non-human identities. Practitioners should treat the gateway as part of the identity plane, not an app convenience layer.

Open-source flexibility is not the same as security completeness. The article shows a common pattern in AI infrastructure: teams adopt a flexible gateway first, then discover that auth, logging, and policy controls are either missing or unevenly implemented. That gap matters because LLM usage creates high-frequency, high-context access events that conventional application logging does not always capture well. The implication is that identity governance must be designed into the access path from the start.

LLM access creates an NHI governance problem even when humans are the primary users. Model calls often depend on service accounts, API keys, and backend tokens behind the scenes, which means the effective actor is frequently non-human even when the requester is a person. That shifts the control model toward NHI lifecycle management, secrets handling, and request-level policy enforcement. Practitioners should map every LLM path to its underlying identity chain before they approve scale-out.

Identity-aware proxies are now the practical answer to LLM sprawl. The article supports a broader field view: as teams connect more models, more providers, and more internal tools, the governance burden shifts toward an identity-aware access layer that can be applied consistently. That does not replace model routing or observability tools. It establishes the control plane that keeps those tools governable in production.

From our research:

• 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to SailPoint.
• For a broader control-plane view, see OWASP NHI Top 10 and the OWASP Agentic AI Top 10.

What this signals

Identity-aware gateways will increasingly become the control point for LLM governance. As teams connect more providers and more internal workflows, the question stops being which model gateway is convenient and becomes which one can enforce policy, preserve audit evidence, and fit into existing IAM and NHI controls. That is why gateway selection should now be treated as an identity architecture decision, not a developer preference. For practitioners, the practical next step is to align gateway controls with access review, secrets governance, and request logging before scale makes those changes harder.

Ephemeral model access creates an identity evidence problem as well as a security problem. If requests are mediated by short-lived tokens, scattered service accounts, and user-to-service delegation chains, the organisation can lose sight of who actually had access at the moment of a call. This is where lifecycle governance and request telemetry have to meet. Teams that already use the OWASP NHI Top 10 should map LLM gateways to the same identity control expectations they apply to other high-value non-human access paths.

For practitioners

• Map every LLM request to an identity chain Document the human user, service account, token, and backend credentials involved in each model path so you know which identity actually holds access at enforcement time.
• Centralise policy enforcement at the gateway Apply role, device, location, and request-context rules in one control point instead of duplicating checks across applications and model wrappers.
• Instrument complete request logging Log prompt metadata, model selection, policy decision, and identity context for every request so audit and investigation do not depend on application-specific traces.
• Inventory and rotate gateway secrets Track every API key and token used by the gateway, define owners, and rotate credentials on a lifecycle schedule that matches their access scope.
• Separate routing choice from governance choice Do not confuse model abstraction with control maturity. Evaluate routing, observability, and identity enforcement as separate requirements before expanding usage.

Key takeaways

• LLM gateways are now identity control points, so governance has to be enforced where model requests enter the stack.
• Open-source routing alone does not solve authentication, logging, or policy gaps, which leaves production AI usage harder to govern.
• Teams should inventory gateway credentials, centralise policy checks, and treat request logging as an audit requirement rather than an optional feature.

Key terms

• LLM Gateway: An LLM gateway is the control layer that mediates requests between users, applications, and model providers. In secure environments it does more than route traffic. It can enforce authentication, policy, logging, and credential handling so model access remains governable across providers and workloads.
• Identity-aware proxy: An identity-aware proxy is an access layer that makes authorization decisions before traffic reaches the target service. For LLM stacks, it can bind requests to user identity, apply contextual policy, and create audit evidence, which is often more reliable than scattering checks through application code.
• Non-human identity: A non-human identity is any credentialed machine actor such as a service account, API key, token, or certificate. In LLM environments, NHIs often sit behind the scenes and carry the real access risk because they can be reused, over-scoped, or left untracked long after the application changes.
• Access policy engine: An access policy engine evaluates rules about who or what can access a resource, under which conditions, and with what constraints. In AI infrastructure it matters because model requests are often high-volume and context-rich, making central enforcement more practical than manual review or scattered application logic.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.

This post draws on content published by Pomerium: LiteLLM Alternatives: Best Open-Source and Secure LLM Gateways in 2025. Read the original.