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OWASP Agentic AI Threats

OWASP Agentic AI Threats is a threat taxonomy for software agents that can act, plan, and use tools with limited human supervision. It focuses on risks such as prompt injection, tool abuse, data leakage, identity misuse, and unsafe autonomous actions, helping teams assess control gaps across AI, IAM, and security operations.

What OWASP Agentic AI Threats Covers

OWASP agentic ai Threats is not just a list of AI bugs, it is a threat taxonomy for systems that can plan, act, and use tools with limited supervision. The value of the taxonomy is that it groups recurring failure patterns into a practical lens for assessing agent behaviour, control gaps, and blast radius.

Its focus is broader than prompt injection alone. The taxonomy helps teams reason about how an agent can be manipulated, how its tool access can be abused, and how mistakes in orchestration, memory, or authorization can turn a useful assistant into a security liability.

Why Agentic AI Threats Matter

Agentic systems create a different security problem from static chat interfaces because they can take actions, not just generate text. Once an agent can call APIs, move through workflows, or chain tools, the main risk is no longer only content risk, it is unauthorized execution, hidden side effects, and trust abuse across systems.

This is why the threat model matters to AI teams, IAM teams, and security operations together. A weakness in one layer, such as an overly broad token, a confused-deputy path, or a poisoned instruction source, can become a cross-domain issue that affects data, access, and operational integrity.

The OWASP Agentic AI Top 10 is the most direct external reference for this taxonomy, and it maps the threats to patterns such as agent goal hijacking, tool misuse, identity and privilege abuse, memory poisoning, and rogue agents. OWASP Agentic AI Top 10

Common Threat Patterns in Agentic Systems

The most important threat patterns usually cluster around four surfaces: instructions, tools, memory, and authority. Prompt injection and indirect prompt injection try to steer the agent’s decisions. Tool misuse turns a legitimate connector into an unsafe action path. Memory poisoning corrupts future reasoning. Identity misuse lets the agent act with more authority than intended.

Those patterns are dangerous because agent pipelines often blend human intent, model output, and automation into one workflow. If the system cannot reliably separate instruction from data, or request from approval, an attacker can exploit that ambiguity to trigger destructive or misleading actions.

MITRE ATLAS is a useful companion when you need an adversary-technique view of the same problem space, especially for agent hijacking, context poisoning, and other AI-oriented attack behaviours. MITRE ATLAS adversarial AI threat matrix

How Teams Use the Taxonomy

Teams use the taxonomy to structure threat modelling, prioritise controls, and compare agent designs before they are put into production. It is especially useful when deciding whether an agent should be allowed to browse, write, execute, recommend, or approve anything on behalf of a user or service.

The practical test is simple: if the threat could change the agent’s authority, its tool behaviour, or the trust placed in its output, the taxonomy should inform the control design. That usually means tighter authorization, better containment, stronger logging, and explicit separation between user intent and machine action.

For teams building security programmes around agentic systems, Agentic AI Security Guide provides a layered companion view that connects threats to inputs, memory, tools, orchestration, and identity.

Risk and Threat Considerations

Agentic systems can fail in ways that are materially worse than ordinary model misuse because the agent may have standing access, persistent context, and the ability to trigger real-world side effects. That makes the main risk not only bad output, but abuse of delegated authority, unsafe automation, and downstream compromise through trusted integrations.

Failure mechanism: An attacker or malicious instruction can redirect the agent’s planning, cause it to call the wrong tool, or persuade it to use valid credentials and permissions in an unintended way.

Impact: The result can be data exposure, unauthorized transactions, destructive actions, lateral movement through connected systems, or persistent trust erosion across the automation stack.

Standards & Framework Alignment

This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.

OWASP Agentic AI Top 10 provides the primary governance reference for this term.

Framework Control / Reference Relevance
OWASP Agentic AI Top 10 ASI01 — Agent Goal Hijack Defines hijacking of an agent's goals and planning.
ASI02 — Tool Misuse Covers unsafe or unintended use of tools by agents.
ASI03 — Identity & Privilege Abuse Addresses misuse of delegated identity or excess privilege in agentic systems.
Recommendation — Constrain agent goals and validate that instructions cannot redirect planned actions. Restrict tool scopes and gate high-impact actions before execution. Apply least privilege to agent credentials and separate approval from execution.

Practitioner Guidance

Why practitioners should care: This taxonomy is most useful when you are deciding where agent control boundaries must sit. If a threat category would change whether the agent can browse, execute, approve, or persist state, it deserves a specific control decision rather than a generic safety review.

Common misunderstanding: Many teams treat agent threats as a prompt-engineering problem. In practice, the serious failures often come from authority design, tool exposure, memory handling, and weak separation between suggestions and actions.

Practitioner takeaway: Use the taxonomy as a pre-production control lens, then revisit it whenever an agent gains a new tool, a new permission, or a new path to act on behalf of someone else.