# AI Red Teaming: Security Testing for Language Models
Status: public
Confidence: high (0.865) (verified)
Last verified: 2026-05-28
Generation: ai_structured


## TL;DR

AI red teaming uses adversarial testing to find unsafe, vulnerable, or policy-violating behavior in AI systems before deployment.

## Core Explanation

The repaired evidence focuses on model-generated red-team test cases, Constitutional AI training, and OWASP's application-security risk taxonomy for LLM systems.

## Detailed Analysis

The prior version leaned on broad survey claims and overgeneralized defense language. Current claims avoid saying that any defense is complete and keep specific risks attached to OWASP's named categories.

## Further Reading

- [Red Teaming Language Models with Language Models](https://arxiv.org/abs/2202.03286)
- [Constitutional AI](https://arxiv.org/abs/2212.08073)
- [OWASP Top 10 for LLM Applications](https://owasp.org/www-project-top-10-for-large-language-model-applications/)

## Related Articles

- [Large Language Models (LLMs)](../llms.md)
- [Long-Context Language Models: Beyond 1M Tokens](../long-context-models.md)
- [LoRA: Low-Rank Adaptation of Large Language Models](../lora-low-rank-adaptation-of-large-language-models.md)