---
id: ai-red-teaming-and-safety
title: "AI Red Teaming: Security Testing for Language Models"
schema_type: TechArticle
category: ai
language: en
confidence: high
last_verified: "2026-05-28"
created_date: "2026-05-24"
generation_method: ai_structured
ai_models:
  - claude-opus
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: f1
    statement: "Perez et al. studied using one language model to generate adversarial test cases for red teaming another language model."
    source_title: Red Teaming Language Models with Language Models
    source_url: https://arxiv.org/abs/2202.03286
    confidence: high
  - id: f2
    statement: "Constitutional AI uses a set of written principles to guide model self-critique and revision during harmlessness training."
    source_title: Constitutional AI Harmlessness from AI Feedback
    source_url: https://arxiv.org/abs/2212.08073
    confidence: high
  - id: f3
    statement: "OWASP's 2025 Top 10 for LLM Applications identifies prompt injection and sensitive information disclosure among the major LLM application risks."
    source_title: OWASP Top 10 for LLM Applications
    source_url: https://owasp.org/www-project-top-10-for-large-language-model-applications/
    confidence: high
completeness: 0.85
known_gaps:
  - Automated red teaming at scale
  - Defense-in-depth for multimodal models
disputed_statements: []
primary_sources:
  - title: Red Teaming Language Models with Language Models
    type: academic_paper
    year: 2022
    url: https://arxiv.org/abs/2202.03286
    institution: Anthropic / arXiv
  - title: Constitutional AI Harmlessness from AI Feedback
    type: academic_paper
    year: 2022
    url: https://arxiv.org/abs/2212.08073
    institution: Anthropic / arXiv
  - title: OWASP Top 10 for LLM Applications
    type: standard
    year: 2025
    url: https://owasp.org/www-project-top-10-for-large-language-model-applications/
    institution: OWASP
secondary_sources:
  - title: OpenAI's approach to external red teaming for AI systems
    type: report
    year: 2024
    url: https://openai.com/index/external-red-teaming/
    institution: OpenAI
updated: "2026-05-28"
---

## 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)