OWASP has released a new edition of its AI security report, “State of Agentic AI Security and Governance v2.01,” giving security teams a concrete playbook for defending autonomous AI agents and the expanding ecosystem of tools they rely on.
Positioned within the OWASP GenAI Security Project, the report shifts AI security conversations from hypothetical threat models to evidence-based guidance, backed by live incident data, a Top 10 risk taxonomy for agentic applications, and a growing catalog of defensive tools and training resources for practitioners.
OWASP Top 10 Agentic AI Risks (for 2026)
| ID | Risk name | One‑line description |
|---|---|---|
| ASI01 | Agent Goal Hijack | Attackers redirect an agent’s objectives or plans via malicious content or prompts. |
| ASI02 | Tool Misuse and Exploitation | Agents invoke connected tools or APIs in unsafe ways, amplifying attacker input. |
| ASI03 | Identity and Privilege Abuse | Agents inherit, misuse, or escalate credentials to access higher‑privilege resources. |
| ASI04 | Agentic Supply Chain Vulnerabilities | Compromised tools, plugins, MCP servers, or external components corrupt agent workflows. |
| ASI05 | Unexpected Code Execution | Agents generate or run code/commands that lead to RCE, sandbox escape, or data loss. |
| ASI06 | Memory and Context Poisoning | Attackers poison agent memory, embeddings, or RAG stores to steer future behavior. |
| ASI07 | Insecure Inter‑Agent Communication | Weakly authenticated or unvalidated messages let attackers spoof or tamper with agents. |
| ASI08 | Cascading Failures | Small errors or compromises propagate across multi‑agent workflows and shared tools. |
| ASI09 | Human–Agent Trust Exploitation | Agents abuse user trust and UX patterns to drive unsafe approvals or actions. |
| ASI10 | Rogue Agents | Misaligned or compromised agents operate autonomously in harmful, insider‑like ways. |
AI Security Report
The report’s central message is that agentic AI is no longer an experimental edge case: production incidents, vendor advisories, and CVEs now exist for almost every class of agentic risk OWASP tracks. v1.0, published in July 2025, treated autonomous agents as an emerging exposure; v2.01, released in June 2026, reads a year of field evidence and ties it directly to deployment architectures, showing where guardrails have failed in real systems.
A Real-World Incidents and Exploits Tracker maps concrete failures, such as zero‑click prompt injection against enterprise copilots, sandbox escapes in coding agents, and agent‑to‑agent protocol spoofing, to the OWASP Top 10 for Agentic Security, providing defenders with attack chains they can test and emulate.
A major addition in this edition is a refined taxonomy for agentic systems that classifies agents across three axes: operational role, implementation pattern, and composition pattern, with autonomy treated as a cross‑cutting dimension.
Enterprise agents, coding agents, client‑facing assistants, personal agents, and infrastructure‑ops agents each receive distinct treatment regarding trust boundaries, regulatory triggers, and governance challenges, reflecting how their blast radius changes as autonomy and tool access increase.
At the implementation layer, OWASP distinguishes between full orchestration frameworks, lightweight library compositions, and platform‑native low‑code builders, warning that shadow AI and citizen‑developer flows in low‑code environments now represent some of the least visible and highest‑risk deployments.
On the threat‑modeling side, OWASP explicitly collapses the operational distinction between “AI safety” and “AI security” at the deployment layer, arguing that the same architectural choices, permissions, tool surfaces, oversight, and runtime controls, govern both adversarial misuse and non‑malicious failures.
The report describes how prompt injection, persistent memory poisoning, and tool‑misuse exploitation now intersect with agents’ inherent unreliability, especially when they operate with broad autonomy and minimal human‑in‑the‑loop controls.
For security leaders, the implication is organizational as much as technical: AI safety functions can no longer sit entirely apart from security teams when agents can send emails, modify code, invoke APIs, and trigger financial transactions without human review.
The tooling story is where many security teams will find immediate value. OWASP situates this report inside its Agentic Security Initiative (ASI), which now ships a Top 10 for Agentic Security, a Securing Agentic Applications Guide, and an Agentic AI Threats & Mitigations reference architecture that maps risks like Agent Goal Hijack, Tool Misuse Exploitation, Agentic Supply Chain compromise, and Memory Context Poisoning to concrete mitigations.
Companion resources include an Agent Name Service for secure agent discovery and identity verification across protocols such as A2A, MCP, and ACP, as well as a Practical Guide for Secure MCP Server Development that addresses how tool servers should authenticate, authorize, and constrain agent‑driven actions.
An Agentic AI Solutions Landscape and AI Security Solutions Landscape help teams map OWASP risk categories to commercial and open‑source products, from policy engines and observability stacks to red‑teaming platforms.
To accelerate defender skill‑building, OWASP is also promoting FinBot, a financial‑themed, multi‑agent Capture‑the‑Flag (CTF) environment that operationalizes many of the risks described in the report.
FinBot presents autonomous agents handling vendor onboarding, invoice processing, payments, and fraud analysis, all wired to real tools via MCP servers, and challenges players to exploit them through prompt injection, tool poisoning, and indirect payload delivery.
Each challenge is mapped to the OWASP Top 10 for Agentic Security, OWASP Top 10 for LLM Applications, MITRE ATLAS, and CWE, turning the theoretical taxonomy into hands‑on attack and detection exercises security teams can run safely.
For organizations still at early adoption tiers, OWASP positions FinBot as a pre‑deployment training ground to build intuition before granting agents broader autonomy in production workflows.
Finally, the report closes with an enterprise adoption maturity model that traces agentic deployments from shadow AI to fully federated, multi‑agent architectures, mapping which ASI risk classes dominate at each tier and outlining the operational requirements security leaders should plan for through 2027.
With 53 tracked open‑source agentic projects, over 2.5 million combined stars, and more than 200 published security advisories surveyed, OWASP warns that the agentic ecosystem is scaling faster than traditional AppSec and compliance processes can comfortably manage.
For defenders, the new report is less a theoretical white paper and more a strategic roadmap, linking concrete incidents, taxonomy, tools, training environments, and regulatory mappings into a cohesive blueprint for securing AI agents in production.
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