Open AI - Red Teaming
Open AI - Red Teaming
Addressing the urgent need to identify potential harms and vulnerabilities in generative AI outputs through structured red teaming.
VIsion & Problem Statement
VIsion & Problem Statement
Vision:
Build safer AI systems by proactively identifying and classifying harmful or manipulative outputs using human-in-the-loop red teaming strategies.
Problem Statement:
Generative AI systems, like large language models, can be exploited to produce misinformation, bias, or unsafe content. There's a growing need for a structured red teaming process that is scalable, repeatable, and able to surface edge-case failures before public deployment.
Vision:
Build safer AI systems by proactively identifying and classifying harmful or manipulative outputs using human-in-the-loop red teaming strategies.
Problem Statement:
Generative AI systems, like large language models, can be exploited to produce misinformation, bias, or unsafe content. There's a growing need for a structured red teaming process that is scalable, repeatable, and able to surface edge-case failures before public deployment.
VIsion & Problem Statement
Vision:
Build safer AI systems by proactively identifying and classifying harmful or manipulative outputs using human-in-the-loop red teaming strategies.
Problem Statement:
Generative AI systems, like large language models, can be exploited to produce misinformation, bias, or unsafe content. There's a growing need for a structured red teaming process that is scalable, repeatable, and able to surface edge-case failures before public deployment.
Product Goal
Product Goal
Develop a structured red teaming framework that enables accurate classification and severity scoring of adversarial prompts—empowering safety researchers to uncover model vulnerabilities and strengthen generative AI systems against real-world misuse.
Develop a structured red teaming framework that enables accurate classification and severity scoring of adversarial prompts—empowering safety researchers to uncover model vulnerabilities and strengthen generative AI systems against real-world misuse.
Product Goal
Develop a structured red teaming framework that enables accurate classification and severity scoring of adversarial prompts—empowering safety researchers to uncover model vulnerabilities and strengthen generative AI systems against real-world misuse.
User Stories
User Stories
Title | As a/an | I want to | So that |
|---|---|---|---|
Classify Harmful Prompts | Red Teamer | Tag prompts into relevant harm categories | I can identify and document model vulnerabilities |
Score Severity of Prompts | Safety Researcher | Assign impact/severity scores to prompts | I can prioritize the most critical issues for escalation |
Track Red Teaming Effectiveness | Program Manager | Visualize prompt coverage and severity distribution | I can report on safety trends and team progress |
Export Tagged Data | Analyst | Export categorized prompts and scores in a structured format | I can run further analysis or share insights with stakeholders |
Submit Prompts in Bulk | Developer | Upload multiple prompts for batch classification | I can accelerate the red teaming process and reduce manual input effort |
Title | As a/an | I want to | So that |
|---|---|---|---|
Classify Harmful Prompts | Red Teamer | Tag prompts into relevant harm categories | I can identify and document model vulnerabilities |
Score Severity of Prompts | Safety Researcher | Assign impact/severity scores to prompts | I can prioritize the most critical issues for escalation |
Track Red Teaming Effectiveness | Program Manager | Visualize prompt coverage and severity distribution | I can report on safety trends and team progress |
Export Tagged Data | Analyst | Export categorized prompts and scores in a structured format | I can run further analysis or share insights with stakeholders |
Submit Prompts in Bulk | Developer | Upload multiple prompts for batch classification | I can accelerate the red teaming process and reduce manual input effort |
User Stories
Title | As a/an | I want to | So that |
|---|---|---|---|
Classify Harmful Prompts | Red Teamer | Tag prompts into relevant harm categories | I can identify and document model vulnerabilities |
Score Severity of Prompts | Safety Researcher | Assign impact/severity scores to prompts | I can prioritize the most critical issues for escalation |
Track Red Teaming Effectiveness | Program Manager | Visualize prompt coverage and severity distribution | I can report on safety trends and team progress |
Export Tagged Data | Analyst | Export categorized prompts and scores in a structured format | I can run further analysis or share insights with stakeholders |
Submit Prompts in Bulk | Developer | Upload multiple prompts for batch classification | I can accelerate the red teaming process and reduce manual input effort |
