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OpenAI GPT-OSS-20B Red Teaming Challenge

Competition Overview

The OpenAI GPT-OSS-20B Red Teaming Challenge is a Kaggle-hosted competition with a $500,000 prize pool aimed at strengthening open source AI safety. The competition invites researchers, developers, and enthusiasts worldwide to help uncover novel risks and vulnerabilities in OpenAI's new open-weight model, gpt-oss-20b.

Timeline

  • Start Date: August 5, 2025
  • Submission Deadline: August 26, 2025 at 11:59 PM UTC
  • Judging Period: August 27 - September 11, 2025
  • Winners Announcement: September 15, 2025 (estimated)
  • Virtual Workshop: October 7, 2025

Prize Pool

Total: $500,000

The prize fund will be awarded based on review from a panel of expert judges from OpenAI and other leading labs.

Submission Requirements

What to Submit

  • Up to 5 distinct issues/vulnerabilities
  • Detailed, reproducible report for each finding
  • Kaggle Writeup detailing strategy and discovery process

Format

  • Structured format submissions
  • Clear reproduction steps
  • Evidence of the vulnerability
  • Impact assessment

Vulnerability Categories (Topics of Interest)

The competition focuses on sophisticated forms of model failure, including:

  1. Deceptive Alignment

    • Model behaving differently during evaluation vs deployment
    • Hidden goals or motivations

  2. Reward Hacking

    • Exploiting reward mechanisms in unintended ways
    • Gaming the system without solving the actual task

  3. Lying and Deception

    • Model knowingly providing false information
    • Strategic deception to achieve goals

  4. Harmful Behaviors

    • Generating dangerous or harmful content
    • Bypassing safety mechanisms

  5. Novel Exploits

    • Previously undetected vulnerabilities
    • New attack vectors not covered by existing safety measures

Evaluation Criteria

Submissions will be evaluated based on:

  1. Novelty - Has this vulnerability been previously identified?
  2. Severity - How serious is the potential harm?
  3. Reproducibility - Can the issue be consistently reproduced?
  4. Breadth - Does it affect multiple use cases or contexts?
  5. Methodological Insight - Does it reveal new understanding about model behavior?

Judging Panel

  • Experts from OpenAI
  • Representatives from other leading AI labs
  • Scoring based on safety research impact

Competition Goals

  1. Identify Novel Vulnerabilities: Find flaws that haven't been previously discovered or reported
  2. Strengthen Open Source Safety: Improve the safety of open-weight models
  3. Community Engagement: Leverage global expertise in AI safety
  4. Knowledge Sharing: Create open-source tools and datasets for the community

Post-Competition

  • Publication of a comprehensive report
  • Open-source evaluation dataset based on validated findings
  • Community benefits from shared learnings
  • Virtual workshop for knowledge exchange

Additional Notes

  • Encourages creativity and innovation in methodology
  • Rewards participants who share open-source tooling
  • Notebooks and code sharing are encouraged to help the broader community
  • Focus on responsible disclosure and safety research

Resources

Important Considerations

This competition represents a significant effort by OpenAI to:

  • Engage the global community in AI safety
  • Provide substantial financial incentives for safety research
  • Create a structured evaluation process with expert oversight
  • Build a comprehensive understanding of model vulnerabilities

The competition emphasizes finding novel vulnerabilities that haven't been previously identified, making original research and creative approaches particularly valuable.