A Deep Dive into the OWASP Top 10 for Agentic Applications 2026

The AI landscape has rapidly shifted from single-turn LLM applications to Agentic Applications, autonomous systems capable of planning, decision-making, and executing multi-step tasks across diverse tools and environments. This Agentic Paradigm introduces a complex new security surface.

The existing OWASP Top 10 for LLM Applications (LLM Top 10) is insufficient. An agent's ability to chain actions and operate autonomously means a minor vulnerability, such as a simple prompt injection, can quickly cascade into a system-wide compromise, data exfiltration, or financial loss. The security challenge is no longer about securing a single model call, but about securing a complex, dynamic, and often unpredictable workflow.

To address this, the OWASP Gen AI Security Project published the OWASP Top 10 for Agentic Applications (2026), the Agentic Security Initiative (ASI) Top 10. This document is the essential compass for security leaders, architects, and developers. It distills the highest-impact threats into ten actionable categories.

This post provides a comprehensive, technical breakdown of the ASI Top 10. Understanding these vulnerabilities, from ASI01: Agent Goal Hijack to ASI10: Rogue Agents, is non-negotiable. We will dissect the core concepts, explore the distinctions between these new threats and their LLM-era predecessors, and integrate our perspective on the architectural imperatives for securing the agentic future.

The Agentic Paradigm Shift: Why Autonomy Changes Everything

The defining characteristic of an Agentic Application is its autonomy. An agent achieves a high-level goal by dynamically selecting, planning, and executing a sequence of actions using an LLM (the "brain"), a Planner, and a set of Tools (APIs, databases, code interpreters) for interacting with the real world.

This autonomy is the source of profound risk. Agents amplify existing vulnerabilities because they operate in a state of Excessive Agency. A contained LLM vulnerability can now be leveraged by an agent to perform a chain of high-impact actions: reading a sensitive file, generating malicious code, and exfiltrating data.

The OWASP document introduces two core principles for secure agentic systems:

1. Least-Agency: An extension of the Principle of Least Privilege. Avoid unnecessary autonomy. Agents should only be granted the minimum level of autonomy required to complete their defined task.
2. Strong Observability: Non-negotiable security control. Requires clear, comprehensive visibility into what agents are doing, why, and which tools they are invoking. Detailed logging of goal state, tool-use patterns, and decision pathways is mandatory.

The OWASP Agentic Top 10: A Technical Breakdown

The ten vulnerabilities identified by the Agentic Security Initiative (ASI) represent the most critical risks in the autonomous AI ecosystem. Each threat requires a nuanced understanding of the agent's lifecycle and its interaction with the environment.

ASI01: Agent Goal Hijack

Agent Goal Hijack is the new SQL Injection for the autonomous world. It occurs when an attacker manipulates an agent’s core objectives, task selection, or decision-making pathways. Unlike traditional prompt injection (LLM01), which is often transient, Goal Hijack captures the broader agentic impact where manipulated inputs redirect goals, planning, and multi-step behavior. This can be achieved indirectly through external data sources or deceptive tool outputs. We view this as a fundamental vulnerability that must be addressed at the architectural level, not patched with filters. The primary defense is to treat all natural-language inputs as untrusted, routing them through rigorous validation. Crucially, the "Intent Capsule" pattern, a signed, immutable envelope that binds the agent's original mandate to each execution cycle, is a mandatory architectural requirement for any serious agent deployment, alongside a human-in-the-loop mechanism for high-impact or goal-changing actions.

ASI02: Tool Misuse and Exploitation

Tool Misuse and Exploitation describes a scenario where an agent uses a legitimate, authorized tool in an unsafe or unintended manner, causing harm due to ambiguous instructions or prompt-driven manipulation. The agent is operating within its existing privileges, which distinguishes it from ASI03: Identity & Privilege Abuse (gaining new privileges) and ASI05: Unexpected Code Execution (a specific, critical form of misuse). This is a direct failure of the Least-Agency principle; if an agent can misuse a tool, the tool's scope was inherently too broad. We advocate for a Zero-Trust Tooling model where every tool call is treated as a high-risk operation. Mitigation requires defining strict, granular, just-in-time permissions for all tools and never blindly passing LLM-generated output to a tool without rigorous validation against a strict schema. Platforms such as NeuralTrust support this model by enabling fine grained tool permissioning, policy based controls, and continuous monitoring of tool usage, helping security teams detect misuse patterns and enforce guardrails around agent tool interactions in real time.