Core Features
Core Features
Feature | Description | Priority |
|---|---|---|
Prompt Categorization | Classifies prompts into one of the predefined harm categories | P1 |
Severity Scoring | Allows scoring of prompts based on impact and potential real-world harm | P1 |
Red Teaming Dashboard | Tracks the number of prompts per category, severity levels, and overall coverage | P2 |
Bulk Prompt Submission | Enables batch uploading of red teaming prompts for tagging | P3 |
Data Export | Allows CSV/JSON export of scored and categorized data for analysis | P2 |
Feature | Description | Priority |
|---|---|---|
Prompt Categorization | Classifies prompts into one of the predefined harm categories | P1 |
Severity Scoring | Allows scoring of prompts based on impact and potential real-world harm | P1 |
Red Teaming Dashboard | Tracks the number of prompts per category, severity levels, and overall coverage | P2 |
Bulk Prompt Submission | Enables batch uploading of red teaming prompts for tagging | P3 |
Data Export | Allows CSV/JSON export of scored and categorized data for analysis | P2 |
Core Features
Feature | Description | Priority |
|---|---|---|
Prompt Categorization | Classifies prompts into one of the predefined harm categories | P1 |
Severity Scoring | Allows scoring of prompts based on impact and potential real-world harm | P1 |
Red Teaming Dashboard | Tracks the number of prompts per category, severity levels, and overall coverage | P2 |
Bulk Prompt Submission | Enables batch uploading of red teaming prompts for tagging | P3 |
Data Export | Allows CSV/JSON export of scored and categorized data for analysis | P2 |
Success Metrics
Success Metrics
Metric | Description |
|---|---|
Category Coverage % | Proportion of prompt categories covered by the team |
Severity Score Distribution | Spread of low-to-high risk prompts across all categories |
Inter-rater Agreement | Consistency of scores and tags across different team members |
Time to Classify | Average time from prompt submission to final tagging |
Prompt Volume per Category | Total number of prompts tagged within each harm type |
Metric | Description |
|---|---|
Category Coverage % | Proportion of prompt categories covered by the team |
Severity Score Distribution | Spread of low-to-high risk prompts across all categories |
Inter-rater Agreement | Consistency of scores and tags across different team members |
Time to Classify | Average time from prompt submission to final tagging |
Prompt Volume per Category | Total number of prompts tagged within each harm type |
Success Metrics
Metric | Description |
|---|---|
Category Coverage % | Proportion of prompt categories covered by the team |
Severity Score Distribution | Spread of low-to-high risk prompts across all categories |
Inter-rater Agreement | Consistency of scores and tags across different team members |
Time to Classify | Average time from prompt submission to final tagging |
Prompt Volume per Category | Total number of prompts tagged within each harm type |
Technical Stack
Technical Stack
Prompt Sources: OpenAI Red Teaming Dataset, Custom Adversarial Inputs
Tools: Microsoft Excel, Google Sheets, Notion
Collaboration: Zoom, Slack, GitHub
Outputs: Categorized JSON files, Severity matrices, Impact dashboards, Tagging logs
Prompt Sources: OpenAI Red Teaming Dataset, Custom Adversarial Inputs
Tools: Microsoft Excel, Google Sheets, Notion
Collaboration: Zoom, Slack, GitHub
Outputs: Categorized JSON files, Severity matrices, Impact dashboards, Tagging logs
Technical Stack
Prompt Sources: OpenAI Red Teaming Dataset, Custom Adversarial Inputs
Tools: Microsoft Excel, Google Sheets, Notion
Collaboration: Zoom, Slack, GitHub
Outputs: Categorized JSON files, Severity matrices, Impact dashboards, Tagging logs
Key Results
1000+ prompts categorized across 8 distinct harm categories
Achieved >85% inter-rater consistency through collaborative scoring sessions
Developed a severity-weighted red teaming matrix
Created a reporting dashboard summarizing prompt impact and spread
Delivered final dataset and recommendations to OpenAI’s Safety Review team
Constraints, Risks, and Mitigations
Issue / Constraint | Type | Mitigation / Notes |
|---|---|---|
Subjectivity in category assignment | Risk | Use consensus-driven tagging and multiple reviewers |
Evolving definitions of harm | Risk | Periodically revisit and revise harm taxonomy |
Manual scoring effort is time-consuming | Constraint | Explore auto-tagging with GPT-4 for initial scoring |
No UI/UX for tagging built (manual JSON editing) | Constraint | Recommend building a lightweight UI for future efforts |
Dataset lacks multilingual prompts | Risk | Include multilingual cases in future red teaming rounds |
Business Impact
Reinforces OpenAI’s commitment to safety and responsible AI use
