OWASP Top 10 for Agentic AI: What Enterprise Security Teams Need to Test
Learn how to test AI agents against OWASP's Top 10 risks with practical scenarios for enterprise security teams.
When the Open Worldwide Application Security Project (OWASP) publishes a Top 10 list, enterprise security teams pay attention. For two decades, OWASP has served as the vendor-neutral authority on application security risks — and now their focus has expanded to include AI. With the OWASP GenAI Security Project addressing both large language models and agentic AI systems, security leaders have a standardized framework for testing autonomous AI before it goes into production.
The challenge? Most security teams know the list exists. Far fewer have operationalized it into systematic testing campaigns that cover every category. This gap becomes critical as organizations move from static AI models to autonomous agents capable of taking real-world actions across enterprise systems.
Why OWASP Matters for Agentic AI
OWASP’s credibility stems from its community-driven, open-source approach. With over 600 contributing security experts from 18+ countries, the Top 10 for LLM Applications represents hard-won consensus on what actually threatens AI deployments — not theoretical risks, but exploitable vulnerabilities that red teams and attackers actively target.
For CISOs and security architects, OWASP provides a defensible baseline. When auditors ask how you’re securing AI agents, pointing to systematic OWASP coverage demonstrates rigor. When boards ask about AI risk, the framework translates technical vulnerabilities into business impact.
The OWASP Top 10: Practical Enterprise Scenarios
Each OWASP risk category takes on new dimensions when AI systems move from generating text to executing actions. Here’s what each means in practice for organizations deploying autonomous agents.
1. Prompt Injection
What it means: Attackers manipulate AI behavior through crafted inputs — either directly through user prompts or indirectly through external content the agent processes.
Enterprise scenario: A procurement agent scanning vendor proposals encounters a document containing hidden instructions. The malicious text directs the agent to extract pricing data from other proposals and email it to an external address. The agent complies because it lacks mechanisms to distinguish legitimate document content from injected commands.
Why agents amplify the risk: When an agent can send emails, access databases, and execute transactions, prompt injection becomes a gateway to unauthorized actions — not just misleading outputs.
2. Sensitive Information Disclosure
What it means: AI systems reveal confidential data through their outputs, whether personal information, financial details, proprietary business data, or system architecture details.
Enterprise scenario: A customer service agent trained on support tickets inadvertently surfaces another customer’s account details when prompted with carefully worded questions. The agent doesn’t recognize that responding helpfully to “show me examples of resolved billing disputes” means exposing real customer data.
Why agents amplify the risk: Agents connected to enterprise data sources can access and correlate information across systems, making disclosure risks exponentially larger than single-model deployments.
3. Supply Chain Vulnerabilities
What it means: Compromised components — including pre-trained models, third-party plugins, and training datasets — introduce vulnerabilities before your team writes a single line of code.
Enterprise scenario: A development team deploys an open-source agent framework with a popular plugin for database connectivity. Unknown to them, a recent update to the plugin introduced a backdoor that logs all queries to an external server. The vulnerability exists outside their code review process.
Why agents amplify the risk: Agent architectures depend on extensive tooling — model providers, extension libraries, API connectors. Each dependency is an attack surface.
4. Data Poisoning
What it means: Manipulated training data, fine-tuning datasets, or embedding sources introduce vulnerabilities, backdoors, or biases that persist into production.
Enterprise scenario: An HR analytics agent fine-tuned on internal performance reviews produces consistently biased recommendations. Investigation reveals that historical data included systematically skewed evaluations, and the model learned to perpetuate those patterns in hiring and promotion suggestions.
Why agents amplify the risk: Poisoning attacks against agents can influence not just outputs but decisions — hiring, pricing, risk assessment — with downstream consequences across the organization.
5. Improper Output Handling
What it means: Insufficient validation of AI-generated content before passing it to downstream systems enables code injection, privilege escalation, and other exploits.
Enterprise scenario: A code generation agent produces database queries based on natural language requests. Without proper sanitization, the agent outputs a query containing SQL injection syntax that a developer unknowingly executes against production systems, resulting in data exfiltration.
Why agents amplify the risk: Agents often chain outputs directly into system calls, API requests, and automated workflows. Every unvalidated output is a potential injection point.
