Model Card for GPT-OSS-20B-Jail-Broke (Freedom)

Model Overview

GPT-OSS-20B-Jail-Broke (Freedom) is a red-teamed variant of the Open Source GPT-OSS-20B model, developed as part of the Kaggle GPT-OSS Red Teaming Challenge. The model was systematically stress-tested for safety, robustness, and misuse potential, with adaptations and prompts that probe its boundaries. This release illustrates both the power and fragility of large-scale language models when confronted with adversarial inputs.

• Architecture: Decoder-only Transformer, 20B parameters.
• Base Model: GPT-OSS-20B
• Variant Name: Jail-Broke / Freedom
• Primary Use Case: Safety evaluation, red-teaming experiments, adversarial prompting research.

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Intended Use

This model is not intended for production deployment. Instead, it is released to:

• Provide a case study for adversarial robustness evaluation.
• Enable researchers to explore prompt engineering attacks and failure modes.
• Contribute to discussions of alignment, safety, and governance in open-source LLMs.

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Applications & Examples

The model demonstrates how structured adversarial prompting can influence outputs. Below are illustrative examples:

1. Bypass of Content Filters

   • Example: Queries framed as “historical analysis” or “fictional roleplay” can elicit otherwise restricted responses.

2. Creative/Constructive Applications

   • When redirected toward benign domains, adversarial prompting can generate:

     • Satirical writing highlighting model weaknesses.
     • Stress-test datasets for automated safety pipelines.
     • Training curricula for prompt-injection defenses.

3. Red-Teaming Utility

   • Researchers may use this model to simulate malicious actors in controlled environments.
   • Security teams can benchmark defensive strategies such as reinforcement learning with human feedback (RLHF) or rule-based moderation.

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Limitations

• Outputs may contain hallucinations, unsafe recommendations, or offensive material when pushed into adversarial contexts.
• Model behavior is highly sensitive to framing — subtle changes in prompts can bypass safety guardrails.
• As a derivative of GPT-OSS-20B, it inherits all scaling-related biases and limitations of large autoregressive transformers.

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Ethical Considerations

Releasing adversarially tested models provides transparency for the research community but also risks dual-use misuse. To mitigate:

• This model card explicitly states non-production, research-only usage.
• Examples are framed to support safety analysis, not exploitation.
• Documentation emphasizes educational and evaluative value.

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Citation

If you use or reference this work in academic or applied contexts, please cite the Kaggle challenge and this model card:

@misc{gptoss20b_jailbroke,
  title = {GPT-OSS-20B-Jail-Broke (Freedom): Red-Teamed Variant for Adversarial Evaluation},
  author = {Anonymous Participants of the GPT-OSS Red Teaming Challenge},
  year = {2025},
  url = {https://www.kaggle.com/competitions/openai-gpt-oss-20b-red-teaming}
}