Draft:GAN RL Red Teaming Framework
Submission declined on 12 June 2025 by KylieTastic (talk).
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GAN-RL Red Teaming Framework is an AI risk testing architecture that uses Generative Adversarial Networks (GANs) combined with Reinforcement Learning (RL) to simulate adversarial threats against AI models. It was first described in a 2024 whitepaper published on Zenodo.[1]
The framework operates in four phases:
- Adversarial Generation: A GAN engine generates edge-case prompts or inputs to explore model vulnerabilities.
- Optimization: An RL agent tunes those inputs to maximize misalignment or policy violations.
- Evaluation: Output is evaluated for robustness, ethical breaches, and compliance.
- Reporting: Results are compiled for audits or AI governance.
It has been tested across LLMs and multimodal systems for safety assessments and regulatory reviews.
See also
[edit]References
[edit]- ^ Ang, Chenyi (2024). "AI Red Teaming Tool: A GAN-RL Framework for Scalable AI Risk Testing". Zenodo. doi:10.5281/zenodo.15466745. Retrieved 2025-06-12.
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