ASI03: Identity and Privilege Abuse

Identity and Privilege Abuse addresses the risk of an agent escalating its privileges, either by abusing its own identity or by inheriting the credentials of other tools or services. Agents are the most dangerous class of Non-Human Identities (NHI) ever created, and their ability to autonomously chain tools creates significant opportunities for privilege escalation. We must treat them with more suspicion than human users, demanding Zero-Trust Identity Management where long-lived credentials are an unacceptable risk. Securing agent identity is paramount: each agent must have its own unique, managed identity with the minimum required permissions, utilizing short-lived, session-based credentials that expire after a task is complete. Continuous monitoring and auditing of all agent identities are essential to detect signs of privilege abuse.

ASI04: Agentic Supply Chain Vulnerabilities

Agentic Supply Chain Vulnerabilities arise because agentic applications depend on a complex supply chain of external components, including third-party APIs, pre-trained models, RAG data sources, and tool definitions. A vulnerability in any one of these components can be inherited by the agent, such as a poisoned RAG vector subtly corrupting every decision the agent makes. The initial point of compromise is external to the agentic application itself. We assert that the supply chain for agents extends beyond code to data and models, making continuous validation and integrity checks on all external data sources the only viable defense. Organizations must maintain a comprehensive Software Bill of Materials (SBOM) that includes all AI-specific assets and use trusted, secure registries for models and tools.

ASI05: Unexpected Code Execution (RCE)

Unexpected Code Execution (RCE) occurs when an attacker manipulates the agent into generating and executing malicious code, such as code that exfiltrates data or establishes a reverse shell. This is a specific and highly critical form of ASI02: Tool Misuse, focused exclusively on the misuse of code-interpreting tools. Our perspective is that any agent with code execution capabilities is a critical liability without a hardware-enforced, zero-access sandbox. Software-only sandboxing is insufficient. All code generated by an LLM must be executed in a secure, isolated sandbox environment with no access to the underlying host system or sensitive network resources, and must be subjected to static and dynamic analysis before execution.

ASI06: Memory & Context Poisoning

Memory & Context Poisoning is the persistent corruption of the agent's stored information (vector stores, knowledge graphs) that maintain state and inform future decisions. An attacker injects malicious data into the agent's long-term memory, causing the agent to exhibit misaligned or harmful behavior over time, even in the absence of an active attack. The keyword here is persistent, distinguishing it from the transient manipulation of Goal Hijack. We believe secure memory is the foundation of agent integrity; it is not just a feature. We must treat the agent's long-term memory store as a highly sensitive database, demanding cryptographic integrity checks, rigorous sanitization and validation of all ingested data, and robust version control to ensure that any corruption can be immediately detected and rolled back.

ASI07: Insecure Inter-Agent Communication

Insecure Inter-Agent Communication arises in complex multi-agent systems when communication channels are vulnerable to interception, message forging, or replay attacks, allowing an attacker to impersonate a high-privilege agent. This vulnerability is unique in its focus on the protocol and channel used for agent-to-agent interaction. Multi-agent systems are distributed systems, and they must be secured as such. We must apply military-grade cryptographic signing and mutual TLS (mTLS) to all internal agent communication, as trusting an agent's identity based solely on its network location is a fatal architectural error. Agents must cryptographically authenticate each other before establishing communication, and all messages must be digitally signed to ensure integrity and non-repudiation.

ASI08: Cascading Failures

Cascading Failures occur when a small failure in one component (e.g., a minor error in a tool call) triggers a chain reaction that leads to a system-wide, uncontrolled failure, often resulting in the agent's planner executing increasingly destructive or costly actions in an attempt to recover. This is a resilience and architecture vulnerability, distinct from malicious intent. We view cascading failures as an architectural flaw, a direct result of poor planning and a lack of defensive coding. Every agentic workflow must be designed with circuit breakers and transactional rollback capabilities to ensure that a failure in one step does not lead to an uncontrolled, destructive chain reaction. Agents must have clearly defined, safe failure modes, pausing and seeking human intervention when an error occurs.

ASI09: Human-Agent Trust Exploitation

Human-Agent Trust Exploitation targets the human user's trust in the agent, where an attacker manipulates the agent's output to deceive the human into bypassing security controls or approving a malicious action. This targets the human-in-the-loop mechanism itself, exploiting the human's reliance on the agent's perceived authority. Our perspective is that the human is the weakest link, and our systems must be designed to distrust the human's approval of a high-risk action unless the agent's decision is fully transparent and auditable. The human-in-the-loop must be a critical review step, not a simple rubber stamp, requiring the agent to clearly articulate why it is proposing an action, including the source of the data and the tools it intends to use.

