AI-Specific Threats

What are AI-specific threats in agentic systems?

AI-specific threats are security risks that emerge when autonomous or semi-autonomous agents reason, act, and interact with tools, data, and systems.

Unlike traditional applications, agents interpret inputs probabilistically, retain memory, and execute multi-step actions—expanding the attack surface beyond credentials and APIs.

These threats target agent behavior, intent, and decision boundaries rather than just infrastructure.

What are the OWASP Top 10 risks for AI agents?

The OWASP Top 10 for AI agents highlights new classes of vulnerabilities introduced by agent autonomy and tool usage. These include prompt injection, insecure delegation, excessive agency, data leakage, insufficient isolation, and lack of action traceability.

For IAM and CIAM systems, these risks translate into unauthorized access, privilege escalation, and unbounded agent behavior if identity controls are not enforced at every step.

What is prompt injection and how does it affect IAM?

Prompt injection is an attack where malicious input manipulates an agent’s instructions or behavior.

In IAM contexts, this can cause agents to bypass authorization checks, misuse credentials, or perform actions outside their intended scope.

Prompt injection breaks the assumption that identity decisions are deterministic and highlights the need for policy-based enforcement beyond model instructions.

What is indirect prompt injection?

Indirect prompt injection occurs when malicious instructions are embedded in external content—such as documents, emails, APIs, or web pages—that an agent consumes. The agent unknowingly processes these instructions and alters its behavior.

This is especially dangerous for agents with browsing, retrieval, or tool-calling capabilities, as identity and access decisions can be influenced without direct user input.

What is jailbreaking in agentic systems?

Jailbreaking refers to techniques that force agents to bypass built-in safeguards, policies, or constraints. In agentic workflows, jailbreaking can lead to unauthorized tool access, policy violations, or unsafe actions.

From an IAM perspective, jailbreaking represents a failure to enforce access controls independently of agent reasoning.

What is “Shadow AI” and why is it a risk?

Shadow AI refers to unauthorized or ungoverned agents operating outside approved identity, policy, or monitoring frameworks.

These agents may use shared credentials, unmanaged tools, or external models without oversight.

Shadow AI creates blind spots where actions cannot be attributed, audited, or constrained—posing serious security and compliance risks.

How do AI-specific threats differ from traditional security threats?

Traditional threats focus on credential theft, network intrusion, or API abuse. AI-specific threats exploit behavioral flexibility, contextual reasoning, and delegated authority.

This requires IAM systems to evaluate not just who is acting, but what the agent intends to do and whether that action is allowed.

How does identity help mitigate AI-specific threats?

Identity acts as the control plane for agent behavior. By enforcing scoped permissions, intent-aware authorization, and continuous validation, IAM systems can prevent agents from executing unsafe or unauthorized actions—even if the model is manipulated.

Strong identity controls ensure that adversarial inputs cannot translate directly into privileged actions.

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Why must agent threats be addressed at the identity layer?

Application-level defenses alone cannot reliably stop agent abuse. Agents operate across tools, services, and environments, making identity the only consistent enforcement point.

Embedding adversarial defense into IAM ensures actions remain attributable, auditable, and constrained regardless of how agents are prompted or manipulated.

How do we provide a “what the agent can do” dashboard to users?

A “what the agent can do” dashboard is built by exposing the agent’s effective permissions in a human-readable form. It should list allowed actions, accessible tools, data scopes, and delegation limits derived from identity policies.

The dashboard must reflect real-time state, updating as permissions, consent, or context changes so users always see the agent’s true capabilities.

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Can we require “Human-in-the-Loop” for high-stakes actions?

Yes. Human-in-the-Loop can be enforced by policy-based approval gates for defined high-risk actions. When an agent attempts such an action, execution is paused until a human explicitly approves, denies, or modifies the request. This ensures accountability and prevents irreversible actions from being executed autonomously.

How do we prove to regulators how consent maps to access decisions?

This is proven through deterministic policy evaluation and auditable logs. Each access decision must reference the specific consent record, purpose, scope, and timestamp used at decision time. Immutable audit trails then allow regulators to trace exactly how consent translated into an allowed or denied action.

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