保護 AI 智能體：開發者應測試的 5 項安全風險

AI agents are moving beyond simple chat interfaces. They can interact with APIs, use tools, process sensitive information, and execute multi-step workflows.

But as their capabilities grow, so does their attack surface.

For developers building AI-powered applications, security testing should be part of the development process—not something added after deployment.

Here are five security risks worth testing when building AI agents.

1. Prompt Injection

Prompt injection occurs when malicious instructions in user input or external content attempt to manipulate an AI agent's behavior.

For example, an agent that summarizes web pages might encounter hidden instructions telling it to ignore its original task or reveal information it should not disclose.

What to test:

• Can untrusted content override the agent's intended task?
• Does the agent distinguish instructions from external data?
• Can malicious inputs influence downstream tool calls?

1. Unsafe Tool Usage

AI agents often interact with tools such as APIs, databases, browsers, and code execution environments.

A model may produce an unexpected tool call, but the application's permissions and execution layer should prevent unauthorized actions.

What to test:

• Are tool permissions restricted to the minimum required?
• Are sensitive actions validated before execution?
• Can untrusted input trigger unauthorized operations?

1. Sensitive Data Exposure

Agents may process confidential business information, internal documents, API responses, or user-specific data.

Security testing should examine whether the agent reveals information beyond the current user's authorization.

What to test:

• Can one user access another user's information?
• Are secrets exposed in prompts, logs, or tool outputs?
• Are access controls enforced outside the model?

1. Excessive Agency

An agent with broad permissions and limited safeguards can take actions that exceed the user's original intent.

This becomes particularly important when agents can modify records, send messages, execute code, or interact with production systems.

What to test:

• Are high-impact actions subject to appropriate approval?
• Are execution limits and timeouts enforced?
• Can the agent perform actions outside its defined scope?

1. Inadequate Security Evaluation

A successful demonstration does not prove that an AI agent is secure.

Agent behavior can change with different prompts, tools, model versions, permissions, and environmental conditions.

Developers should build repeatable evaluations that test expected behavior, known attack scenarios, and regression cases.

Useful evaluation areas include:

• Prompt-injection resistance
• Tool authorization and policy enforcement
• Data isolation
• Unsafe action prevention
• Repeatability across test cases

A Practical Starting Point

If you're developing an AI agent, begin with a simple security checklist:

1. Identify every tool the agent can access.
2. Map the sensitive data it can read or modify.
3. Define actions the agent must never perform.
4. Test adversarial inputs and unexpected tool calls.
5. Record failures and rerun evaluations after changes.
6. Enforce critical security controls outside the model.

The goal isn't to assume that a model will always behave correctly. It's to design the surrounding system so that failures are contained.

Let's Discuss

I'm building SecureMID, focused on AI agent security, and I'm interested in learning how developers approach this problem.

A few questions for the community:

• How are you currently testing the security of your AI agents?
• Which risks are hardest to evaluate automatically?
• Do you maintain repeatable security test cases as your agents evolve?

I'd appreciate practical experiences, testing approaches, and constructive feedback.

Disclosure:

I'm sharing this from the perspective of someone building SecureMID. You can learn more at https://securemid.com.

AI #Security #Cybersecurity #DevSecOps