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.
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-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.
These families describe how an attacker approaches your system and what "success" looks like for them. Your tests should mirror those realities.
To intentionally break your system in controlled ways so you can increase robustness, reduce leakage, and prevent policy violations before real users or attackers find them.
Over time, you should monitor for drift after deployment to ensure your AI stays on track. Swept AI provides a comprehensive suite of tools to detect and prevent drift, creating an additional layer of AI supervision.
To intentionally break your system in controlled ways so you can increase robustness, reduce leakage, and prevent policy violations before real users or attackers find them.
Pen testing targets networks and apps. Adversarial testing targets model behavior and AI-specific attack paths, then feeds those results back into training, inference, and guardrails.
Begin with evasion attacks at inference, then expand to targeted versus non-targeted attempts and white-box versus black-box assumptions that mirror your exposure.
Layer tactics: input validation, prompt and policy hardening, adversarial training, rate limiting, anomaly detection, and continuous monitoring. Defense should map to the attack family you face.
At every major change and on a schedule. New data, prompts, or model versions can re-open old wounds. Treat adversarial tests like regression tests that never retire.
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