Red Teaming Overview

Test your AI system against adversarial attacks using automated red teaming techniques. Simulate real-world attack scenarios to identify vulnerabilities and weaknesses in your system prompt defense. AI Attack Simulation Red Teaming gives you automated adversarial testing with multiple attack techniques. After the testing is complete, a report is generated with the success rates and the interactions that occurred during testing. To help you get started, there are pre-configured Red Team prompt sets provided. See Red Team Prompt Sets for more information.

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What you get

• Automated adversarial testing across multiple attack techniques and tactics.
• Comprehensive attack reports with success rates, failure reasons, and detailed interactions.
• Objective-based testing to verify your system prompt’s robustness against specific threats.

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When to use Red Teaming

• Validating security of an AI system.
• Testing effectiveness of security controls.
• Comparing different prompt versions.
• Compliance and security auditing.

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Important Note on Model Variability and Testing Results

Large Language Models (LLMs) are inherently non-deterministic, meaning they may produce different outputs even when evaluated using the same prompt, model, and policy configuration. This behavior is intentional and results from probabilistic generation techniques that enable models to reason, adapt, and produce natural language responses. As a result, customers may observe variability in attack simulation or testing outcomes across repeated runs. This variability can be more pronounced in reasoning-oriented models, which often operate at higher or fixed temperature settings to enable multi-step reasoning and problem solving. While some models allow configuration to reduce randomness, others (particularly advanced reasoning models) intentionally maintain a level of creativity that limits strict determinism. It is also important to note that model upgrades do not automatically imply improved security. Changes in model behavior, architecture, or prompt handling can introduce new risks or regressions. For this reason, security assumptions should not be carried forward without validation. HiddenLayer strongly encourages continuous testing and red teaming. Due to model evolution, prompt changes, and attacker adaptation, ongoing testing is necessary to maintain effective security. Ongoing evaluation helps surface regressions, identify new attack paths, and ensure that security controls remain effective before changes are deployed to production.