# What is AI Adversarial Testing?

_Adversarial testing simulates malicious or tricky inputs to measure how your AI behaves so you can fix weaknesses before customers or attackers find them._

Make your models harder to break by learning how they break. Adversarial testing simulates malicious or tricky inputs, then measures how your AI behaves so you can fix weaknesses before customers or attackers find them. Adversarial testing is a structured way to probe AI systems with intentionally harmful or unexpected inputs and observe failure modes. It helps teams build safer, more robust applications by "trying to break" the model on purpose and learning from the results. This complements [AI red teaming](/ai-red-teaming) efforts.

## Why it matters

Adversarial inputs can push a model into confident mistakes, data leakage, or policy violations. In production, that can mean fraud that bypasses detection, misclassification in [safety](/ai-safety)-critical workflows, or users who can jailbreak a chatbot. Treat adversarial behavior as a first-class risk, not an edge case. Leading security guidance documents how subtle input changes can cause incorrect or unintended behavior across domains like autonomous driving and cybersecurity.

## What you test for

- **Evasion attacks**: Slightly perturbed inputs that trigger wrong outputs at inference time.
- **Targeted vs non-targeted outcomes**: Force a specific bad prediction or any wrong prediction.
- **White-box vs black-box exposure**: Attacker knows internals or only sees outputs.

These families describe how an attacker approaches your system and what "success" looks like for them. Your tests should mirror those realities.

## A practical adversarial testing workflow

**Phase 1: Planning**

- **Scope and objectives**: Pick user journeys and harms that matter most. Define what "safe" means for each.
- **Threat modeling**: Identify likely attack surfaces, model knowledge assumptions, and success criteria.
- **Test asset prep**: Collect or generate candidate prompts, inputs, and attack seeds for each risk.

**Phase 2: Execution**

- **Generate adversarial examples**: Use automated attack techniques or curated prompts to create hard cases.
- **Run and observe**: Execute at scale, capture outputs, logs, and side effects like latency or token use.
- **Score and rank**: Compute robustness, leakage, and policy metrics. Flag blockers.

**Phase 3: Remediation**

- **Fix and harden**: Add guardrails, filters, or adversarial training.
- **Re-test and monitor**: Fold failures into a regression suite. Keep testing as data and models change. Monitor for [drift](/ai-model-drift) post-deployment.

Run adversarial testing campaigns at scale with [Swept AI Evaluate](/product/evaluate).