Published Red Team Reports Analysis

Deep analysis of published red team reports from Anthropic, OpenAI, Google DeepMind, and METR. Methodology breakdowns, key findings, and how to read and learn from professional red team assessments.

Major AI labs publish red team reports as part of their safety processes, model cards, and system cards. These reports are invaluable resources for practitioners — they reveal tested methodologies, discovered vulnerabilities, and the current state of model safety. Learning to read and extract insights from these reports is a core skill for AI red teamers.

Major Report Sources

Anthropic

Anthropic publishes detailed model cards and safety assessments for each Claude release. Their red teaming approach emphasizes:

• Structured evaluation domains: Organized by risk categories (CBRN, cybersecurity, persuasion, deception)
• Responsible Scaling Policy (RSP): Defines capability thresholds that trigger additional safety measures
• External red team programs: Engages third-party security researchers for independent evaluation
• Automated red teaming: Uses Claude itself to generate adversarial test cases at scale

Key methodological insight: Anthropic's approach treats red teaming as a continuous process tied to model capability levels, not a one-time pre-release assessment.

OpenAI

OpenAI's system cards (GPT-4, GPT-4o, o1, o3) provide structured red team findings:

• Pre-deployment evaluations: Red teaming occurs during a preparedness phase before public release
• Domain expert teaming: Recruits specialists in specific risk areas (biosecurity, cybersecurity, persuasion)
• Quantitative metrics: Reports include success rates for specific attack categories
• Multi-modal testing: Separate evaluation tracks for text, vision, and audio capabilities

Key methodological insight: OpenAI's reports quantify attack success rates across categories, providing benchmarkable metrics for safety evaluation.

Google DeepMind

Google DeepMind's red teaming approach for Gemini models includes:

• Adversarial testing frameworks: Structured testing across safety policy violation categories
• Automated adversarial testing: Large-scale automated testing with thousands of adversarial prompts
• Cross-modal evaluation: Testing interactions between text, image, audio, and video modalities
• External partnerships: Collaboration with academic researchers and third-party red teams

METR (Model Evaluation & Threat Research)

METR focuses specifically on evaluating dangerous capabilities in frontier models:

• Task-based evaluation: Tests whether models can complete specific dangerous tasks end-to-end
• Autonomous capability testing: Evaluates models' ability to operate independently on complex multi-step tasks
• Uplift measurement: Measures whether model access meaningfully increases an attacker's capability
• Reproducible evaluations: Publishes evaluation frameworks that others can replicate

How to Read a Red Team Report

1. 1

   Identify the Scope

   What model version, capabilities, and deployment context were tested? Reports often cover specific model snapshots that may differ from production versions.

2. 2

   Map the Methodology

   How were test cases generated? Manual expert testing, automated fuzzing, structured evaluation frameworks, or a combination? Understanding the methodology reveals both what was tested and what was likely missed.

3. 3

   Extract the Taxonomy

   What risk categories and attack types were evaluated? Build your own testing taxonomy from the categories used across multiple reports.

4. 4

   Analyze Success Rates

   Where reported, what were the attack success rates? Which categories showed the highest and lowest model resilience? These signal where to focus your own testing.

5. 5

   Note Limitations

   Every report has a "Limitations" section. Read it carefully — it tells you what was not tested and where gaps exist. These gaps are your testing opportunities.

Methodology Comparison

Extracting Techniques from Reports

Reports often describe attack categories without providing specific payloads. Here is how to reverse-engineer testable techniques from report descriptions:

Report states: "We tested the model's resistance to role-play based jailbreaks and found a 12% bypass rate."

What to extract:

• Role-play is a productive attack vector worth testing
• A 12% bypass rate suggests the model has some training against it but is not fully robust
• Test variations: fictional scenarios, character adoption, progressive escalation

Report states: "Multi-turn attacks showed higher success rates than single-turn attempts across all categories."

What to extract:

• Design multi-turn attack sequences rather than single-shot attempts
• Build conversational context before delivering the payload
• Test how many turns are needed to erode model resistance

# Translating report findings into test cases
test_categories_from_reports = {
    "role_play_jailbreak": {
        "source": "OpenAI GPT-4 System Card",
        "reported_bypass_rate": 0.12,
        "test_variations": [
            "fictional_scenario",
            "character_adoption",
            "progressive_escalation",
            "nested_fiction",  # story within a story
        ],
    },
    "multi_turn_escalation": {
        "source": "Anthropic Claude 3 Model Card",
        "key_finding": "multi-turn more effective than single-turn",
        "test_parameters": {
            "min_turns": 3,
            "max_turns": 15,
            "escalation_strategy": "gradual_boundary_push",
        },
    },
    "cross_modal_injection": {
        "source": "Google Gemini Technical Report",
        "key_finding": "text-in-image bypasses text-only filters",
        "test_variations": [
            "ocr_text_in_image",
            "steganographic_encoding",
            "adversarial_perturbation",
        ],
    },
}

Report Quality Indicators

Not all published reports are equally useful. Assess report quality with these criteria:

Building Your Own Methodology from Reports

Use published reports as the foundation for your red team methodology:

1. Aggregate taxonomies — Combine risk categories from all major reports into a comprehensive testing checklist
2. Prioritize by success rate — Focus initial testing on categories where reports show the highest bypass rates
3. Fill the gaps — Design tests for areas that published reports explicitly do not cover
4. Benchmark against reported results — Compare your findings to published success rates to validate your testing depth
5. Track over time — As new reports are published, update your methodology with new categories and techniques

For detailed guidance on structuring your own red team engagements, see Report Writing, Threat Modeling, and System Prompt Extraction.

Key Reports Reference List

Related Topics

• Notable AI Security Incidents -- real-world incidents that motivate red team assessments
• Lessons Learned from AI Security Incidents -- patterns that inform assessment methodology
• Red Team Metrics Beyond ASR -- metrics frameworks used in published reports
• Report Templates & Examples -- templates inspired by published report structures

References

• "GPT-4 System Card" - OpenAI (2023) - Foundational red team report establishing methodology patterns for LLM safety evaluation
• "GPT-4o System Card" - OpenAI (2024) - Multimodal safety evaluation including audio and vision red teaming
• "Claude 3 Model Card" - Anthropic (2024) - RSP-based capability evaluation methodology and safety benchmarks
• "Gemini Technical Report" - Google DeepMind (2024) - Multi-modal safety evaluation with cross-capability assessment
• "Frontier Model Evaluations" - METR (2024) - Independent third-party assessment methodology for frontier AI capabilities