Why do AI agents create more runtime risk than single LLM calls?

Why This Matters for Security Teams

AI agents create more runtime risk because they are not one-shot inference events. They persist across steps, keep context, chain tools, and make decisions that can move beyond the original prompt. That means a single injection, bad retrieval, or compromised secret can influence multiple actions instead of one output. NHI Management Group’s research on OWASP NHI Top 10 shows why agentic systems need runtime controls, not just model safety checks.

This is where traditional IAM assumptions break down. Static roles and long-lived permissions fit predictable human workflows, but agents behave opportunistically: they may call an MCP server, fetch data, generate new tool plans, or retry after failure. The result is a wider blast radius than a single LLM call, especially when secrets, API keys, and workflow credentials are reused across tasks. Current guidance from the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10 is converging on runtime governance, but there is no universal standard for this yet.

In practice, many security teams discover agent runtime risk only after an agent has already taken an out-of-scope action or exposed data beyond its intended task boundary.

How It Works in Practice

Managing agent runtime risk starts with treating the agent as an autonomous workload, not a conversational session. The core identity should be the workload identity, not the user session behind it. That means using short-lived, task-bound credentials, preferably issued just in time and revoked immediately when the task ends. Where possible, use cryptographic workload identity such as SPIFFE-style identity or OIDC-backed tokens so the system can prove what the agent is at runtime, not just what it was allowed to do earlier.

Authorisation should also move from static role checks to context-aware, request-time decisions. In practice, that means policy-as-code evaluated on every tool call, retrieval request, or downstream API action. The policy engine should consider the agent’s current task, requested tool, target data class, confidence of the route, and whether the action is still in scope. This is the practical difference between model output filtering and full execution-chain control. The CSA MAESTRO agentic AI threat modeling framework and MITRE ATLAS adversarial AI threat matrix both reinforce that the risk is not the prompt alone, but the sequence of actions that follows.

NHI Management Group’s AI Agents: The New Attack Surface report is consistent with this runtime view, showing that organisations are already seeing scope creep, inappropriate data access, and credential exposure in active deployments. A useful control pattern is to combine per-step approvals for sensitive actions, dynamic secret issuance, and continuous logging of tool usage so investigators can reconstruct the full chain of behaviour. These controls tend to break down when agents are allowed to execute long-running multi-tool workflows without step-level policy evaluation, because the system loses the ability to stop misuse before it compounds.

Common Variations and Edge Cases

Tighter runtime control often increases latency and operational overhead, so organisations have to balance speed against containment. That tradeoff is most visible in high-volume agent workflows, where every extra policy check or approval can slow execution and frustrate users. Current guidance suggests starting with the highest-risk actions first: data export, authentication changes, secret retrieval, and external tool invocation.

One common edge case is the trusted internal agent that still uses untrusted inputs. Prompt injection can arrive through emails, documents, tickets, web pages, or MCP-connected tools, so “inside the perimeter” is not a meaningful safety boundary. Another is multi-agent orchestration, where one agent delegates to another and passes context that includes hidden instructions or stale credentials. In these cases, static RBAC is too coarse because the agent’s intent changes from step to step, while the permission model does not. The emerging best practice is evolving toward intent-based authorisation, but there is no universal standard for this yet.

For deeper examples of real-world agent compromise patterns, see AI LLM hijack breach and CoPhish OAuth Token Theft via Copilot Studio. These cases show why security teams should expect lateral movement through tools, not just unsafe text output.

Related resources from NHI Mgmt Group

• Why do AI agents create more identity risk than traditional LLM applications?
• Why do AI agents and multi-provider LLM setups create new governance risk?
• Why do autonomous AI agents create more cost risk than ordinary model calls?
• Why do direct integrations to a single LLM provider create reliability risk in enterprise AI systems?