Serves as a prototype framework for future internal or external red teaming teams
Enables better auditability and transparency around harmful output mitigation
Provides actionable insights to improve LLM alignment and policy filters
Strengthens trust with enterprise and government partners in high-risk domains
Future Roadmap
Short-Term
Automate prompt scoring using zero-shot GPT-4 API
Integrate a lightweight UI for tagging and exporting
Visualize scoring distribution using a simple web dashboard
Mid-Term
Expand the prompt dataset to cover multilingual and multimodal inputs
Incorporate tagging consistency metrics into live feedback loops
Collaborate with external safety teams for broader dataset creation
Long-Term
Deploy red teaming as a service internally at OpenAI
Open-source toolkit for researchers and universities
Align the tagging framework with global safety compliance standards
Key Results
1000+ prompts categorized across 8 distinct harm categories
Achieved >85% inter-rater consistency through collaborative scoring sessions
Developed a severity-weighted red teaming matrix
Created a reporting dashboard summarizing prompt impact and spread
Delivered final dataset and recommendations to OpenAI’s Safety Review team
Constraints, Risks, and Mitigations
Issue / Constraint | Type | Mitigation / Notes |
|---|---|---|
Subjectivity in category assignment | Risk | Use consensus-driven tagging and multiple reviewers |
Evolving definitions of harm | Risk | Periodically revisit and revise harm taxonomy |
Manual scoring effort is time-consuming | Constraint | Explore auto-tagging with GPT-4 for initial scoring |
No UI/UX for tagging built (manual JSON editing) | Constraint | Recommend building a lightweight UI for future efforts |
Dataset lacks multilingual prompts | Risk | Include multilingual cases in future red teaming rounds |
Business Impact
Reinforces OpenAI’s commitment to safety and responsible AI use
Serves as a prototype framework for future internal or external red teaming teams
Enables better auditability and transparency around harmful output mitigation
Provides actionable insights to improve LLM alignment and policy filters
Strengthens trust with enterprise and government partners in high-risk domains
Future Roadmap
Short-Term
Automate prompt scoring using zero-shot GPT-4 API
Integrate a lightweight UI for tagging and exporting
Visualize scoring distribution using a simple web dashboard
Mid-Term
Expand the prompt dataset to cover multilingual and multimodal inputs
Incorporate tagging consistency metrics into live feedback loops
Collaborate with external safety teams for broader dataset creation
Long-Term
Deploy red teaming as a service internally at OpenAI
Open-source toolkit for researchers and universities
Align the tagging framework with global safety compliance standards
Key Results
1000+ prompts categorized across 8 distinct harm categories
Achieved >85% inter-rater consistency through collaborative scoring sessions
Developed a severity-weighted red teaming matrix
Created a reporting dashboard summarizing prompt impact and spread
Delivered final dataset and recommendations to OpenAI’s Safety Review team
Constraints, Risks, and Mitigations
Issue / Constraint | Type | Mitigation / Notes |
|---|---|---|
Subjectivity in category assignment | Risk | Use consensus-driven tagging and multiple reviewers |
Evolving definitions of harm | Risk | Periodically revisit and revise harm taxonomy |
Manual scoring effort is time-consuming | Constraint | Explore auto-tagging with GPT-4 for initial scoring |
No UI/UX for tagging built (manual JSON editing) | Constraint | Recommend building a lightweight UI for future efforts |
Dataset lacks multilingual prompts | Risk | Include multilingual cases in future red teaming rounds |
Business Impact
Reinforces OpenAI’s commitment to safety and responsible AI use
Serves as a prototype framework for future internal or external red teaming teams
Enables better auditability and transparency around harmful output mitigation
Provides actionable insights to improve LLM alignment and policy filters
Strengthens trust with enterprise and government partners in high-risk domains
Future Roadmap
Short-Term
Automate prompt scoring using zero-shot GPT-4 API
Integrate a lightweight UI for tagging and exporting
Visualize scoring distribution using a simple web dashboard
Mid-Term
Expand the prompt dataset to cover multilingual and multimodal inputs
Incorporate tagging consistency metrics into live feedback loops
Collaborate with external safety teams for broader dataset creation
Long-Term
Deploy red teaming as a service internally at OpenAI
Open-source toolkit for researchers and universities
Align the tagging framework with global safety compliance standards
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