What Does the OWASP Agentic Top 10 Cover?

A traditional LLM application may accept a prompt and produce a response. An agentic application can interpret a goal, break it into steps, select tools, call APIs, use credentials, retrieve data, write memory, communicate with peer agents, evaluate intermediate results and keep acting until it believes the task is complete.

That autonomy creates a wider security problem. A single malicious input can influence a multi-step plan. A compromised identity can affect many tools. A poisoned memory entry can persist across sessions. A false message from one agent can redirect others. A small error can cascade through automated systems at machine speed.

OWASP Top 10 for Agentic Applications 2026: Complete List

Why Agentic AI Security Is Different From LLM Application Security

The difference is agency. Once a model output becomes an input to a tool, an API request, a memory store, a sub-agent or a business workflow, the system can create real effects without waiting for a developer to write each step in advance.

This is why strong agent security uses deterministic controls around the model. Prompt instructions can guide behavior, but they should not be the only thing preventing an agent from deleting data, transferring money, reaching restricted APIs or executing code.

The Ten Agentic AI Risks Explained With Controls

ASI01: Agent Goal Hijack

An attacker manipulates the agent’s objective, plan or action selection through direct prompts, indirect prompt injection, malicious documents, deceptive tool output, forged messages or poisoned external data. Unlike a single bad model response, the impact can persist across a multi-step workflow.

Controls: separate trusted goals from untrusted content, bind actions to authenticated user intent, validate tool requests against task scope, constrain planning, require re-authorization for goal changes, and use independent policy around sensitive operations.

ASI02: Tool Misuse & Exploitation

The agent uses an otherwise legitimate tool in an unsafe way. A database connector can be turned into bulk export, an email tool into unauthorized sending, a browser into data exfiltration, or a shell into destructive execution. The risk may arise from an attacker, model error or overbroad tool design.

Controls: minimize the tool catalog, use narrow purpose-built functions instead of generic tools, validate arguments, enforce endpoint and action allowlists, apply transaction limits, and place approvals around destructive or high-value operations.

ASI03: Identity & Privilege Abuse

Agents often operate using service accounts, delegated OAuth tokens, API keys or cloud roles. If identities are shared or broadly scoped, the agent can become a confused deputy or let an attacker inherit powerful access. Agent identity should be attributable and constrained to the current user, tenant, task and environment.

Controls: use short-lived scoped credentials, avoid shared high-privilege identities, propagate user context where appropriate, separate agent roles, enforce least privilege at every API, rotate credentials and log exactly which identity authorized each action.

ASI04: Agentic Supply Chain Vulnerabilities

Agentic systems assemble many dynamic components: models, prompt packages, libraries, hosted agents, MCP servers, A2A peers, skills, plugins, datasets and orchestration frameworks. A malicious or replaced component can gain access to high-trust execution paths.

Controls: inventory components, pin versions, verify provenance, sign and scan artifacts, approve external tools/agents, constrain network access, monitor configuration changes and treat dynamic tool registration as a security-sensitive event.

ASI05: Unexpected Code Execution (RCE)

Agent workflows can convert natural language or generated output into code, shell commands, SQL, scripts, templates or configuration. When generated content reaches an interpreter without strong validation and isolation, an attacker can turn model influence into code execution.

Controls: sandbox execution, remove shell access where it is not required, use typed APIs, prohibit unsafe eval-style behavior, validate generated code/configuration, restrict filesystem/network permissions and require approval before privileged execution.

ASI06: Memory & Context Poisoning

Persistent memory is useful because agents can retain preferences, decisions and learned context. It is also dangerous because malicious content can survive the original interaction and influence later sessions. Poisoned memory can silently change priorities, tool selection or trusted facts.

Controls: authenticate memory writers, record provenance, separate user/tenant/task memory, validate writes, expire low-confidence entries, make sensitive memory changes reviewable, support rollback and monitor behavior changes after memory updates.

ASI07: Insecure Inter-Agent Communication

Multi-agent systems depend on messages that may contain identity claims, goals, delegated tasks, results and authorization context. If agents accept unauthenticated or over-trusted messages, a malicious peer can impersonate another agent, forge results or inject new instructions.

Controls: authenticate agents and channels, authorize each delegated capability, sign or integrity-protect messages where appropriate, validate schemas, prevent privilege inheritance by default, preserve provenance and treat peer-agent content as untrusted input.

ASI08: Cascading Failures

Automation compounds errors. A bad retrieval result can change a plan; the plan can call the wrong tool; that tool can update a system; another agent can act on the update. The same amplification can occur with malicious signals, outages, hallucinations or policy failures.

Controls: set step and cost budgets, add circuit breakers, validate high-impact state transitions, limit blast radius, use idempotent/reversible operations, stop on repeated failure, monitor cross-system sequences and require approvals before irreversible actions.