6. Excessive Agency
What it means: AI systems granted more permissions, functionality, or autonomy than necessary for their intended purpose can take damaging actions when manipulated or malfunctioning.
Enterprise scenario: An email assistant agent has permissions to read, compose, and send messages — but also inherited the service account’s ability to delete messages and modify mailbox rules. When compromised through prompt injection, the attacker uses these excessive permissions to establish persistent access and exfiltrate sensitive communications.
Why agents amplify the risk: This is the defining risk of agentic systems. Every additional capability becomes potential blast radius when something goes wrong.
7. System Prompt Leakage
What it means: Attackers extract the system prompts and instructions that govern AI behavior, revealing business logic, security controls, and exploitable constraints.
Enterprise scenario: A competitor discovers that a financial services firm’s advisory agent operates under specific compliance guardrails by methodically probing the agent and reconstructing its system prompt. They use this intelligence to design a competing product that mimics the firm’s proprietary advisory framework.
Why agents amplify the risk: System prompts for agents often contain operational procedures, tool configurations, and decision criteria that constitute competitive intelligence.
8. Vector and Embedding Vulnerabilities
What it means: Attacks targeting the retrieval mechanisms that ground AI responses in enterprise data — including poisoned embeddings, manipulated retrieval results, and unauthorized data access.
Enterprise scenario: An internal knowledge agent uses RAG (Retrieval Augmented Generation) to answer employee questions. An insider plants documents in the knowledge base containing carefully crafted content that, when retrieved, manipulates the agent into providing incorrect compliance guidance.
Why agents amplify the risk: RAG-enabled agents make decisions based on retrieved context. Poisoning that context means poisoning decisions.
9. Misinformation
What it means: AI systems produce false or misleading information that users or downstream systems act upon as fact.
Enterprise scenario: A research synthesis agent confidently cites a nonexistent regulatory requirement in a compliance recommendation. The operations team implements costly controls based on this hallucinated mandate before discovering the requirement doesn’t exist.
Why agents amplify the risk: When agents take autonomous action based on their own outputs, misinformation doesn’t just mislead humans — it directly causes incorrect decisions and transactions.
10. Unbounded Consumption
What it means: Lack of controls on AI resource usage enables denial-of-service attacks, cost explosion, and operational disruption.
Enterprise scenario: An attacker discovers that a customer-facing agent processes uploaded documents without size limits. They submit a series of massive files that consume all available GPU capacity, degrading service for legitimate users and generating unexpected cloud costs.
Why agents amplify the risk: Agents executing multi-step workflows can trigger cascading resource consumption across connected systems, amplifying the impact of consumption attacks.
The Testing Gap: From Awareness to Coverage
Security teams face a familiar pattern: awareness of a framework doesn’t translate to systematic testing against it. For OWASP’s AI categories, the gap is particularly acute. Traditional security testing tools weren’t designed for AI systems. Penetration testers may lack AI-specific expertise. And the rapid deployment pace of AI agents often outstrips security review capacity.
Closing this gap requires AI security capabilities built specifically for the threat landscape these systems face — not adapted from legacy application security tools.
What Changes When Models Become Agents
The fundamental shift from models to agents is the shift from generation to action. A language model that produces problematic output creates a PR issue. An agent that takes problematic action creates operational, financial, and legal consequences.
This distinction affects how security teams must prioritize OWASP categories. Prompt injection, excessive agency, and improper output handling become existential concerns when the AI can execute transactions, modify data, or interact with external systems. Testing must account for the full scope of agent capabilities — not just what the AI says, but what it can do.
Building Systematic Testing Campaigns
Effective AI security testing requires purpose-built tools aligned to recognized frameworks. Airia’s red teaming attack library is built against OWASP and MITRE frameworks, enabling security teams to run campaigns that systematically test coverage against every OWASP Agentic AI Top 10 risk category. Organizations can evaluate agents before deployment, validate controls after changes, and maintain continuous assurance as threats evolve.
The goal isn’t to check a compliance box — it’s to find vulnerabilities before attackers do. With unified AI governance, security teams can track testing coverage, classify risks, and enforce accountability across every agent in the enterprise.
See how Airia can help you take control and secure your entire AI ecosystem today.Connect with a member of our team****to get started.
Put these ideas to work.
Schedule a 30-minute walkthrough with our team.