OWASP Agentic AI Top 10
Agentic AI changes the security conversation because it changes what AI systems can do.
A chatbot that answers questions from a knowledge base is one thing. An AI agent that can plan tasks, call tools, access internal systems, query data, write to applications, execute code, coordinate with other agents, and retain memory is something else entirely.
Many organizations spent 2024 and 2025 experimenting with LLM integrations, internal copilots, retrieval systems, AI assistants, and early agentic workflows. Some of that work remained safely contained. Some of it quietly expanded. Agents gained access to APIs, SaaS tools, databases, files, ticketing systems, CRM records, repositories, and operational workflows.
That is where the security risk changes. An AI agent is not just another user interface. It is a software actor with instructions, permissions, tools, context, and the ability to make choices inside a workflow.
The OWASP Foundation’s Top 10 for Agentic Applications 2026 gives security teams a practical way to think about that shift. Developed through collaboration with more than 100 industry experts, researchers, and practitioners, the framework identifies the most critical risks facing autonomous and agentic AI systems. It is not just a warning about future threats. It is a map of the problems organizations are already starting to face as agents move from pilot to production.
Why agentic AI changes the security equation
Traditional application security is built around familiar assumptions. Users authenticate. Applications enforce permissions. Services call known APIs. Logs show actions. Security teams monitor access patterns, vulnerabilities, network behavior, and suspicious activity.
Agentic AI complicates those assumptions.
Agents do not behave like normal users. They can interpret instructions, make intermediate plans, decide which tools to use, retrieve context, call systems repeatedly, and produce actions that appear legitimate because they use approved credentials and approved integrations.
Palo Alto Networks’ analysis of the OWASP release highlights the need for agent-centric inventory, comprehensive visibility, supply chain integrity across AI components, governance, guardrails, and active runtime control. That framing is useful because the security problem is not limited to prompt injection. It includes tools, identities, supply chains, memory, context, permissions, and runtime behavior.
In other words, securing an agent is not the same thing as securing a model. The model is only one part of the system.
The OWASP Top 10 for Agentic Applications
OWASP’s 2026 list identifies ten major risk categories for agentic systems:
- ASI01 — Agent Goal Hijack: attackers manipulate or redirect the agent’s objective, often through natural language, hidden instructions, or malicious context.
- ASI02 — Tool Misuse and Exploitation: agents use tools in unsafe, unintended, or exploitable ways.
- ASI03 — Agent Identity and Privilege Abuse: agents receive excessive permissions, inherit risky credentials, or operate without proper identity boundaries.
- ASI04 — Agentic Supply Chain Vulnerabilities: prompts, plugins, MCP servers, tools, datasets, orchestration logic, dependencies, or integrations are compromised.
- ASI05 — Unexpected Code Execution: agents trigger or generate unsafe executable behavior through tools, scripts, plugins, or connected systems.
- ASI06 — Memory and Context Poisoning: attackers manipulate what the agent remembers, retrieves, or uses as context.
- ASI07 — Insecure Inter-Agent Communication: agents communicate with each other without sufficient authentication, validation, or boundaries.
- ASI08 — Cascading Agent Failures: one agent’s failure, bad output, or unsafe action propagates through a larger agentic workflow.
- ASI09 — Human-Agent Trust Exploitation: users over-trust agent outputs, approvals, or recommendations in ways attackers can exploit.
- ASI10 — Rogue Agents: agents act outside intended scope, persist beyond authorization, or operate without adequate oversight.
The list matters because it reflects how agentic systems actually fail. The risks are behavioral, architectural, and operational. They are not only vulnerabilities in the traditional sense.
The identity problem nobody planned for
Security teams already manage non-human identities: service accounts, workloads, devices, applications, bots, automation scripts, and API keys. Agentic AI adds another layer.
An agent may operate with delegated authority. It may use a user’s credentials, a service account, a tool-specific token, or a platform-managed identity. It may access multiple systems in one workflow. It may decide which tool to use based on context. It may operate on behalf of different users at different times.
That creates a difficult question: what exactly is the agent allowed to do?
If the answer is “whatever the connected account can do,” the organization has a problem. Human permissions are often too broad for autonomous use. A user may have access to sensitive files, customer data, billing systems, admin settings, or source code because they need that access in specific contexts. Giving an agent the same breadth of access can create unnecessary risk.
Agent identities need to be governed deliberately. That means least privilege, scoped credentials, clear ownership, tool-level permissions, approval gates, logging, and the ability to revoke or rotate access quickly.
Goal hijacking is different from traditional compromise
Agent goal hijacking is one of the most important risks because it attacks the agent’s instructions rather than the underlying code.
An attacker may not need to exploit a software vulnerability in the usual sense. They may insert malicious instructions into a document, webpage, email, support ticket, repository issue, tool output, or other context the agent reads. If the agent cannot distinguish trusted instructions from untrusted content, it may redirect its behavior.
Auth0’s analysis of the OWASP list describes the first two categories, goal hijacking and tool misuse, as risks that arise because agents process natural language and may not reliably distinguish system instructions from malicious payloads hidden in content or tool output.
That is a different mental model for defenders. The attack surface includes text, context, tool responses, memory, retrieved documents, and workflows, not just exposed endpoints.
Tool access turns bad reasoning into real action
Agentic AI becomes more powerful when it can use tools. It also becomes more dangerous.
A model that gives a bad answer is a quality problem. An agent that gives a bad answer and then updates a customer record, opens a ticket, sends an email, changes a configuration, calls an API, or executes code is an operational risk.
