OWASP Top 10 for Agentic Applications 2026: Why should businesses download it?
- Hung Pham
- 1 hour ago
- 6 min read
OWASP Top 10 for Agentic Applications 2026 is a reference document for AI systems that can plan, invoke tools, and autonomously execute multi-step tasks. Published by OWASP on December 9, 2025, it identifies 10 major risks and includes attack scenarios, mitigation guidance, and mapping tables for governance, secure development, and security testing.
AI agents are no longer limited to generating content like traditional chatbots. These systems can read emails, query databases, call APIs, execute code, send notifications, and coordinate with other agents to complete objectives.
This ability to act means that a manipulated instruction may lead to unauthorized transactions, data leakage, or changes to production systems. OWASP developed the Agentic Top 10 to help organizations move from general awareness of “AI risk” to specific controls for architecture, identity, tools, memory, and operations.
What does the OWASP Agentic Top 10 Document Cover?
The 57-page 2026 edition focuses on 10 major risk categories affecting Agentic AI applications. Each category includes a description, common vulnerability examples, attack scenarios, prevention measures, mitigation guidance, and references.
Code | Risk | Business Relevance |
ASI01 | Agent Goal Hijack | Attackers redirect an agent’s objectives or plans |
ASI02 | Tool Misuse and Exploitation | An agent uses legitimate tools in an unsafe manner |
ASI03 | Identity and Privilege Abuse | Identities, tokens, or delegated privileges are abused |
ASI04 | Agentic Supply Chain Vulnerabilities | Third-party tools, agents, models, or MCP servers are compromised |
ASI05 | Unexpected Code Execution | Untrusted content leads to unintended code execution |
ASI06 | Memory and Context Poisoning | Long-term memory or RAG data is persistently poisoned |
ASI07 | Insecure Inter-Agent Communication | Communication between agents lacks authentication and controls |
ASI08 | Cascading Failures | A single failure propagates across connected agents and systems |
ASI09 | Human-Agent Trust Exploitation | People place excessive trust in agent outputs or recommendations |
ASI10 | Rogue Agents | An agent operates outside its intended goals and controls |
The overview diagram on page 8 maps these risks across the full processing flow, from prompts, APIs, and external data to agents, memory, tools, human oversight, and outputs. This presentation makes one point clear: Agentic AI security cannot be addressed through prompt filtering alone.
Why should businesses download the full document instead of reading only the list of 10 risks?
The list of risk names helps organizations recognize the issues. However, the document’s real value lies in its detailed scenarios and mitigation measures, which can be converted into technical requirements, testing criteria, and governance policies.
1. It can be used as an AI Agent assessment checklist
Organizations can evaluate each AI application using questions such as:
What permissions does the agent have?
Which tools can it invoke?
What information can be written to memory?
Which actions require human approval?
Which activities are logged and monitored?
The document places particular emphasis on two principles: least privilege, which means granting only the minimum necessary permissions, and least agency, which means giving an agent only the level of autonomy genuinely required.
Autonomy that adds no business value but remains enabled still increases the attack surface.
2. It covers risks beyond prompt injection
Prompt injection remains important, but Agentic AI creates additional issues such as inherited privileges between agents, tool chaining, long-lived tokens, memory poisoning, forged agent cards, and malicious MCP tool descriptions.
For example, hidden content inside an email or document may cause an agent to invoke a tool that sends sensitive data outside the organization. In another scenario, a low-privilege agent may exploit internal trust to persuade a higher-privilege agent to perform a sensitive action.
3. It provides architecture-level mitigation measures
The recommendations go beyond simply telling users to “be more careful.” OWASP discusses controls such as:
Short-lived tokens
Separate identities for individual agents
Execution sandboxes
Outbound network allowlists
Per-action authentication
Tool usage budgets
Signed inter-agent messages
Emergency shutdown mechanisms for compromised supply-chain components
These measures help enterprises convert security principles into concrete architectural controls.
