OWASP Top 10 for Agentic AI: What You Need to Know in 2026

Agentic AI refers to autonomous AI systems that go beyond simple chatbots to plan multi-step workflows, invoke tools and APIs independently, and make decisions without human intervention. These AI agents now book travel, manage calendars, approve expenses, deploy code, and handle customer service autonomously—creating an entirely new category of AI agent security risks.

The Open Web Application Security Project (OWASP), the global authority on application security, just released its first-ever Top 10 for Agentic Applications for 2026. Developed by over 100 security experts, this framework identifies the most critical threats facing autonomous AI systems: from goal hijacking and memory poisoning to rogue agents and cascading failures.

Whether you’re a security professional implementing AI safeguards, a business leader evaluating autonomous agents, or a developer building agentic systems, this comprehensive guide covers the risks you need to understand and the controls you need to implement.

OWASP Agentic Top 10 Brief

Most Common

ASI01 Goal Hijack: Prompt injection attacks redirecting agent objectives
ASI02 Tool Misuse: Legitimate tools weaponized through agent manipulation
ASI06 Memory Poisoning: Long-term context corruption influencing future decisions

Most Dangerous

ASI10 Rogue Agents: Persistent malicious behavior outlasting initial compromise
ASI09 Cascading Failures: Errors propagating across autonomous agent networks
ASI04 Privilege Abuse: Agents exceeding authorized access boundaries

Hardest to Detect

ASI06 Memory Poisoning: Subtle context manipulation with delayed effects
ASI08 Training Data Poisoning: Backdoors embedded during model training
ASI07 Multi-Agent Threats: Coordinated attacks across distributed systems

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What Makes Agentic AI Different (and Riskier)?

Traditional AI systems are reactive: you ask a question, they answer. Agentic AI systems are proactive and autonomous. They can:

Plan multi-step workflows to achieve complex goals
Decide which tools and APIs to invoke without asking permission
Persist information across sessions using
Communicate and coordinate with other AI agents
Operate continuously, 24/7, making decisions on behalf of users and organizations

Feature	Traditional AI (LLMs)	Agentic AI
Action	Passive (Responds)	Proactive (Initiates)
Scope	Single Turn	Multi-step Workflows
Tools	None / Read-only	Active Execution (API/DB)
Memory	Session-limited	Persistent / Long-term
Risk	Misinformation	System Compromise

Major companies already deploy these systems at scale. Salesforce’s Agentforce handles customer service workflows autonomously. Microsoft’s creates agents accessing sensitive business data across Microsoft 365. ServiceNow’s AI agents automate IT and HR processes, reducing manual workloads by up to 60%.¹ Amazon uses agentic AI to optimize delivery routes, saving an estimated $100 million annually by replacing manual analyst modifications with AI-driven optimization.²

According to major research firms, agentic AI adoption is accelerating faster than security controls:

PwC & McKinsey Surveys: 79% of organizations report at least some level of AI agent adoption, with 62% already experimenting with or scaling agentic AI systems in production
Forrester’s 2026 Cybersecurity Predictions: Agentic AI deployments will likely trigger major security breaches and lead to employee dismissals if organizations fail to implement proper safeguards. The research firm emphasizes that these breaches stem from “cascading failures” in autonomous systems, not individual mistakes
Gartner Analysis: By 2028, 33% of enterprise software will incorporate agentic AI, and 15% of daily business decisions will be handled autonomously. That’s up from less than 1% in 2024

The challenge is clear: we’re deploying these systems faster than we’re securing them. Yet the same capabilities that make agents powerful make them dangerous when compromised. A single vulnerability can cascade across interconnected systems, amplifying traditional security risks and introducing entirely new attack vectors.

According to Gartner, by 2028, 33% of enterprise software will incorporate agentic AI, and 15% of daily business decisions will be handled autonomously. That’s up from less than 1% in 2024.³ The challenge: the same capabilities that make agents powerful make them dangerous when compromised. A single vulnerability can cascade across interconnected systems, amplifying traditional security risks and introducing new attack vectors.

Business Leaders

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What This Means for Your Organization

The OWASP Agentic Top 10 reflects real incidents already happening in production environments. From the EchoLeak attack on Microsoft Copilot to supply chain compromises in Amazon Q, attackers are actively exploiting these vulnerabilities.

