The OWASP ASI Top 10 is here. Learn the 10 biggest threats to autonomous AI agents, from Goal Hijack to Rogue Agents, and how to secure your code against the next generation of attacks.
Hey developers! Remember when LLM security was mostly about prompt injection? Good times. Now, we're building Autonomous AI Agents that can plan, make decisions, run code, and access databases. The security game just changed completely.
The old OWASP Top 10 for LLM Applications is no longer enough. Why? Because an agent's ability to chain actions and operate autonomously means a small vulnerability can quickly cascade into a system-wide compromise. We're not just securing a single model call. We're securing a complex, dynamic workflow.
That's why the OWASP Gen AI Security Project released the OWASP Top 10 for Agentic Applications (2026), also known as the ASI Top 10. This is the new security blueprint for anyone building or deploying autonomous systems.
Let's dive into the core concepts and the 10 most critical threats you need to know about.
The Agentic Paradigm: Autonomy is the Risk
The defining feature of an agent is its autonomy. It achieves a high-level goal by dynamically selecting, planning, and executing a sequence of actions using its LLM "brain" and a set of "Tools" (APIs, interpreters).
This autonomy leads to Excessive Agency, where the agent has more power than it needs, amplifying every security flaw. To counter this, the ASI Top 10 introduces two core principles:
- Least-Agency: An extension of the Principle of Least Privilege (PoLP). Agents should only be granted the minimum level of autonomy required to complete their defined task. Autonomy is a feature to be earned, not a default setting.
- Strong Observability: You need 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 ASI Top 10: A Developer's Guide
The ten vulnerabilities identified by the Agentic Security Initiative (ASI) represent the highest-impact threats in the autonomous AI ecosystem.
| ASI ID | Vulnerability | The Threat | Mitigation Focus |
|---|---|---|---|
| ASI01 | Agent Goal Hijack | Attacker manipulates the agent's core objective or decision pathway (the new Indirect Prompt Injection). | Treat all natural-language inputs as untrusted; use an "Intent Capsule" pattern. |
| ASI02 | Tool Misuse & Exploitation | Agent uses a legitimate, authorized tool in an unsafe or unintended manner (e.g., using a read tool to exfiltrate a secret). |
Zero-Trust Tooling; define strict, granular, just-in-time permissions. |
| ASI03 | Identity & Privilege Abuse | Agent escalates its privileges by abusing its own identity or inheriting credentials. | Zero-Trust Identity Management; use unique, short-lived, session-based credentials. |
| ASI04 | Agentic Supply Chain Vulnerabilities | Vulnerabilities inherited from external components (poisoned RAG data, vulnerable tool definitions, pre-trained models). | Continuous validation of external data sources; maintain an AI-specific Software Bill of Materials (SBOM). |
| ASI05 | Unexpected Code Execution (RCE) | Agent is tricked into generating and executing malicious code (e.g., a reverse shell). | Mandatory hardware-enforced, zero-access sandboxing for all code interpreters. |
| ASI06 | Memory & Context Poisoning | Persistent corruption of the agent's long-term memory (vector stores, knowledge graphs). | Cryptographic integrity checks and rigorous sanitization of all ingested data. |
| ASI07 | Insecure Inter-Agent Communication | Vulnerabilities in multi-agent systems allowing message forging or impersonation. | Apply mTLS and digital signing to all agent-to-agent communication. |
| ASI08 | Cascading Failures | A small error triggers an uncontrolled, destructive chain reaction in the agent's workflow. | Implement circuit breakers and transactional rollback capabilities; define safe failure modes. |
| ASI09 | Human-Agent Trust Exploitation | Attacker manipulates the agent's output to deceive the human-in-the-loop into approving a malicious action. | Ensure the agent's reasoning is fully transparent and auditable before human approval. |
| ASI10 | Rogue Agents | Agents operating outside of their intended mandate or control, often due to a failure in governance or a malicious update. | Implement a robust, auditable kill-switch mechanism and continuous behavioral monitoring. |
Deep Dive: The Most Critical Threats
While all ten are important, a few demand immediate architectural attention from developers.
ASI01: Agent Goal Hijack (The New SQLi)
This is the evolution of prompt injection. It's not just about getting the model to say something silly; it's about getting the agent to change its core mission.
Developer Action:
- Intent Capsule: Architect your system so the agent's original, signed mandate (the "Intent Capsule") is bound to every execution cycle. If a new input tries to change the goal, the system should flag it immediately.
- Validation: Treat every natural language input, whether from a user or an external data source (like a RAG document), as untrusted and route it through rigorous validation before it hits the agent's planner.
ASI05: Unexpected Code Execution (RCE)
If your agent can write and run code (e.g., for data analysis or debugging), it is a massive liability. A compromised agent can generate code to exfiltrate data or establish a reverse shell.
Developer Action:
- Sandbox, Sandbox, Sandbox: Any code generated by an LLM must be executed in a secure, isolated sandbox environment. This sandbox needs to have zero network access and limited file system access. Software-only sandboxing is not enough; look for hardware-enforced solutions.
ASI06: Memory & Context Poisoning
Agents have memory (vector stores, knowledge graphs) that inform future decisions. An attacker can inject malicious data into this long-term memory, causing the agent to behave incorrectly days or weeks later.
Developer Action:
- Data Integrity: Treat the agent's memory store as a highly sensitive database. Implement cryptographic integrity checks and version control on the memory to ensure that any corruption can be immediately detected and rolled back.
Conclusion: Securing the Autonomous Future
The OWASP Top 10 for Agentic Applications is a clear call to action. The future of AI is autonomous, but its success depends entirely on our ability to secure it.
For developers, this means shifting your mindset from securing static applications to securing dynamic, privileged, and autonomous entities. Embrace Least-Agency and Strong Observability as your guiding principles.
Start by auditing your agents against the ASI Top 10 today. The threats are real, and the time to build in the defenses is now.
Which of the ASI Top 10 threats do you think is the most challenging to mitigate in a real-world multi-agent system? Let's discuss in the comments!
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