Why do agentic AI systems increase the risk of hidden blast radius and lateral impact?

Why This Matters for Security Teams

agentic ai increases blast radius because the control plane is no longer a single application. It is a chain of tool calls, identities, permissions, and data handoffs that can be reused, redirected, or abused once an agent is given execution authority. That makes the real risk less about one model response and more about what the agent can reach if it is prompted, tricked, or compromised. Current guidance from the OWASP Agentic AI Top 10 and NHI research such as AI Agents: The New Attack Surface report both point to the same issue: autonomy expands the number of reachable assets faster than most governance models expand oversight.

The practical problem is hidden trust. An agent may appear to have one task, but it can chain APIs, retrieve secrets, write files, query records, and trigger downstream automations in ways that are not obvious from the initial request. That creates lateral impact even when the original entry point looks narrow. In practice, many security teams discover these paths only after an agent has already touched sensitive systems, rather than through intentional blast-radius analysis.

How It Works in Practice

The hidden blast radius of agentic systems comes from three mechanics working together: delegated authority, runtime tool chaining, and weak visibility across the full dependency path. A single agent can start with a harmless-looking instruction, then call retrieval tools, invoke internal services, pass outputs into another agent, and reuse credentials that were never meant to be persistent. That is why static IAM alone is usually insufficient for autonomous workloads. Role definitions are too coarse, and pre-approved access lists rarely reflect the agent’s actual runtime intent.

Security teams should think in terms of workload identity, not just user-like access. Standards such as NIST AI Risk Management Framework and the CSA MAESTRO agentic AI threat modeling framework both reinforce the need to identify where autonomy changes trust boundaries. In practice, that means:

• Issuing just-in-time, short-lived credentials per task instead of long-lived secrets.
• Binding tool access to the specific workload identity and runtime context.
• Evaluating policy at request time, not only during onboarding or provisioning.
• Logging every tool hop so investigators can reconstruct the full chain of impact.

This is where the NHI perspective matters. If an agent inherits a token, API key, or service account with broad access, lateral movement becomes much easier once a single prompt injection or compromised connector is introduced. NHIMG research on CoPhish OAuth Token Theft via Copilot Studio shows how quickly that trust can be redirected, while the OWASP NHI Top 10 frames the underlying identity and access risks. These controls tend to break down when agents are allowed to cross multiple business domains through shared connectors because the permission graph becomes broader than any single owner can see.

Common Variations and Edge Cases

Tighter agent controls often increase operational overhead, requiring organisations to balance containment against speed, cost, and developer friction. That tradeoff becomes sharper in multi-agent systems, where one orchestrator may spawn sub-agents, each with different tools, data scopes, and retention rules. There is no universal standard for this yet, but current guidance suggests that the more autonomous the system, the smaller and shorter-lived each permission should be.

Edge cases usually appear in environments with shared service accounts, inherited cloud roles, or broad data connectors. In those settings, a single compromised agent can create a hidden blast radius that crosses teams, subscriptions, or even environments. This is especially difficult when the organisation lacks full auditability of agent actions, a gap highlighted in AI Agents: The New Attack Surface report. The same issue is visible in broader industry analysis from NIST Cybersecurity Framework 2.0, which emphasizes mapping assets, dependencies, and protective controls across the environment.

The hardest cases are agents that can read from one system, decide in another, and act in a third. In those workflows, blast radius is not defined by the first tool call; it is defined by the full transitive path of trust. That is why the safest operating model is least privilege plus per-action evaluation, not broad standing access.

Related resources from NHI Mgmt Group

• Why do AI agents increase non-human identity risk in existing IAM programmes?
• When does just-in-time access reduce risk for agentic AI, and when does it fall short?
• How does the rise of AI identities impact traditional IAM systems?
• How should security teams limit the risk from AI agents that have access to production systems?