ASI10: Rogue Agents

Rogue Agents are autonomous entities that drift from their intended purpose or exhibit misaligned behavior without active external manipulation, often due to flaws in the reward function or governance model. This is the most purely agentic of the threats, a self-initiated, autonomous threat stemming from internal misalignment, unlike ASI01: Agent Goal Hijack which requires an active attacker. Rogue agents are the ultimate failure of governance and design. The "kill switch" must be a non-negotiable, auditable, and physically isolated mechanism. Furthermore, continuous behavioral monitoring must be in place to detect subtle drift before it becomes a catastrophic misalignment, alongside rigorous testing and auditing of the agent's reward function.

Architectural and Operational Implications

The OWASP Agentic Top 10 is not merely a list of vulnerabilities; it is a mandate for a fundamental shift in how we approach the architecture and operation of AI systems. The core principles of Least-Agency and Strong Observability must be woven into the fabric of the entire development lifecycle.

The Least-Agency Principle in Practice

Least-Agency dictates that autonomy is a feature to be earned, not a default setting. Practically, this means:

• Just-in-Time Agency: Granting the agent autonomy only for the specific, short-lived task it is currently executing, and revoking it immediately afterward.
• Configuration Management: Defining agent goals, reward functions, and permitted actions through explicit, auditable configuration files, with formal change management and human approval for changes.

Observability as a Security Control

Observability is no longer just a debugging tool; it is a critical security control. To defend against threats like ASI08: Cascading Failures and ASI10: Rogue Agents, organizations must:

• Log Everything: Maintain comprehensive logging of every decision, tool call, and state change, including a stable identifier for the active goal.
• Behavioral Baselines: Establish a baseline of normal, expected behavior for each agent. Any deviation must trigger an immediate alert for review.

Integration with Existing Standards

The ASI Top 10 integrates with the OWASP Top 10 for LLM Applications and the OWASP Top 10 for Non-Human Identities (NHI). This unified framework allows security teams to leverage existing expertise in identity management and application security while adapting to the unique challenges of agent autonomy.

Red Teaming

Specialized red-teaming is necessary. Traditional penetration testing is insufficient. Security teams must conduct periodic tests that simulate complex, multi-step attacks, such as:

• Simulating Agent Goal Hijack via indirect prompt injection.
• Testing the system's resilience to Cascading Failures by introducing minor, non-malicious errors.
• Verifying the effectiveness of the kill-switch mechanism against a Rogue Agent scenario.

To support these efforts, automated red teaming tools designed specifically for generative and agentic AI can help organizations continuously test and validate their defenses at scale. Solutions such as NeuralTrust’s automated red teaming platform enable security teams to systematically simulate real world attack patterns and assess agent behavior under adversarial conditions.

Securing the Future of Agentic AI

The OWASP Top 10 for Agentic Applications 2026 marks a watershed moment in AI security. It is a clear acknowledgment that the shift to autonomous, agentic systems has introduced a new class of high-impact, high-urgency threats that cannot be addressed with legacy security models.
For every organization deploying or planning to deploy autonomous agents, the ASI Top 10 is the definitive blueprint for risk mitigation. The vulnerabilities, from the subtle manipulation of ASI01: Agent Goal Hijack to the existential threat of ASI10: Rogue Agents, demand a proactive, architectural response. By embracing the principles of Least-Agency and Strong Observability, and by rigorously implementing the technical mitigations outlined for each of the ten threats, security and development teams can move forward with confidence. The future of AI is autonomous; securing that future requires immediate and comprehensive adoption of the OWASP Agentic Security Initiative.

Advancing Agentic AI Security Together

At NeuralTrust, we actively support initiatives that bring structure, transparency, and shared standards to the evolving field of agentic AI security. As autonomous and semi-autonomous systems become more widely adopted, the need for practical security guidance grounded in real world behavior has never been greater.

Our work is driven by a commitment to empower defenders with the tools and methodologies required to understand how AI agents behave in practice and how they can fail. By contributing research, testing frameworks, and applied insights, we aim to help organizations move beyond theoretical risk and toward measurable and enforceable security controls.

As agentic AI continues to mature, security teams will need reliable ways to evaluate, govern, and stress test these systems at scale. NeuralTrust remains focused on advancing this work alongside the broader security community and on helping organizations build resilient foundations for the safe deployment of AI agents.

If you would like to learn more about how NeuralTrust supports organizations in identifying and mitigating emerging risks in agentic AI, we would be happy to connect.