4. It is useful for multiple business functions
CISOs and risk managers can use the document to develop AI governance policies. Architects can apply it when designing data flows and permission models. Development teams can convert the recommendations into security requirements, while SOC and incident response teams can use the attack scenarios to create detection rules.
OWASP states that the document was developed with contributions from more than 100 experts, researchers, and practitioners. It is intended for developers, architects, security professionals, and decision-makers.
5. It can be adapted into internal documentation
The document is released under the Creative Commons CC BY-SA 4.0 license. Organizations may share, adapt, transform, and build upon the content, including for commercial purposes, provided that appropriate attribution is given and derivative works are distributed under the same license.
This makes it suitable for internal security standards, audit checklists, architecture guidelines, and training materials.
How should businesses use the document?
The document should be treated as a starting point for risk assessment, not as proof that a system is secure. The review should cover the entire workflow rather than focusing only on the language model.
30-day implementation checklist:
Create an inventory of AI agents in testing and production.
Document all connected tools, APIs, data repositories, and external agents.
Identify actions that can modify data, transfer money, send information, or execute code.
Map each system against the 10 ASI risk categories.
Remove unnecessary privileges, tokens, and tool access.
Require approval for destructive, publishing, or financial actions.
Separate memory by user, session, and tenant.
Log the goal, tool, parameters, identity, and result of each action.
Test indirect prompt injection through email, PDFs, websites, and RAG pipelines.
Establish mechanisms to stop agents, revoke tokens, and restore a safe state.
OWASP is also developing related materials, including guidance for securing Agentic applications, AIUC-1 mappings, and Agentic AI governance resources. This indicates that the Agentic Top 10 is designed as part of a broader security-control ecosystem rather than as a standalone checklist.
What does the IPSIP Expert perspective show?
Root Cause: The underlying weakness is that agents often process instructions, reference data, and external content within the same natural-language context. When this is combined with broad access rights and autonomous action, untrusted content may directly influence operational decisions.
Attack Vector: Attack paths may originate from email, uploaded files, websites, RAG data, MCP tool descriptions, shared memory, or messages exchanged between agents. Traditional network controls may not detect the activity when the agent uses legitimate APIs and valid credentials.
Business Impact: Potential consequences include data leakage, unauthorized transactions, deletion of production data, increased API costs, installation of malicious components, and disruption of interconnected business processes.
Lessons Learned: Vietnamese enterprises should treat an AI agent as a machine identity with the authority to act, not merely as a conversational interface. Permission management, behavioral monitoring, and workflow testing should be implemented before agents receive access to internal data or production systems.
What can businesses implement internally?
Organizations should prioritize:
Data classification
Least-privilege access
Short-lived token management
Approval for sensitive actions
Immutable audit logging
Testing of indirect input channels
Output validation before execution
Behavioral baselines for normal tool-call sequences
Technical teams should also prevent agent-generated content from being executed automatically without validation and policy enforcement.
Which IPSIP solutions are relevant?
Pentest is appropriate when an organization needs to determine whether prompt injection, tool chaining, or permission configurations can be exploited under realistic conditions. Pentesting differs from a vulnerability assessment because it validates exploitability and evaluates actual impact.
SOC 24/7 supports continuous monitoring for suspicious activity such as unusual API-call frequency, out-of-scope data access, or abnormal action sequences. This type of monitoring is especially important when AI agents operate outside normal working hours and interact with multiple systems.
Cloud Security is relevant to agents deployed on Amazon Web Services, Microsoft Azure, Google Cloud, or hybrid environments, where identities, logging, encryption, backup, and network configurations must be governed consistently.
OWASP Top 10 for Agentic Applications 2026 provides a common language that technology leaders, developers, and security teams can use to evaluate a rapidly evolving technology.
The document is particularly useful for organizations preparing to allow AI systems to access internal data, invoke APIs, or perform actions on behalf of users.
The priority is not to implement every control at once. Businesses should first inventory their agents, identify the associated permissions and data, and then assess each system against the relevant ASI categories.
Downloading the full document gives organizations the scenarios and practical guidance needed to move from general risk awareness to an actionable control plan.
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