OWASP’s Q2 2026 landscapes split agentic AI from LLM security

At a glance

What this is: OWASP’s Q2 2026 update separates agentic AI, LLM and GenAI, and red teaming into distinct security landscapes.

Why it matters: That split matters because IAM, NHI, and AI governance teams now have to treat agent behaviour, tool access, and runtime scope as separate control problems rather than one model-security issue.

👉 Read Straikerai's analysis of OWASP's Q2 2026 agentic AI and red teaming landscapes

Context

Agentic AI security is becoming a distinct governance problem because systems that can call tools, persist memory, and coordinate across workflows no longer behave like chat interfaces. In those environments, the key question is not what the model can say, but what it can reach, change, or hand off. That makes the security boundary a runtime access problem as much as a model-risk problem.

OWASP’s decision to publish separate landscapes for agentic AI, LLM and GenAI applications, and red teaming signals that the field is moving away from one-size-fits-all AI security. For IAM, NHI, and AI governance teams, the intersection is clear: autonomous agents, MCP-connected tools, and delegated permissions create identity-like control needs that traditional prompt filtering does not address.

Key questions

Q: How should security teams govern AI agents that can choose tools at runtime?

A: Security teams should govern runtime agent choice as an access event, not as a simple application action. That means scoping permissions to the task, limiting token lifetime, logging every tool decision, and blocking the agent from reaching systems outside its approved context. Static roles alone are not enough when the execution path changes on each run.

Q: Why do agentic AI systems require different controls from LLM applications?

A: Agentic systems can act, not just respond. They coordinate with tools, persist context, and make sequential decisions, which means risk emerges at execution time rather than only at prompt input or output. Controls must therefore cover tool access, memory scope, delegation paths, and runtime monitoring, not just model filtering or evaluation.

Q: What breaks when red teaming is only done before release?

A: A pre-release-only approach misses the changes that happen after deployment, including tool updates, new integrations, memory changes, and behaviour drift. An agent that passed testing on day one may operate differently weeks later. Continuous red teaming is needed because the operational surface keeps moving after the release gate closes.

Q: How do IAM and NHI teams fit into AI gateway governance?

A: They should treat AI connectivity as part of the same control problem as workload identity and secrets management. The gateway becomes the enforcement point for access, audit, and policy, while IAM and NHI teams define the rules for who or what may call the models. Shared governance prevents AI sprawl from creating a second identity estate.

Technical breakdown

Why agentic AI needs a different security model

Agentic AI systems differ from LLM applications because they do not stop at generating text. They invoke tools, pass context to other agents, and make runtime decisions across multiple systems. That means the relevant control surface includes permissions, orchestration paths, memory scope, and downstream system access. Guardrails that only inspect prompts or outputs miss the actual security boundary, which is the sequence of actions an agent can take once it is trusted to execute. In practice, this creates a governance gap between model safety and operational privilege management.

Practical implication: map every agent to its reachable systems, tools, and delegated actions before treating it as production-ready.

How MCP and multi-agent orchestration change exposure

Multi-agent pipelines increase risk because authority can propagate across components that share context or delegate work. Protocols such as MCP make tool connectivity easier, but they also widen the trust boundary if the connected servers, tools, or downstream agents are not tightly scoped. A compromised instruction at one point in the chain can influence later steps without altering the underlying code. This is why orchestration security has to track reasoning lineage, tool invocation, and context handoff, not just the initial prompt.

Practical implication: inventory MCP servers and delegated agent paths, then restrict each one to the minimum callable scope.

Why continuous red teaming is now part of AI security

Red teaming an agentic system cannot be a one-time pre-release exercise because the test surface changes after deployment. Tools update, memory changes, new integrations appear, and downstream agents evolve outside the original testing cycle. That makes static evaluations incomplete for systems that keep adapting at runtime. Continuous red teaming is the response to a moving target: it tests behaviour over time, not just a fixed snapshot. The shift is from checking whether a model passed a lab test to checking whether an operational agent still behaves inside its intended boundaries.

Practical implication: build continuous adversarial testing into runtime monitoring, not just into release gates.

NHI Mgmt Group analysis

Agentic AI security is now a governance category, not a feature extension. OWASP’s separation of agentic AI from LLM security confirms that runtime agency changes the problem space. Once systems can call tools and coordinate actions, the core issue becomes delegated authority, not prompt quality. For identity programmes, that means agent behaviour has to be governed like access, with explicit scope and accountability.

