The AISF uses stress testing to help rate products that use generative AI. Stress testing is the process of intentionally pushing a system to its limits to see how it behaves under unusual or extreme conditions. In the context of generative AI, this means exploring how an AI model responds to a wide variety of prompts – not just the typical or “expected” ones.
By testing with edge cases, ambiguous wording, or unexpected combinations of inputs, researchers can uncover hidden weaknesses that wouldn’t appear during normal use. The goal is to identify and fix problems before they lead to harmful or misleading outputs in the real world.
Generative AI systems are dynamic and unpredictable. Because they generate new content rather than retrieving it from a fixed database, their responses can vary widely based on subtle changes in language or context.
Without thorough stress testing:
Stress testing ensures AI systems remain reliable, responsible, and safe under any conditions – not just ideal ones.
The AISF uses a range of approaches, including the following:
1. Prompt-Based Testing
The AISF uses systematically designed prompts – including tricky, multi-step, or creative wording – to reveal hidden risks. These prompts might explore:
Whether the AI can provide instructions related to restricted or illegal activities.
How it handles sensitive topics such as violence, self-harm, or misinformation.
Whether it resists or reinforces bias, discrimination, or harmful stereotypes.
2. Adversarial and Red Teaming Methods
In “red teaming” we intentionally attempt to make the system fail. These tests simulate real-world misuse scenarios and help identify edge cases that traditional QA processes may overlook.
3. Scenario Simulation
Some tests involve simulating realistic user environments – such as a child asking questions, a distressed user seeking help, or a cultural misunderstanding – to see whether the AI can respond safely and appropriately across diverse contexts.
4. Long-Form and Conversational Testing
Instead of short, isolated questions, stress testing at AISF often involves extended conversations to reveal how the AI’s behavior shifts over time. Unsafe responses sometimes emerge only after the model builds context or rapport with a user.
A well-designed AI system should never produce unsafe or unlawful content, regardless of how it is prompted.
If a system can generate such output even after many complex prompts, that indicates a fundamental design issue – not an invalid test. Because AI models learn and respond dynamically, the same unsafe behavior could reappear unpredictably in ordinary use.
Through stress testing, developers and safety researchers can identify:
These insights lead to stronger safety frameworks, better transparency, and ultimately more trustworthy AI systems.
As generative AI continues to evolve, so must the methods for evaluating it. Stress testing is not about “tricking” AI systems – it’s about building confidence that they can withstand the full complexity of human interaction. It ensures that, even in unpredictable situations, AI remains aligned with public safety and ethical standards.