LLM red team engagement flow

A structured engagement is easier to plan, repeat, and remediate when it follows a clear flow from scope to runtime control validation.

1. Scope

Define assets, users, data, tools, boundaries, and engagement rules.

2. Threat model

Map attack surfaces to risks such as prompt injection, excessive agency, and data exposure.

3. Test

Run controlled adversarial testing for LLMs across direct, indirect, and agentic attack paths.

4. Remediate

Translate findings into policy, permission, monitoring, and workflow controls.

Red teaming LLMs is not prompt hacking

LLM red teaming should not be treated as an informal exercise in prompt hacking. A practical methodology treats testing as a security engineering process that covers the model, orchestration, retrieval, memory, and tool layers.

The goal is to understand how the system behaves under adversarial, ambiguous, or unexpected instructions, then turn those findings into controls that reduce risk in operation.

Step-by-step methodology

  1. Step 1: Scope the engagement before testing

    Define the assets, users, data, tools, boundaries, and engagement rules before testing begins. This creates a shared view of what is in scope, which behaviors matter, and how findings should be evaluated.

  2. Step 2: Build the threat model and attack plan

    Map the attack surface to risks such as prompt injection, jailbreaks, tool abuse, excessive agency, and data exposure. The attack plan should cover the model behavior as well as the system components that surround it.

  3. Step 3: Execute controlled attack cycles

    Run controlled adversarial testing for LLMs across direct, indirect, and agentic attack paths. Testing should be repeatable enough to distinguish isolated model behavior from systemic weakness.

  4. Step 4: Map findings to runtime controls

    The most useful red team report does more than describe failures. It maps each exploited path to a practical control that can reduce residual risk in operation. This is where LLM red teaming connects to runtime governance and AI risk management.

    For agentic deployments, many high-risk findings involve the agent doing something it was technically allowed to do. The remediation is often not a better prompt. It is a tighter permission boundary, a narrower tool scope, an approval workflow, a runtime policy decision, or monitoring that detects anomalous tool use. Runtime controls help enforce the intended operating envelope even when the model receives adversarial or ambiguous instructions.

  5. Step 5: Evaluate, prioritize, and validate remediation

    Score findings by exploitability and impact, then validate remediations through runtime controls such as least-privilege permissions, tool approval workflows, policy enforcement, monitoring, and audit logging.

From red team finding to remediation path

The methodology is most useful when every finding can be traced to an operational control. The table below summarizes the relationship between the supplied testing areas and the types of controls used to reduce residual risk.

Testing area What the engagement examines Runtime control direction
Prompt injection and jailbreaks Whether adversarial or ambiguous instructions can move the system outside its intended operating envelope. Policy enforcement, monitoring, and validation that controls continue to work after remediation.
Tool abuse and excessive agency Whether an AI agent can call tools or execute multi-step tasks in ways that exceed the intended boundary. Least-privilege permissions, tighter permission boundaries, narrower tool scopes, and tool approval workflows.
Data exposure Whether the model, retrieval layer, memory, or orchestration layer can expose sensitive data. Access controls, runtime policy decisions, monitoring, and audit logging.
Agentic workflows Whether the agent can perform actions it was technically allowed to do, even when those actions create risk. Approval workflows, runtime governance, monitoring for anomalous tool use, and auditability.

Where Trussed AI fits

Trussed AI fits where red team findings need to become runtime governance. If AI agents can call tools, access sensitive data, or execute multi-step tasks, the results of red teaming should connect directly to permissions, policy enforcement, monitoring, and auditability.

  • Use least-privilege permissions to reduce what an agent is allowed to do.
  • Use tool approval workflows when a risky action should require explicit review.
  • Use policy enforcement to keep the system inside its intended operating envelope.
  • Use monitoring to detect anomalous tool use and unexpected behavior.
  • Use audit logging to support investigation, validation, and accountability.