AI Agent Security: Risks Beyond Prompt Injection
Prompt injection is the most visible AI security issue, but it is only one part of the risk. The more important question is what happens after an AI system is influenced. If the agent can call tools, query customer data, send messages, update records, trigger automations, or make decisions, the assessment needs to cover the full workflow around the model.
AI agent security is application security plus workflow security plus authorization testing. The model is one component. The surrounding product architecture decides what the model can reach and what damage a manipulated workflow can cause.
Map what the agent can access
Start by documenting the tools, APIs, files, databases, retrieval sources, and external services available to the AI workflow. Then map which users, tenants, or roles can trigger those capabilities.
This access map matters because AI risks often emerge from excessive permissions. An agent that summarizes support tickets has different risk than an agent that can issue refunds, change account settings, query production data, or send customer-facing messages. The test should validate whether the agent can be pushed outside intended business boundaries.
Test indirect prompt injection and tool abuse
Indirect prompt injection happens when malicious or untrusted content enters the context through documents, webpages, tickets, emails, uploaded files, or retrieved knowledge. The attacker does not need to directly prompt the model if the model consumes attacker-controlled content from another source.
Tool abuse testing looks at whether the AI workflow can be manipulated into calling tools in unsafe sequences. This includes unauthorized data retrieval, cross-tenant exposure, unsafe automation, excessive action execution, and bypassing approval steps. A good assessment validates abuse paths rather than only producing clever prompt examples.
Review authorization outside the model
The product should enforce permissions outside the model. The model should not be the primary security boundary. If a user cannot access a record through the normal application, the AI workflow should not be able to expose it through summarization, retrieval, tool calls, logs, or generated output.
Testing should cover object-level authorization, tenant isolation, scoped API tokens, tool-specific permissions, audit logs, and human approval gates. It should also check whether sensitive context is retained, exposed, or reused in ways the product team did not intend.
Produce evidence product teams can act on
AI security findings need to be reproducible. Include the interaction path, relevant prompt or input, affected workflow, tool call, data exposure, business impact, and remediation guidance. Product teams need enough detail to improve guardrails without blocking useful AI functionality.
CyberImmune assesses AI-enabled products, LLM workflows, and agentic systems as part of modern security services. If your product is adding AI workflows, Talk to a Security Lead before those workflows become part of an enterprise review.