What should teams do first when malicious LLMs start appearing in their threat model?

Where malicious LLMs change the control problem first

The first shift is not “treat the model as malicious” in the abstract, but “treat its output as an untrusted input to high-trust workflows.” That means looking for places where generated text can trigger action, approval, reset, transfer, escalation, or disclosure. The practical question is where a convincing message can advance a business or identity workflow before a person or control has a chance to verify it.

In practice, the highest-value starting points are human-facing decisions with real side effects: email handling, credential reset prompts, support desks, payment approvals, and any workflow that turns language into authority. If those steps depend on tone, urgency, or apparent legitimacy, an LLM can become an influence channel even when it has no direct system access.

Which workflows deserve the first hardening pass?

Start with the workflows where a generated message can create trust, then map the exact handoff that moves the process forward. The most common failure pattern is not a technical exploit at the model layer, it is a workflow that allows “sounds right” to stand in for verification. A team should ask where the message changes state, who can approve that change, and what proof is required before the action proceeds.

• Email and chat flows, especially where a message can request a reset, exception, or urgent exception handling.
• Credential and account recovery, where social proof can bypass stronger verification.
• Support and service-desk processes, where operators may rely on the model’s confidence or formatting instead of independent checks.
• Payment, procurement, and finance approvals, where language quality can be mistaken for legitimacy.

Once those workflows are identified, tighten the decision point, not just the content filter. Controls should force the workflow to verify identity, intent, and authorization separately from the message itself. The useful design principle is simple: a convincing message should never be enough on its own to move an identity-related workflow forward.

What controls matter at the decision point?

The right controls are the ones that break the chain from persuasive output to privileged action. That usually means adding step-up verification, out-of-band confirmation, scoped approvals, and explicit human review for actions with material impact. Where possible, remove free-form text from the approval path and replace it with structured requests that can be checked against known identity, account, or transaction state.

For teams running agentic or tool-using systems, the same principle applies to delegated actions. The model can suggest, draft, summarize, or triage, but the authorization boundary must remain outside the generated content. A good control is one that makes the system prove the action is permitted before it can execute, rather than trusting the message that asked for it. Guidance in the Agentic AI Security Guide and Enterprise AI Copilot Security Guide is useful here because both focus on constraining inputs, connectors, and decision pathways before they become a control bypass.

For teams dealing with secrets, recovery, or cloud access, tighten the places where a message can expose or re-issue sensitive material. The LLMjacking guide and AI Infrastructure Workload Identity Guide both reinforce the need to separate conversational trust from the identities and credentials that actually authorize action.

Practitioner Guidance

What to prioritise: Harden the exact points where language becomes authority. If a workflow can approve, reset, pay, or expose something valuable based on a message alone, that workflow is the first place to redesign.

What to verify: Require a second factor of proof that is independent of the generated content, such as verified caller context, authenticated session state, transaction metadata, or an approval channel that the LLM cannot spoof.

Common mistake: Teams often focus on whether the LLM “says the right thing” and miss the real control issue, which is whether the surrounding process trusts it too quickly.

Practitioner takeaway: The first job is to reduce the model’s ability to impersonate legitimacy inside business processes, then make sure any high-impact workflow needs proof that a message cannot fabricate.

For a broader threat-modeling view, the MITRE ATLAS adversarial AI threat matrix and CSA MAESTRO agentic AI threat modeling framework help teams map where prompt influence, tool misuse, and workflow trust boundaries can be abused.

Related resources from NHI Mgmt Group

• How should security teams use LLMs to triage cloud security alerts without overtrusting the model’s first answer?
• How should data science teams use pre-trained LLMs to detect anomalies in tabular data without building a custom model first?
• What should security teams do first when bomb threat extortion emails start reaching employees directly?
• How should security teams prioritise NHI remediation in cloud environments?