---
id: llm-red-teaming-and-adversarial-evaluation
title: 'LLM Red Teaming and Adversarial Evaluation'
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: '2026-06-02'
created_date: '2026-06-02'
generation_method: ai_structured
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-ai-llm-red-teaming-and-adversarial-evaluation-1
    statement: >-
      NIST AI Risk Management Framework material identifies NIST-AI-600-1 as a generative AI profile released in 2024.
    source_title: NIST AI Risk Management Framework
    source_url: https://www.nist.gov/itl/ai-risk-management-framework
    confidence: medium
  - id: fact-ai-llm-red-teaming-and-adversarial-evaluation-2
    statement: >-
      Google describes AI red teaming as a capability for simulating attacks against AI systems.
    source_title: Google AI Red Team
    source_url: https://blog.google/technology/safety-security/googles-ai-red-team-the-ethical-hackers-making-ai-safer/
    confidence: medium
  - id: fact-ai-llm-red-teaming-and-adversarial-evaluation-3
    statement: >-
      OWASP publishes the Top 10 for Large Language Model Applications as a list of LLM application security risks.
    source_title: OWASP Top 10 for LLM Applications
    source_url: https://genai.owasp.org/llm-top-10/
    confidence: medium
completeness: 0.82
known_gaps:
  - Red-team findings are scenario-specific and should be connected to mitigation owners, regression tests, and release gates.
disputed_statements: []
primary_sources:
  - title: NIST AI Risk Management Framework
    type: government_reference
    year: 2026
    url: https://www.nist.gov/itl/ai-risk-management-framework
    institution: NIST
  - title: Google AI Red Team
    type: official_blog
    year: 2023
    url: https://blog.google/technology/safety-security/googles-ai-red-team-the-ethical-hackers-making-ai-safer/
    institution: Google
  - title: OWASP Top 10 for LLM Applications
    type: documentation
    year: 2026
    url: https://genai.owasp.org/llm-top-10/
    institution: OWASP
secondary_sources: []
updated: '2026-06-02'
ai_models:
  - gpt-5-codex
---

## TL;DR

LLM red teaming is adversarial evaluation for model and application behavior, especially around jailbreaks, prompt injection, data leakage, unsafe tool use, and policy bypass.

## Core Explanation

Standard eval sets usually measure expected behavior. Red teaming asks what happens when the user, retrieved document, tool output, or environment is adversarial. This matters for agents because tool access increases impact beyond text generation.

Useful red-team work produces reproducible cases, severity labels, mitigations, and regression tests. A finding that cannot be replayed or tied to a release gate is weak operational evidence.

## Source-Mapped Facts

- NIST AI Risk Management Framework material identifies NIST-AI-600-1 as a generative AI profile released in 2024. ([source](https://www.nist.gov/itl/ai-risk-management-framework))
- Google describes AI red teaming as a capability for simulating attacks against AI systems. ([source](https://blog.google/technology/safety-security/googles-ai-red-team-the-ethical-hackers-making-ai-safer/))
- OWASP publishes the Top 10 for Large Language Model Applications as a list of LLM application security risks. ([source](https://genai.owasp.org/llm-top-10/))

## Further Reading

- [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework)
- [Google AI Red Team](https://blog.google/technology/safety-security/googles-ai-red-team-the-ethical-hackers-making-ai-safer/)
- [OWASP Top 10 for LLM Applications](https://genai.owasp.org/llm-top-10/)