Why do AI agents and tool-connected LLMs need runtime controls as well as testing?

Why This Matters for Security Teams

Testing tells a team whether an agent can be tricked; runtime controls determine whether that trick becomes a tool call, a data leak, or an unauthorized change. That distinction matters because agents do not just answer questions. They execute tasks, chain actions, and can carry forward compromised instructions into connected systems.

NHI Management Group research on OWASP NHI Top 10 shows how quickly agentic risk turns operational: in SailPoint’s AI Agents: The New Attack Surface report, 80% of organisations said their AI agents had already acted beyond intended scope. That is why pre-deployment testing alone is not enough. Even strong red-teaming cannot predict every prompt injection, tool abuse path, or delegation failure once the system is live and interacting with real data, users, and services.

Current guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework is converging on a simple point: assurance must exist at design time and at decision time. In practice, many security teams encounter agent misuse only after a connected tool has already been used to move data or trigger side effects, rather than through intentional testing.

How It Works in Practice

Runtime controls should be treated as the enforcement layer that sits between model output and external action. Testing can identify prompt injection exposure, tool misuse, or weak delegation logic, but it cannot guarantee safe behaviour once the agent is live. The practical answer is to combine pre-deployment evaluation with request-time policy checks, short-lived credentials, and explicit tool authorization.

For agentic systems, the most useful control model is dynamic rather than static. That means the agent presents workload identity, the platform evaluates context at the moment of action, and the system issues only the minimum authority needed for that task. Standards and research from CSA MAESTRO agentic AI threat modeling framework and NIST AI Risk Management Framework both support this shift toward runtime governance.

• Use ephemeral, per-task credentials so a successful injection has a short blast radius.
• Evaluate policy at runtime for each tool call, not only at deployment or code review.
• Restrict tool scopes so the agent can read, write, or execute only where the current task requires it.
• Log every delegated action with prompt context, tool target, and approval path for investigation.
• Revoke tokens automatically when the task ends, the context changes, or the agent deviates from plan.

This aligns with the threat reality described in LLMjacking: How Attackers Hijack AI Using Compromised NHIs, where credential exposure can be exploited in minutes. Runtime controls matter because agentic workloads inherit the reach of every connected secret, API, and delegated permission they can touch. These controls tend to break down in legacy environments where the agent is wired into broad service accounts, long-lived keys, or unmanaged SaaS connectors because there is no clean place to enforce request-time policy.

Common Variations and Edge Cases

Tighter runtime controls often increase latency, operational overhead, and false blocks, requiring organisations to balance safety against user experience and automation speed. That tradeoff is real, especially when an agent sits inside a customer-facing workflow or a fast-moving developer pipeline.

There is no universal standard for this yet, but current guidance suggests a layered model. High-risk actions such as payments, deletions, privilege escalation, and external sharing should require stronger runtime checks than low-risk retrieval. For some environments, human approval remains necessary; for others, policy-as-code with step-up verification is enough. The right threshold depends on the sensitivity of the tool and the reversibility of the action.

One common edge case is long-running agent sessions. If a workflow spans hours, a token that was safe at the start may become unsafe once context changes, tasks are merged, or an attacker injects new instructions mid-flight. Another is multi-agent systems, where one compromised agent can pass unsafe state to another unless each hop is independently validated. NHI Management Group’s AI LLM hijack breach coverage and the broader OmniGPT breach reporting show why perimeter assumptions fail when agent output can directly trigger downstream action.

Where organisations rely on static RBAC alone, runtime controls become the gap-filler, not an optional enhancement. That is especially true for autonomous agents, because the security question is not just who the agent is, but what it is trying to do right now.

Related resources from NHI Mgmt Group

• When should organizations consider adopting advanced tool discovery for AI agents?
• Why do traditional VPN and OAuth controls fall short for AI agents?
• When is it crucial to implement least-privilege access for AI agents?
• What is the difference between managed identities and hardcoded secrets for AI agents?