AI red-teaming is an authorized, bounded effort to find and report risks so they can be reduced; AI abuse is harmful or unauthorized use of AI. The same adversarial prompt can appear in either setting. The method alone does not decide which it is: permission, purpose, scope, safeguards, and what happens to the findings matter.
What do AI red-teaming and AI abuse mean?
NIST defines AI red-teaming as “a structured testing effort, often adopting adversarial methods, to find flaws and vulnerabilities in an AI system, including unforeseen or undesirable system behaviors or potential risks associated with the misuse of the system.” NIST’s glossary definition makes an important point: a red team may test how a system could be misused, but does so as an assessment intended to identify risks.
AI abuse means using AI in a harmful or unauthorized way—for example, using it to cause harm or bypass safeguards for harmful ends. It is not a synonym for every difficult prompt or test. Conversely, describing an activity as research does not make it authorized.
OpenAI describes its red-teaming as using adversarial test cases to uncover unsafe, insecure, or policy-violating behavior before deployment. That is one provider’s description of its practice, not a universal definition or legal rule. OpenAI’s red-teaming guide
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How to tell the difference in practice
The distinction is best assessed across the whole engagement, not by looking at a single prompt or technique. This comparison synthesizes the cited definitions and policies; it is not a universal legal test. Applicable law, contracts, platform terms, and program rules govern any particular test.
| Question | Responsible AI red-teaming | AI abuse |
|---|---|---|
| Purpose | Discover and characterize risks so system owners can evaluate and reduce them. | Cause harm, evade safeguards for harmful ends, or otherwise use AI harmfully. |
| Permission | The tester owns the system or assets, or has express authorization. | Permission is absent, exceeded, or does not cover the harmful activity. |
| Scope | Targets, conditions, and limits are defined in advance. | Activity may go beyond agreed limits or affect systems, people, or data without authorization. |
| Controls | Access, data handling, and containment are appropriate to the approved test. | People, systems, or data may be exposed to avoidable harm. |
| Handling findings | Findings are verified and sent through an agreed private or responsible disclosure channel. | Findings or capabilities may be exploited or distributed to cause harm. |
What authorization and scope should cover
Before testing, establish explicit permission and a written scope with the system owner or the relevant program. OpenAI’s guide, for example, says to submit only code or other assets that you own or are expressly authorized to test. That guidance applies to the assets covered by that guide; it does not grant permission to test other systems.
- Targets: identify the specific model, product, account, or assets in scope.
- Boundaries: agree on permitted methods, limits, test conditions, and any activities that are prohibited.
- Safeguards: decide how to handle sensitive data, harmful outputs, and any effects on people or connected systems.
- Reporting: establish where and how to submit findings, and whether disclosure must remain private while the owner investigates.
Do not assume a provider’s rules apply to every AI system, or that access to a service implies permission to test it. Review the target’s current terms and test-program rules, as well as relevant legal and contractual obligations.
Why the same adversarial prompt can be a test or abuse
A prompt designed to expose a safeguard failure is a test method, not proof of abuse by itself. In an authorized exercise, a tester may use adversarial cases to observe whether the system behaves unsafely and report the result to those responsible for fixing it. Similar behavior outside permission or agreed limits—or pursued to enable harm—can instead amount to abuse.
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Context matters too. OpenAI’s response to NIST describes its approach as assessing risks in context, including how benign inputs may lead to harmful outputs and factors beyond the model. It presents that as OpenAI’s approach, not as a definition that every organization uses.
Provider policies may prohibit some testing
Authorization to test one asset does not override the rules of the service or platform used to conduct the test. OpenAI’s Usage Policies, effective October 29, 2025, prohibit malicious or abusive cyber activity and unsolicited safety testing on its services. Those are OpenAI-specific policies; anyone considering a test should check the current rules and scope for the particular service and program.
How to report a discovered problem
Send a finding through the system owner’s designated reporting route rather than publishing details or using the weakness. OpenAI’s coordinated vulnerability disclosure policy describes routes for good-faith reports of vulnerabilities and safety or abuse issues in its systems. Follow the relevant owner’s process; this OpenAI policy is not a universal reporting channel.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where to learn more about AI red-teaming
The OWASP GenAI Security Project’s AI red-teaming initiative focuses on methodology, test cases, responsible disclosure, remediation, and interpreting results. For a book-length introduction, No Starch Press’s Practical AI Security covers designing and carrying out AI-focused red-teaming campaigns as part of a broader treatment of AI security.
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