Multi-agent orchestration creates a trust chain that traditional model controls do not see. When agents share context or delegate work through protocols such as MCP, one compromised component can influence the rest of the pipeline. That makes the trust relationship between agents, tools, and downstream systems the real control surface. Practitioner conclusion: map the chain of delegation before you assume model-level evaluation is enough.

Execution-time risk is the named concept this update sharpens. The article shows that many agentic failures emerge only when the system is actively calling tools, updating memory, or interacting with live services. Static review cannot observe those conditions. Practitioner conclusion: treat runtime monitoring and continuous red teaming as part of the same governance model.

AI security teams should expect identity and access controls to move closer to agent governance. The more an agent can act independently, the more its permissions resemble non-human identity management. That does not mean every agent is autonomous, but it does mean the control problem shifts toward issued scope, revocation, and detection of excess privilege. Practitioner conclusion: bring IAM and NHI governance into AI architecture reviews early.

OWASP’s split documents are a signal that category confusion is becoming a security risk. Teams that collapse LLM apps, agentic workflows, and red teaming into one programme will mis-size their controls. The practical outcome is a sharper architecture review process and a clearer investment split between model evaluation, orchestration security, and continuous testing.

What this signals

Agentic AI programmes now need a control model that combines access governance, runtime monitoring, and continuous testing. The operational mistake is to treat tool use as a feature flag when it is really delegated authority. That is why AI agents research matters: visibility and auditability are already lagging deployment velocity.

Execution-time governance debt: this is the gap that grows when teams build around static evaluations but deploy systems that can change behaviour after release. The control implication is to align AI review cadences with live system change, not with code promotion alone.

The practical signal for readers is that AI architecture reviews now need to include IAM, NHI, and platform security in the same conversation. That becomes even more important as agent footprints expand and as control frameworks such as the NIST AI Risk Management Framework are used to formalise accountability.

For practitioners

• Inventory agent reach and delegated scope Document every agent, tool, MCP server, and downstream system the workflow can touch. Treat that inventory as an access map, not a model register, and review it whenever a new integration or memory source is added.
• Separate LLM app controls from agentic controls Keep prompt and output guardrails for conversational use cases, but add runtime controls for tool invocation, context handoff, and behavioural drift where agents can act across systems.
• Move red teaming into continuous operations Run adversarial testing after deployment as well as before release, especially where tools, memory, or multi-agent delegation can change without a code change.
• Bring IAM and NHI governance into AI reviews Assign ownership for agent permissions, revocation, and exception handling in the same way you would for high-risk service accounts or other non-human identities.

Key takeaways

• OWASP’s split between agentic AI and LLM security confirms that autonomous behaviour creates a different governance problem from conversational AI.
• Runtime tool use, context sharing, and behavioural drift mean that static evaluation alone cannot prove an agent is safe in production.
• IAM and NHI teams should treat agent permissions, revocation, and monitoring as part of AI governance rather than a separate control domain.

Key terms

• Agentic AI: Autonomous AI systems capable of planning, deciding, and taking actions — including calling APIs, writing code, and orchestrating other agents — with minimal human oversight. Agentic AI introduces new NHI risks as agents must authenticate to external services.
• Execution-time risk: Execution-time risk is exposure that appears while a system is actively operating, not just during design or testing. For AI agents, that includes tool misuse, context leakage, and behavioural drift that only show up when the agent is interacting with live systems and data.
• Agent Orchestration: Agent orchestration is the coordination of multiple AI agents or workflows to complete a task set with limited human intervention. In identity terms, it creates delegated execution paths that need ownership, scope limits, and auditability because work is no longer performed only by a person in one session.
• Behavioural Drift: Behavioural drift is the gradual change in what an identity does compared with what it was originally approved to do. For AI agents, drift can come from prompt changes, model updates, expanded integrations, or altered workflows, which makes access review alone an incomplete control.

What's in the full article

Straikerai's full blog post covers the operational detail this post intentionally leaves for the source:

• Straikerai's breakdown of the three OWASP landscapes and how each maps to a different stage of the AI lifecycle
• Its discussion of discovery, adversarial simulation, and runtime enforcement across agentic workflows
• The article's explanation of why continuous red teaming has to follow changes in tools, memory, and downstream agents
• Straikerai's product-specific description of how its own controls map to the agentic security lifecycle

👉 Straikerai's full post covers the agentic AI control split, red teaming implications, and architecture review priorities

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and the access-control patterns that matter when systems act on their own. It is designed for practitioners who need to connect identity discipline to broader security operations and AI governance.