OWASP Top 10 for LLM AI Agents
OWASP Top 10 for LLM AI Agents: The Security Risks Your Governance Framework Cannot Ignore
May 12, 2026 · iAgentsFlow
AI agents are no longer just answering questions.
They are taking actions, calling APIs, accessing enterprise systems, and making decisions that affect real customers and business operations.
As AI agents become more capable, the attack surface around them expands rapidly.
The OWASP Top 10 for LLM Applications provides the most important security framework enterprises should use to secure AI systems operating across platforms like Salesforce, ServiceNow, Workday, and enterprise automation environments.
Why AI Agents Create a Different Kind of Security Problem
Traditional applications operate with predictable inputs and outputs.
AI agents work differently.
They:
- Interpret natural language
- Process external content
- Make autonomous decisions
- Access enterprise systems dynamically
- Execute workflows across multiple environments
This creates a fundamentally new security model.
Attackers can manipulate AI systems not only through code exploits, but also through prompts hidden inside documents, emails, websites, and retrieved content.
The OWASP framework was created specifically to address these risks.
LLM01 — Prompt Injection
Prompt injection is currently the most exploited AI security vulnerability.
Attackers embed malicious instructions into prompts or external content to manipulate an AI agent’s behaviour.
Example
A customer support ticket contains hidden instructions telling the AI agent to ignore prior policies and send sensitive information externally.
The AI agent interprets the malicious prompt as legitimate instructions.
How iAgentsFlow Helps
- Runtime guardrails
- Prompt anomaly detection
- Policy enforcement
- Human-in-the-loop approvals
- Execution blocking for malicious actions
LLM02 — Sensitive Information Disclosure
AI agents can unintentionally expose:
- PII
- Financial data
- Healthcare records
- Enterprise secrets
- Security credentials
Example
An AI support assistant accidentally reveals another customer’s account information during a conversation.
How iAgentsFlow Helps
- Granular access policies
- Data governance controls
- Output scanning
- PII detection
- Sensitive data blocking
LLM03 — Supply Chain Vulnerabilities
AI systems inherit risks from:
- Open-source models
- External libraries
- Third-party APIs
- Compromised dependencies
Example
An enterprise deploys a third-party model containing hidden backdoors or malicious behaviour triggers.
How iAgentsFlow Helps
- AI component inventory tracking
- Supply chain monitoring
- Dependency visibility
- Vulnerability alerts
LLM04 — Data and Model Poisoning
Tampered training data or corrupted retrieval content can manipulate AI behaviour.
Example
An attacker inserts false information into an internal knowledge base used by the AI agent.
The AI begins making decisions based on poisoned content.
How iAgentsFlow Helps
- Data lineage tracking
- Retrieval monitoring
- Content integrity validation
- Observability controls
LLM05 — Improper Output Handling
AI-generated outputs can introduce malicious payloads into downstream systems.
Example
A manipulated AI response inserts SQL injection content into an automated workflow.
How iAgentsFlow Helps
- Output validation
- Injection detection
- Runtime policy enforcement
- Downstream execution controls
LLM06 — Excessive Agency
AI agents with excessive permissions become high-risk attack surfaces.
Example
An AI agent with broad system access deletes records or modifies configurations after being manipulated.
How iAgentsFlow Helps
- Least-privilege access policies
- Permission scoping
- Human approval workflows
- Risk-based execution controls
LLM07 — System Prompt Leakage
Attackers attempt to extract internal system prompts and governance instructions.
Example
An attacker tricks an AI agent into revealing hidden operational instructions.
How iAgentsFlow Helps
- Encrypted prompt storage
- Prompt disclosure prevention
- Runtime guardrails
- Prompt monitoring
LLM08 — Vector and Embedding Weaknesses
Manipulated vector databases can influence AI retrieval behavior.
Example
An attacker inserts adversarial embeddings that override legitimate enterprise knowledge.
How iAgentsFlow Helps
- Vector store governance
- Access controls
- Embedding monitoring
- Similarity anomaly detection
LLM09 — Misinformation
AI agents can generate incorrect but highly confident responses.
Example
An AI compliance assistant provides inaccurate regulatory guidance that results in legal exposure.
How iAgentsFlow Helps
- Chain-of-thought observability
- Human review workflows
- Source validation
- Governance controls for high-risk outputs
LLM10 — Unbounded Consumption
Uncontrolled AI agents can consume excessive API or compute resources.
Example
An attacker repeatedly triggers expensive AI workflows, increasing operational costs and degrading performance.
How iAgentsFlow Helps
- Spend management controls
- Rate limiting
- Budget caps
- Consumption anomaly detection
Why Governance and Security Are the Same Problem
Every OWASP AI security risk ultimately maps back to governance failures.
Without governance:
- Permissions become excessive
- Logging becomes incomplete
- Oversight disappears
- Runtime controls fail
AI governance and AI security cannot operate separately.
They are the same operational discipline.
The Connection to the EU AI Act
The EU AI Act requires high-risk AI systems to be:
- Accurate
- Robust
- Secure
- Resistant to malicious manipulation
This directly aligns with OWASP security principles.
The OWASP framework effectively becomes a practical technical roadmap for EU AI Act cybersecurity compliance.
Where to Start
1. Map Your AI Attack Surface
Inventory every AI agent, workflow, and connected system.
2. Apply Least-Privilege Access
Restrict permissions aggressively.
3. Add Human Oversight
Require approvals for consequential AI actions.
4. Build Observability First
Audit logs and runtime visibility are foundational.
Ready to Secure Your AI Agents?
iAgentsFlow provides the AI Governance Control Plane enterprises need to secure AI agents at scale.
From runtime guardrails to immutable audit logs, our platform operationalises AI governance and AI security across enterprise ecosystems.
Book your AI Security Assessment today at:
https://iagentsflow.com