Manual authentication increases the chance that credentials are copied into code, embedded in scripts, or spread across teams without consistent controls. That makes rotation harder, weakens audit trails, and creates a larger breach surface. It also separates access decisions from policy, so security teams lose visibility into who is calling which model and for what purpose.
Why Manual LLM Authentication Breaks Operational Control
Manual authentication turns access into scattered local decisions instead of a governed control point. Once developers paste credentials into code, scripts, notebooks, or ad hoc config, the organisation loses a reliable way to see where those credentials live, who can reuse them, and whether the calling context still matches policy. That is why the problem is not just convenience, it is control drift.
It also creates a hidden dependency on individual developer habits. A manual approach may work in a small prototype, but it becomes brittle as teams copy patterns between repositories, environments, and automation jobs. The more places a secret or token is handled manually, the harder it becomes to prove ownership, enforce rotation, and separate test access from production access.
That failure mode is common in secret sprawl: once credentials are embedded in multiple locations, one compromised file or leaked repo can expose a wider set of systems than the original developer intended. NHIMG’s Ultimate Guide to NHIs notes that 96% of organisations store secrets outside secrets managers in vulnerable locations, which is exactly the kind of pattern manual handling encourages.
Why Rotation, Auditability, and Purpose Boundaries Slip
Manual authentication usually weakens the three controls that matter most for model access: rotation, auditability, and purpose limitation. If the same token is reused across scripts or copied into team-owned files, rotation becomes a coordination problem instead of a normal control operation. If access is embedded in code, audit logs often show the application or script, but not a clean policy decision explaining why the model was called.
That loss of clarity matters because LLM access is not just “can connect or not.” Teams need to know which model was called, from which workflow, under which environment, and for what purpose. When those decisions sit in local code rather than policy, security review becomes reactive, and incident response loses the trail needed to distinguish normal automation from misuse.
A useful comparison point is the broader identity lifecycle problem: NHIMG’s LiteLLM PyPI package breach and DeepSeek breach both illustrate how exposed keys and secrets quickly become a platform-wide issue once they are reachable outside a controlled access flow.
What Good Looks Like Instead
Practitioners should treat manual authentication as a transitional state, not an operating model. The better pattern is to centralise model access decisions, keep credentials out of source code, and make every call path visible enough that security can answer who, what, and why without reverse engineering a script. Where possible, use short-lived credentials, scoped access, and a single policy layer that developers consume rather than reimplement.
What to verify: confirm that LLM access is issued through a managed control point, that no long-lived tokens are embedded in repositories or shared scripts, and that revocation can happen without editing application logic. If the security team cannot trace access by workload, environment, and purpose, manual handling is already too loose.
Common mistake: treating manual authentication as harmless in non-production or early-stage tooling. Those are usually the exact places where credentials first spread, get reused, and later become difficult to inventory or revoke.
Practitioner takeaway: The real breakage is not authentication failure, it is governance failure, because manual handling turns access into scattered implementation detail instead of a controllable security boundary.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, CIS Controls v8, NIST SP 800-63 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 — Secrets and Credential Management | Manual LLM auth spreads credentials into code and scripts, creating secret sprawl. |
| NHI-02 — Identity Lifecycle and Rotation | Manual access makes rotation and revocation harder across copied tokens and scripts. | |
| NHI-05 — Least Privilege and Access Governance | LLM calls need scoped, policy-backed access instead of ad hoc developer decisions. | |
| Recommendation — Keep model credentials out of code and centralise them in managed secret storage. Enforce short-lived credentials and automate rotation and revocation. Scope each model credential to the minimum permitted environment, model, and action. | ||
| NIST CSF 2.0 | PR.AC — Access Control | Manual authentication weakens controlled access enforcement and visibility. |
| Recommendation — Centralise access decisions so model use is enforced through policy, not local code. | ||
| CIS Controls v8 | 5.1 — Establish and Maintain an Inventory of Accounts | Manual tokens and scripts make it hard to inventory who or what can call the model. |
| 6.3 — Securely Manage Authentication Information | The core failure is insecure handling of authentication material in code and scripts. | |
| Recommendation — Inventory every model-facing account, token, and script owner. Store authentication material in approved secret managers and rotate it regularly. | ||
| NIST SP 800-63 | IAL — Identity Proofing | Model access should be tied to a trustworthy identity, not an ad hoc shared secret. |
| AAL — Authentication Assurance Level | Manual LLM auth often lacks the assurance needed for controlled access decisions. | |
| Recommendation — Bind access to verified identities and avoid shared credentials for model use. Raise authentication assurance for sensitive model access paths. | ||
| NIST Zero Trust (SP 800-207) | 3.1 — ZTA Principles | Manual auth bypasses continuous policy enforcement and explicit trust decisions. |
| Recommendation — Apply zero trust principles so each model request is explicitly authorised. | ||
Related resources from NHI Mgmt Group
- What breaks when passwordless authentication is adopted without considering user behavior?
- What breaks when infrastructure access is still managed manually in fast-moving DevOps environments?
- What breaks when identity certification campaigns are handled manually in healthcare?
- How should security teams handle risks from AI browser extensions?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 20, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