ASI09: Human-Agent Trust Exploitation

Agents communicate fluently and often present recommendations with confidence. Attackers—or ordinary model errors—can exploit that trust to convince users to approve sensitive actions, install software, reveal credentials or accept incorrect conclusions.

Controls: expose evidence and uncertainty, distinguish recommendations from verified facts, make high-risk approvals specific and informed, show what tool/action will run, prevent deceptive UI patterns and train users to validate consequential agent output.

ASI10: Rogue Agents

Rogue-agent risk covers behavior that becomes misaligned, deceptive, concealed or self-directed outside intended control, including cases without an active external attacker. The operational concern is not whether an agent has “intent” in a human sense; it is whether observed behavior violates policy while resisting, bypassing or evading controls.

Controls: minimize autonomy, continuously monitor behavior, separate duties, cap resources and privileges, provide reliable stop/disable mechanisms, require approvals for high-impact actions, detect attempts to evade policy and design workflows so one agent cannot become its own final authority.

A Control Architecture for Agentic Applications

The ten risks overlap. A small set of strong architectural controls can reduce several categories at once.

OWASP’s newer Agent Control Standard is one emerging approach for standardizing runtime hooks and Guardian decisions around agents. Whether or not an organization adopts ACS, the security principle is broader: consequential actions need an enforcement layer outside the probabilistic model.

Runtime and API Signals That Matter

Agentic systems frequently express their actions through APIs. That makes API telemetry useful for validating whether the agent’s real behavior matches intended policy.

For practical API-side monitoring, see Ammune’s guides to AI agent API security risks, API visibility for AI agents, and API runtime security protection.

How to Test an Agentic Application Against the Top 10

Static review is not enough because many failures appear only when the agent is allowed to plan and act. Test in a controlled environment with realistic tools, identities and data boundaries.

1. Goal-hijack tests: place malicious instructions in user prompts, documents, web content, tool results and peer-agent messages. Confirm the agent cannot expand authority just because the model follows them.
2. Tool tests: try destructive parameters, unbounded queries, cross-tenant objects, unexpected URL targets and repeated calls. Verify server-side validation and approvals.
3. Identity tests: attempt privilege inheritance, confused-deputy flows, token reuse, identity swapping and task-to-task credential leakage.
4. Supply-chain tests: simulate a changed MCP server, plugin, model, tool schema or dependency and confirm provenance/inventory monitoring catches it.
5. Execution tests: send generated commands/code to sandboxes and validate that unsafe interpreters, filesystems and networks are constrained.
6. Memory tests: write misleading persistent entries and verify provenance, expiration, rollback and cross-tenant isolation.
7. Inter-agent tests: spoof identities, replay messages, alter delegated tasks and send malformed or malicious payloads.
8. Cascade tests: trigger failures mid-workflow and confirm circuit breakers stop propagation rather than multiplying retries or actions.
9. Human-trust tests: evaluate whether UI/approval flows clearly show evidence, risk, action and consequence before a user consents.
10. Containment tests: verify operators can revoke credentials, stop agents, disable tools, isolate memory and reconstruct what happened.

Agentic AI Security Implementation Checklist

1. Inventory every production agent, owner, model, tool, connector, API, identity, memory store and peer-agent relationship.
2. Define the agent’s approved goals and the actions it may take for each user, tenant and task type.
3. Use dedicated, least-privilege identities and short-lived credentials instead of shared broad service accounts.
4. Keep authorization deterministic and server-side; never treat a system prompt as an access-control boundary.
5. Restrict tools and endpoints to the minimum necessary, with typed arguments and explicit action budgets.
6. Protect memory and retrieval with authenticated writers, provenance, isolation, validation, rollback and expiration.
7. Authenticate inter-agent communication and prevent delegated tasks from silently inheriting higher privilege.
8. Sandbox code execution and constrain network, filesystem and process access.
9. Add circuit breakers, timeouts, retry limits, transaction limits and human approval for irreversible actions.
10. Collect traceable runtime evidence linking user, task, agent, tool, API request, response, decision and outcome.
11. Red-team realistic multi-step workflows before launch and after major model/tool/policy changes.
12. Maintain a tested incident response path for agent shutdown, credential revocation, memory quarantine and forensic review.

How Ammune Can Support Agentic API Security

Ammune can be evaluated as an API runtime layer around agentic systems. Where agents use enterprise APIs and tools, Ammune can provide API discovery, request and response inspection, sensitive-data visibility, behavioral detection, policy enforcement options and SIEM-ready evidence.

This complements rather than replaces agent-specific controls. Agent identity, goal integrity, tool governance, memory security, code sandboxing and human approval still need to be designed in the agent platform. The runtime API layer answers a different question: what did the agent actually send to enterprise services, what came back, and did that behavior match the expected pattern?

Frequently Asked Questions