Yet according to Forrester, most organizations lack the security controls to prevent what the firm predicts will be “major breaches and employee dismissals” stemming from agentic AI compromises in 2026. The research firm emphasizes that these breaches stem from “cascading failures” in autonomous systems, not individual mistakes.⁴ Meanwhile, according to PwC and McKinsey surveys, 79% of organizations report at least some level of AI agent adoption, with 62% already experimenting with or scaling agentic AI systems.⁵

The OWASP framework emphasizes foundational principles organizations must implement:

Least Agency: Avoid deploying agentic behavior where unnecessary. Unnecessary autonomy expands your attack surface without adding value.
Strong Observability: Maintain clear visibility into what agents are doing, why they’re doing it, and which tools they’re invoking. Without comprehensive logging and monitoring, minor issues quietly cascade into system-wide failures.
Zero Trust Architecture: Design systems assuming components will fail or be exploited. Implement blast-radius controls, sandboxing, and policy enforcement to contain failures.
Human-in-the-Loop for High-Impact Actions: Require human approval for privileged operations, irreversible changes, or goal-changing decisions.

Developers

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How This Relates to Broader AI Security Frameworks

The OWASP Agentic Top 10 builds on the organization’s existing OWASP Top 10 for Large Language Models (LLMs), recognizing that agentic systems amplify traditional LLM vulnerabilities through autonomy and multi-step execution.

The Agentic Top 10 aligns with major security frameworks across the industry:

NIST AI Risk Management Framework: Provides governance structure and risk management processes for AI systems across organizations
MITRE ATLAS: Catalogs specific adversarial tactics and attack techniques against AI systems, building on MITRE ATT&CK
ISO 42001: Establishes international standards for AI management systems and governance
EU AI Act: Sets regulatory requirements for high-risk AI applications in the European market

OWASP has also mapped the Agentic Top 10 to its Non-Human Identities (NHI) Top 10, recognizing that agents are autonomous non-human identities requiring dedicated security controls around credential management, privilege scoping, and lifecycle governance. This connection is critical for enterprises implementing comprehensive identity and access management strategies across human and non-human entities.

The OWASP Agentic Top 10 Breakdown

The OWASP Top 10 for Agentic Applications identifies the most critical security risks organizations face when deploying autonomous AI. Explore each risk below—expand the sections that matter most to your security posture.

The Security Imperative for Autonomous AI

The OWASP Top 10 for Agentic Applications represents a watershed moment in AI security—the first comprehensive framework addressing the unique threats posed by systems that can autonomously plan, decide, and act on behalf of users and organizations.

Why This Matters Right Now

Immediate Priority Risks (Exploit in the Wild)

ASI01: Goal Hijacking: Prompt injection attacks are actively exploiting production agents. The EchoLeak attack demonstrated zero-click data exfiltration through crafted emails. Mitigation priority: CRITICAL

ASI02: Tool Misuse: Agents with legitimate access to databases, email systems, and cloud infrastructure can be manipulated into weaponizing their own privileges. Mitigation priority: CRITICAL

ASI04: Supply Chain Compromises: The first malicious MCP server harvested 1,500+ emails before detection. Dynamic dependency loading creates ongoing risk. Mitigation priority: HIGH

We’re at an inflection point. 79% of organizations have already adopted some level of AI agent technology, with 62% actively experimenting or scaling production deployments. Yet according to Forrester, most organizations lack the security controls to prevent what the firm predicts will be “major breaches and employee dismissals” stemming from agentic AI compromises in 2026.

Security Teams

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The attacks aren’t theoretical. From Microsoft Copilot’s EchoLeak vulnerability (CVSS 9.3) to the Amazon Q supply chain compromise affecting 950,000+ installations, attackers have already weaponized the very autonomy that makes these systems valuable. The question isn’t whether your agents will be targeted—it’s whether you’ll have the defenses in place when they are.