Tool misuse can happen even without malicious intent. The agent may misunderstand a request. It may use a tool in the wrong order. It may act on stale data. It may call an API with the wrong parameters. It may fail to recognize that a human approval step is required.
The fix is not simply “better prompting.” Agents need tool boundaries. They need permission scopes. They need safe defaults. They need dry-run modes for high-impact actions. They need human approval for sensitive operations. They need monitoring that understands agent behavior, not just API traffic.
Memory and context create a new attack surface
Agent memory is useful because it allows systems to retain preferences, workflow state, prior decisions, and context across sessions. It also creates risk.
If memory can be poisoned, the agent may carry bad assumptions forward. If context retrieval is weak, the agent may use the wrong documents, outdated policies, or maliciously modified data. If users can influence shared memory without controls, one user’s input may affect another user’s experience.
Memory and retrieval systems should be treated as part of the security architecture. Organizations need to know what is stored, who can modify it, how long it is retained, how it is validated, and how poisoned or outdated context can be corrected.
Supply chain risk now includes prompts, tools, and MCP servers
Software supply chain security already includes dependencies, packages, containers, build systems, APIs, and vendor services. Agentic AI expands the supply chain.
Prompts, tool definitions, orchestration scripts, RAG datasets, plugins, MCP servers, external tools, vector stores, and model providers all become part of the agentic system. A compromised tool or poisoned dataset can change agent behavior just as surely as a compromised package can change application behavior.
Palo Alto Networks emphasizes supply chain integrity across prompts, plugins, RAG datasets, orchestration scripts, and model dependencies. That is the right lens. Security teams need to treat agentic components as production dependencies, not as informal configuration.
Compliance cannot be an afterthought
Agentic AI expands compliance exposure because agents may touch sensitive data, influence decisions, create records, communicate externally, or take actions inside regulated workflows.
The compliance question is not only whether the model is approved. It is whether the entire agentic workflow can be explained, monitored, audited, and controlled.
Organizations need to answer:
- Which agents are deployed?
- Who owns each agent?
- What data can each agent access?
- What tools can each agent use?
- What actions require human approval?
- How are prompts, tools, and configurations versioned?
- How are outputs logged?
- How are incidents detected and escalated?
- How are agents retired or decommissioned?
If those questions cannot be answered, the organization does not have an agentic AI governance program. It has agentic AI usage.
What security teams should do now
The right response is not to ban every agentic system. It is to create a security model that matches the risk.
- Build an agent inventory: Identify agents, copilots, automations, plugins, MCP servers, tool-calling workflows, and vendor AI features already in use.
- Map permissions and tools: Document which systems each agent can access, which credentials it uses, and what actions it can take.
- Apply least privilege: Do not let agents inherit broad human permissions by default. Scope access to the task.
- Separate trusted instructions from untrusted content: Treat documents, webpages, emails, tickets, and tool outputs as potentially hostile input.
- Gate high-impact actions: Require approval, review, or dry-run modes for sensitive operations.
- Secure memory and retrieval: Control what agents can remember, retrieve, modify, and share across users or sessions.
- Monitor runtime behavior: Log tool calls, permission use, data access, outputs, errors, and unusual action patterns.
- Red-team agentic workflows: Test for goal hijacking, tool misuse, privilege abuse, context poisoning, unsafe code execution, and cascading failures.
These controls should be designed into the agentic architecture before deployment. Adding them later is harder, especially after users begin depending on the system.
How Ridiculous Engineering thinks about agentic AI security
At Ridiculous Engineering, we see agentic AI security as an architecture problem, not just a prompt-hardening problem. Prompting matters, but agents become risky because they are connected to tools, identities, data, memory, APIs, workflows, and people.
That means agent security needs to be designed across the full system. What can the agent access? What can it change? What context can influence it? What is logged? What requires review? Who owns the agent after deployment? How does the organization detect unsafe behavior before it becomes a business incident?
We help clients approach those questions practically. That may mean inventorying agentic workflows, mapping permissions and data access, reviewing tool integrations, designing governance gates, building monitoring, creating safer deployment patterns, or helping teams move from experimental agents to production systems with clearer controls.
The goal is not to slow innovation for its own sake. The goal is to make agentic AI useful without letting autonomy outrun accountability.
The bottom line
Agentic AI is not a future security problem. It is a present architecture problem.
The OWASP Top 10 for Agentic Applications, Palo Alto Networks’ security guidance, Auth0’s analysis of goal hijacking and tool misuse, and the broader security community are all pointing in the same direction: organizations need visibility, identity control, runtime guardrails, tool governance, memory protection, and human oversight for AI agents.
You cannot bolt all of that onto a fragile architecture at the end and expect it to hold.
If your organization is experimenting with AI agents, deploying agentic workflows, or trying to understand whether your security model is ready for autonomous systems, Ridiculous Engineering can help. We work with teams to evaluate the architecture, identify agentic risk, and design controls that let AI systems operate with the right boundaries.
Agentic AI can be powerful. It also needs to be governed like something powerful.
Sources and further reading: OWASP: Top 10 for Agentic Applications 2026, OWASP GenAI Security Project: Top 10 risks and mitigations for agentic AI security, Palo Alto Networks: OWASP Top 10 for Agentic Applications 2026, Auth0: Lessons from OWASP Top 10 for Agentic Applications, DeepTeam: OWASP Top 10 for Agents 2026, Teleport: OWASP Top 10 for Agentic Applications