OWASP Agentic AI is a security guidance area focused on risks created by autonomous AI agents that can plan, decide, and act with limited human oversight. It addresses how such systems connect to tools, data, and identities, including abuse of permissions, prompt injection, unsafe actions, and weak controls over NHI, IAM, and secrets.
What OWASP Agentic AI Covers
OWASP agentic ai is not a product name, but a security guidance area for autonomous systems that can take actions on their own. Its focus is the agentic attack surface, where planning, tool use, memory, permissions, and external data access can all become security-relevant.
This matters because risk changes once a model is allowed to do more than generate text. An agent that can call tools, read data, invoke APIs, or trigger workflows can also be manipulated into doing the wrong thing at machine speed, especially when trust boundaries are unclear.
Core Security Themes in Agentic AI
The central themes are autonomy, delegated authority, and control of actions. That includes prompt injection, tool misuse, unsafe tool chaining, identity and privilege abuse, memory poisoning, and failures in inter-agent trust. OWASP’s own OWASP Agentic AI Top 10 frames these as distinct risk classes rather than one broad “AI security” problem.
Agentic systems are also shaped by the surrounding control plane. Identity, authorization, secret handling, and policy enforcement become part of the security design because an agent’s capabilities are only as safe as the permissions, tokens, and approvals it can reach. That is why practical guidance for Agentic AI Identity Guide and AI Agent Authorisation Guide is tightly connected to the term.
Why Agentic AI Is a Distinct Security Problem
Traditional application security assumes a person or service initiates a request and stays inside a predictable workflow. Agentic AI can collapse that assumption by deciding its own next step, selecting tools, and carrying out actions across systems. That creates a larger blast radius when inputs are poisoned or when the agent is overtrusted.
The security problem is not just that the model may be wrong. It is that a wrong decision can now become an executed action, with side effects in data stores, SaaS tools, ticketing systems, source control, or infrastructure. A useful way to understand that shift is the spectrum covered in AI Agents vs Agentic AI, where autonomy level changes the risk profile.
How Organisations Assess and Govern Agentic AI
Organisations usually start by inventorying where agents exist, what data they can reach, what identities they use, and which tools they can invoke. From there, governance focuses on least privilege, action-level policy, approval thresholds, logging, and clear ownership for each agent and integration.
That governance model should include lifecycle controls, not just deployment-time review. Agents may need registration, revocation, credential rotation, and periodic reassessment as workflows change. For a deeper operational view, the Agentic AI Security Guide and AI Agent Observability, Audit and Incident Response Guide are the most directly useful references.
Risk and Threat Considerations
Agentic AI expands both the attack surface and the consequences of compromise. The main risk is that an attacker does not need full system control if they can influence the agent’s instructions, memory, inputs, or tool context, because the agent may execute harmful actions while appearing to operate normally.
Failure mechanism: Prompt injection, tool poisoning, credential exposure, or overbroad delegated access can turn a benign workflow into an attacker-controlled action path, especially when the agent can chain tools without strong policy checks.
Impact: The result can be unauthorized data access, fraudulent transactions, privilege abuse, lateral movement, unsafe code or infrastructure changes, and difficult-to-detect compromise of the agent’s standing trust relationships.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 addresses the attack and risk surface, while NIST AI RMF and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agentic AI often fails through overbroad delegated authority and misuse of access. |
| ASI02 — Tool Misuse | Agentic systems are defined by tool use, which attackers can redirect or poison. | |
| ASI06 — Memory & Context Poisoning | Agent memory and context can be manipulated to alter future decisions and actions. | |
| Recommendation — Apply ASI03 to scope every agent action to least privilege and per-action approval. Apply ASI02 to restrict tool invocation, validate inputs, and monitor tool chaining. Apply ASI06 to isolate memory, prevent untrusted writes, and inspect retained context. | ||
| NIST AI RMF | Govern map, measure, and manage AI risk | Agentic AI needs structured AI risk governance across design, deployment, and monitoring. |
| Recommendation — Use the AI RMF functions to govern, measure, and manage agentic AI risk continuously. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Agentic systems depend on secrets, tokens, and other authenticators used by agents and tools. |
| Recommendation — Apply IA-5 to control issuance, storage, rotation, and revocation of agent credentials. | ||
Practitioner Guidance
Why practitioners should care: Treat agentic AI as an execution system, not just a model interface. Once an agent can act, the security question becomes whether every action is narrowly scoped, observable, and reversible.
Common misunderstanding: Many teams assume human oversight automatically limits risk. In practice, oversight is weakest precisely when the agent is fast, repetitive, or deeply embedded in workflows, so the safer pattern is to constrain authority before deployment rather than rely on review after the fact.
Practitioner takeaway: If an agent can reach tools, data, or identities, it needs explicit boundaries, explicit logging, and explicit rules for when it must stop and ask.