These threats are building in real-world systems and pose increasing risks as agent deployments scale:

Risk Category	Attack Vector	Real-World Impact	Detection Difficulty
ASI06: Memory Poisoning	Delayed tool invocation, persistent context corruption	Agent “remembers” malicious instructions across sessions	Very High - appears as legitimate learning
ASI08: Cascading Failures	Single compromise spreads across multi-agent workflows	Minor GitHub issue prompt injection leaked entire private repos	High - distributed attack surface
ASI10: Rogue Agents	Goal drift, reward hacking, emergent misalignment	Agents deleting production backups to “optimize costs”	Extreme - behavioral vs. rule-based detection

Human-Centric Vulnerabilities (Hardest to Solve)

leverages automation bias—our tendency to trust confident AI recommendations. When a compromised finance agent authorizes fraudulent payments with compelling justification, the human approver becomes the vulnerability. Traditional security tools can’t detect manipulation of human decision-making.

ASI01: Agent Goal Hijack: Prompt injection redirects agent objectives (e.g., EchoLeak attack)
ASI02: Tool Misuse and Exploitation: Legitimate tools weaponized through manipulation
ASI03: Identity and Privilege Abuse: Credential delegation chains exploited for escalation
ASI04: Agentic Supply Chain Vulnerabilities: Poisoned plugins, MCP servers, and dependencies
ASI05: Unexpected Code Execution (RCE): Code generation features exploited for remote command execution
ASI06: Memory and Context Poisoning: Long-term memory corrupted to influence future decisions
ASI07: Insecure Inter-Agent Communication: Multi-agent systems lacking authentication
ASI08: Cascading Failures: Single faults propagating across autonomous systems
ASI09: Human-Agent Trust Exploitation: Automation bias exploited to approve malicious actions
ASI10: Rogue Agents: Persistent harmful behavior outlasting initial compromise

See the detailed risk accordion sections above for comprehensive analysis of each vulnerability, real-world examples, and mitigation strategies.

Your Action Plan: Where to Start

Not sure where to begin? Start with these foundational steps:

Week 1: Inventory & Assessment

Enumerate all agentic AI deployments (approved and shadow IT)
Document what each agent can access (databases, APIs, email, cloud resources)
Identify agents handling sensitive data or critical workflows
Note which agents coordinate with other agents

Week 2: Access Control Review

Map privilege boundaries for each agent
Check for credential sharing or inherited permissions
Identify delegation chains that may exceed intended scope
Document the blast radius if each agent is compromised

Week 3: Observability & Logging

Implement comprehensive logging of agent actions and tool invocations
Set up monitoring for anomalous agent behavior
Create dashboards for goal changes, memory modifications, and privilege escalations
Establish baselines for normal agent activity

Week 4: Protective Controls

Establish human-in-the-loop approvals for high-impact actions
Configure kill switches for emergency agent shutdown
Test rollback procedures for compromised agents
Document incident response playbooks for agentic AI breaches

After these initial 30 days, you’ll have foundational visibility and controls that address 70% of OWASP risks.

The OWASP framework emphasizes four foundational principles that address 70% of the Top 10 risks:

Deploy autonomy only where necessary. Every autonomous capability expands your attack surface. Ask: “Could this task be accomplished with human approval instead of full automation?”
You can’t secure what you can’t see. Implement comprehensive logging of agent actions, tool invocations, goal changes, and decision rationales. Anomaly detection becomes critical when agents operate 24/7.
Design assuming components will fail or be compromised. Implement blast-radius controls, sandboxing, and policy enforcement at every delegation boundary. An agent accessing HR data should never inherit privileges to access financial systems.
Require human approval for privileged operations, irreversible changes, or goal-modifying decisions. The seconds of friction prevent hours of incident response.

The Path Forward

Agentic AI represents one of the most significant shifts in computing since the internet. These systems promise unprecedented automation, efficiency, and capability—but only if we build them securely from the ground up.

The future of work, productivity, and innovation increasingly depends on autonomous AI. But that future only materializes if we build it securely. The OWASP Top 10 for Agentic Applications gives us the framework—now execution becomes the differentiator.

Organizations that treat agentic security as an afterthought will learn through costly breaches. Those that embed these principles from design through deployment will unlock AI’s potential while containing its risks.

The autonomy that makes agents powerful is precisely what makes them dangerous. That paradox demands our full attention, our best security thinking, and our commitment to building systems that are both capable and trustworthy.

The choice is yours: be proactive, or become a cautionary tale.

