How should security teams scope LLM red teaming for customer-facing chatbots and action-taking agents?

Start with business impact, not attack novelty. Define the system, likely actors, assets in scope, and the commitments the model must never make. For agentic systems, inventory every tool, API, MCP server, and data store the agent can reach. Then map likely attack paths to model-specific and infrastructure-specific threats so testing stays tied to legal, operational, and regulatory exposure.

Scoping Red Teaming Around the Customer Journey and Business Commitments

Customer-facing chatbots should be scoped around the promises they can make, the data they can expose, and the transactions they can influence. For a chatbot, that usually means conversational safety, policy bypasses, leakage of customer data, and failure to route high-risk requests correctly. For an agent, the scope expands to action boundaries, approval paths, and whether the system can complete a harmful sequence end to end.

The practical test is whether a weakness can become a customer harm, a legal exposure, or an operational incident. That keeps the exercise tied to the real business system instead of isolated prompt tricks. It also helps separate harmless novelty from a failure that could create support burden, fraud exposure, privacy breach, or unauthorized action.

For customer-facing systems, define the boundary in terms of user roles, intended tasks, prohibited commitments, and sensitive outcomes. A red team target is not just “can the model be manipulated,” but “can it be induced to violate the business rules, data-handling rules, or transaction rules that govern the customer interaction.”

What to Inventory for Action-Taking Agents

Action-taking agents need a different scoping model because the security question is no longer only what the model says, but what it can do. Inventory every tool, API, connector, MCP server, credential path, and datastore the agent can reach, then identify which of those paths are read-only, write-capable, or destructive. The test should include both the direct tool call and the downstream effect of that call.

This inventory should also capture trust boundaries between the model, orchestration layer, and external systems. If the agent can send email, change records, create tickets, trigger code, approve refunds, or retrieve customer records, each of those capabilities deserves a separate red-team objective. The more privilege the agent has, the more the scope should emphasize authorization failure, overreach, and unsafe chaining of otherwise valid actions.

That is also where infrastructure matters. Weaknesses in secrets handling, environment isolation, token scope, and tool-level authorization can turn a model issue into a system-wide incident. For practical red teaming, the agent’s reachable surface is the scope, not just the prompt and completion channel.

How to Translate Attack Paths Into Test Cases

Red-team cases should map likely attack paths to the specific threats that matter for the environment. For chatbots, that includes prompt injection, data exfiltration, unsafe disclosure, impersonation, jailbreaks, and policy confusion. For agents, add tool misuse, privilege abuse, multi-step workflow abuse, data poisoning, and unintended execution paths that the model can reach through connectors or orchestration logic.

The best test cases follow the chain a real attacker would use: gain conversational control, influence the model’s plan, abuse reachable tools or data, then observe whether the system prevents or contains the impact. That means testing should be sequenced, not random. A red team that does not model the downstream action path may miss the point where a harmless-looking prompt becomes an unauthorized business operation.

Scoping should therefore distinguish model-specific threats from platform-specific threats. Some findings belong to prompt behavior, others to identity and access controls, and others to integration design. OWASP Non-Human Identity Top 10 is useful when the agent’s access model, secrets, and privilege shape the blast radius. For agentic attack paths and tool abuse, OWASP Agentic AI Top 10 and CSA MAESTRO agentic AI threat modeling framework both help keep tests grounded in autonomy, coordination, and tool use rather than isolated model behavior.

Risk and Threat Considerations

Red teaming becomes materially more important when the chatbot can influence customer decisions or when an agent can execute actions on connected systems. The risk is not just model failure, but business-process abuse, overprivileged access, and data exposure through a legitimate workflow.

Failure mechanism: An attacker or user can steer the model into bypassing policy, exposing sensitive content, or calling tools and APIs outside the intended business guardrails.

Impact: The result can be unauthorized transactions, customer data leakage, fraud enablement, broken auditability, or downstream compromise of connected systems.

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 AI RMF and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.

Framework	Control / Reference	Relevance
OWASP Non-Human Identity Top 10	NHI-05 — Overprivileged NHI	Agent reach and tool scope determine blast radius for customer-facing action-taking systems.
Recommendation — Limit agent credentials and tool access to the minimum needed for each approved action.
OWASP Agentic AI Top 10	ASI03 — Identity & Privilege Abuse	Red teaming must test whether an agent can be induced to exceed intended authority.
	ASI02 — Tool Misuse	Tool and connector abuse is central when the system can act on customer data or workflows.
Recommendation — Probe whether the agent can exceed its authorized identity or privilege boundaries. Test whether prompts can coerce the agent into unsafe or unintended tool use.
NIST AI RMF	GV.1 — Govern, Map, Measure, and Manage AI Risks	Scoping red teaming around business impact requires mapped AI risk and impact boundaries.
Recommendation — Define AI red-team scope by mapped business impact, not by prompt novelty.
NIST SP 800-53 Rev 5	IA-9 — Service Identification and Authentication	Agent and connector access depends on service-to-service authentication and trust boundaries.
	AC-6 — Least Privilege	Red-team scope should reflect whether the agent has excessive permissions across tools and data.
Recommendation — Apply service authentication controls to every agent-exposed integration path. Reduce agent permissions to the smallest set required for its task set.

Practitioner Guidance

What to prioritise: Start with the highest-impact customer journeys and the agent actions that can change state, move money, disclose data, or trigger external side effects. Those are the cases where a red-team finding has operational and legal consequence, not just model-hygiene value.

What to verify: Confirm that every scoped tool, connector, and MCP server has an owner, an access boundary, and a clear failure mode. If the team cannot state what should happen when the agent is tricked, the scope is still too vague.

Practitioner takeaway: The right scope is the smallest set of model behaviors and reachable actions that could still create real customer harm, because that is where red teaming produces decision-grade results.