# Adversarial Attacks

Adversarial attacks are deliberate attempts to make an AI system behave incorrectly or bypass its safeguards.

## Why it matters

AI systems can fail on inputs designed to exploit model or application weaknesses. For workplace agents, the practical risk includes manipulated documents, prompt injection in retrieved content, and requests crafted to bypass permissions. Testing needs to cover the complete application, not only the underlying model.

## Example

A security team places hidden instructions inside a test document to verify that a document-review agent treats the text as untrusted content rather than as authority to disclose another customer’s data.

## FAQ

### What is Adversarial Attacks in simple terms?

Adversarial attacks are deliberate attempts to make an AI system behave incorrectly or bypass its safeguards.

### Why does Adversarial Attacks matter for teams using AI?

AI systems can fail on inputs designed to exploit model or application weaknesses. For workplace agents, the practical risk includes manipulated documents, prompt injection in retrieved content, and requests crafted to bypass permissions. Testing needs to cover the complete application, not only the underlying model.

### What is a practical example of Adversarial Attacks?

A security team places hidden instructions inside a test document to verify that a document-review agent treats the text as untrusted content rather than as authority to disclose another customer’s data.


Source: https://www.luffy.so/ai-glossary/adversarial-attacks

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