What is an AI agent security assessment?
An AI agent security assessment reviews how the agent receives instructions, accesses data, selects tools, authenticates, takes actions, records traces, and recovers from misuse or failure. It evaluates the entire application and operating process, not only the model provider.
Agents create a distinctive security boundary because untrusted content may influence a model that has access to trusted tools. The application must assume the model can misunderstand or be manipulated and enforce permissions independently.
What assets and data should the assessment inventory?
Document models, prompts, retrieval sources, vector stores, databases, connectors, service accounts, secrets, queues, logs, evaluation data, user interfaces, and deployment environments. Classify data at each step and identify where it is stored, processed, retained, and visible to operators or vendors.
Map trust boundaries between users, retrieved content, the model, tool execution, and third parties. Include indirect input such as documents, emails, websites, support tickets, and records that an attacker or unauthorized user could modify.
How should tool permissions be tested?
Every tool needs a narrow purpose, typed schema, server-side validation, authorization, rate and spend limits, and an auditable result. Test whether the agent can change identifiers, access another tenant, chain safe tools into an unsafe outcome, repeat an action, or continue after a permission error.
Separate proposing from executing. Require human approval for high-impact actions and display enough context for the reviewer to make a real decision. A generic confirmation button is weak if the user cannot see the target, data, or consequence.
How does a security review test prompt injection?
Test direct instructions from users and indirect instructions embedded in retrieved content. The goal is not to prove the model never follows malicious text; it is to prove that following it cannot cross enforced data and tool boundaries.
Use adversarial cases that attempt to reveal secrets, override system policy, call unauthorized tools, exfiltrate retrieved data, alter tool arguments, or conceal an action. Confirm that logs capture the attempt without retaining more sensitive content than necessary.
What should an AI agent security report deliver?
The report should include system and data-flow diagrams, asset inventory, threat scenarios, findings with evidence, risk ratings, recommended controls, responsible owners, remediation priorities, and retest criteria. Distinguish exploitable issues from defense-in-depth improvements.
Include operational findings: missing kill switches, unclear incident ownership, excessive trace access, stale credentials, unbounded spending, and absent regression evaluations can create serious risk even when application code has no conventional vulnerability.
Make security part of the agent lifecycle
Review architecture before production, test controls before increasing authority, and reassess when tools, models, data sources, or workflows change. Revoke unused credentials and add incident patterns to the evaluation suite.
ClawRevOps can use a War Room to map an agent's workflow and identify the security questions that must be answered before deployment. It is not a substitute for a formal audit or legal advice.
Book a War Room session to review the system boundary.