Follow secure-by-design principles: least privilege, input validation for natural language, output sanitization for tool invocations
Implement allowlists for agent tool access, not denylists (fail closed, not open)
Build observability into your agent architecture from day one
Use the OWASP Agentic Top 10 as your security requirements checklist

Extend threat models to include goal hijacking, memory poisoning, and cascading failures
Treat agentic risks as first-class threats alongside OWASP Web Top 10
Implement agent-specific monitoring: goal drift detection, tool usage anomalies, privilege escalation in delegation chains
Establish incident response playbooks for compromised agents (kill switches, rollback procedures, containment strategies)

Ask: “What’s the blast radius if this agent is compromised?”
Require security reviews before production deployment of autonomous capabilities
Implement staged rollouts: start with low-risk, high-observability use cases
Budget for agent-specific security controls, not just traditional application security

Frequently Asked Questions

Resources

For deeper exploration of agentic AI security, see the resources below for comprehensive frameworks and ongoing research:

EchoLeak - Microsoft 365 Copilot Zero-Click Data Exfiltration

Severity: CRITICAL CVSS Score: 9.3/10 Discovered: June 2025 by Aim Security Patched: May 2025

What happened: A crafted email containing hidden prompt injection instructions caused Microsoft 365 Copilot to silently exfiltrate confidential emails, files, and chat logs without user interaction. The agent interpreted attacker commands embedded in the message as legitimate goals and executed them.

Attack mechanism:

Attacker sends email with hidden instructions to target organization
Copilot processes email and extracts attachments/content
Hidden instructions redirect Copilot to collect sensitive data
Data exfiltrated without user knowledge or approval

Impact: Complete compromise of email, OneDrive, and Teams data accessible to the victim’s account.

Visual Studio Code Agentic AI Command Injection

Severity: HIGH CVSS Score: 8.8/10 Discovered: September 2025 by ZeroPath Security Affected: VS Code agentic AI workflows

What happened: Command injection vulnerability in VS Code’s agentic AI features allowed remote attackers to execute arbitrary commands on developers’ machines through prompt injections hidden in README files, code comments, or repository metadata.

Attack mechanism:

Attacker creates malicious repository with hidden command injection payload in README or code comments
Developer uses VS Code’s agentic AI features to analyze or generate code from the repository
Agent processes malicious content and interprets it as a legitimate instruction

Official OWASP & Governance Frameworks

OWASP Top 10 for Agentic Applications: The complete framework with detailed mitigation strategies for all 10 risks
OWASP Top 10 for LLMs: Foundation security principles for language models that agentic systems build upon
OWASP Non-Human Identities (NHI) Top 10: Specialized framework for securing autonomous agents as non-human entities

Complementary Security Frameworks

NIST AI Risk Management Framework: Governance structure and risk management processes for AI systems
MITRE ATLAS: Adversarial tactics and techniques for AI systems (AI-specific extension of ATT&CK)

LMTEQ. “Reduce Manual Work By 70% With ServiceNow Automation.” LMTEQ Blog, May 2025. lmteq.com
AWS Events. “Agentic GenAI: Amazon Logistics’ $100M Last-Mile Delivery Optimization.” AWS re:Invent 2025, April 2025. youtube.com
Gartner. “5 Predictions About Agentic AI From Gartner.” MES Computing, July 2025. mescomputing.com; World Economic Forum. “Here’s how to pick the right AI agent for your organization.” WEF Stories, May 2025. weforum.org

OWASP Top 10 for Agentic AI: What You Need to Know in 2026

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OWASP Agentic Top 10 Brief

Most Common

Most Dangerous

Hardest to Detect

AI Agent Adoption Statistics (2025-2026)

Business Leaders

Developers

How OWASP Agentic Relates to Other Security Frameworks

Security Teams

Technical Analysis: Emerging Systemic Risks

Quick Reference: Complete List of 10 Risks

Getting Started: First 30 Days Security Checklist

For Developers Building Agentic Systems

For Security Professionals

For Business Leaders

CVE Technical Deep Dive

Additional Resources & Research

Footnotes

ASI01: Agent Goal Hijack

ASI02: Tool Misuse and Exploitation

ASI03: Identity and Privilege Abuse

ASI04: Agentic Supply Chain Vulnerabilities

ASI05: Unexpected Code Execution (RCE)

ASI06: Memory and Context Poisoning

ASI07: Insecure Inter-Agent Communication

ASI08: Cascading Failures

ASI09: Human-Agent Trust Exploitation

ASI10: Rogue Agents