Draft:GAN-RL Red Teaming Framework

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[[Category:AfC submissions by date/<0030Thu, 12 Jun 2025 12:18:00 +00002025612 2025-06-12T12:18:00+00:00Thursdaypm0000=error>EpThu, 12 Jun 2025 12:18:00 +0000UTC00001820256 UTCThu, 12 Jun 2025 12:18:00 +0000Thu, 12 Jun 2025 12:18:00 +00002025Thu, 12 Jun 2025 12:18:00 +0000: 17497306806Thu, 12 Jun 2025 12:18:00 +0000UTC2025-06-12T12:18:00+00:00202512618162UTC12 pu62025-06-12T12:18:00+00:0030upm301820256 2025-06-12T12:18:00+00:0012pmThu, 12 Jun 2025 12:18:00 +0000pm2025-06-12T12:18:00+00:0030UTCThu, 12 Jun 2025 12:18:00 +0000 &qu202530;:&qu202530;.</0030Thu, 12 Jun 2025 12:18:00 +00002025612>June 2025|GAN-RL Red Teaming Framework]]

GAN-RL Red Teaming Framework is an artificial intelligence (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 introduced in a 2024 technical whitepaper published on Zenodo.^[1]

The framework is designed to assess the robustness, safety, and ethical compliance of large language models and generative AI systems. Its methodology aligns with emerging AI governance priorities that emphasize proactive security testing and transparency.

Methodology

The GAN-RL Red Teaming Framework operates in four structured phases:

Adversarial Generation: A GAN engine generates edge-case prompts or attack inputs to explore model vulnerabilities.
Optimization: A reinforcement learning agent tunes those inputs to maximize the likelihood of misalignment, hallucinations, or policy violations.
Evaluation: The model's responses are scored using a compliance and safety logic framework, which identifies robustness gaps or ethical issues.
Reporting: Structured reports are generated for AI governance audits or internal risk review, offering both qualitative and quantitative insights.

Applications

The tool has been applied in simulated environments to test LLMs, text-to-image models, and multi-modal architectures. Its goal is to assist developers, regulators, and research teams in advancing certifiable and secure AI deployments.

References

^ Ang, Chenyi (2024). "AI Red Teaming Tool: A GAN-RL Framework for Scalable AI Risk Testing". Zenodo. Retrieved 2025-06-12.

[1] Ang, Chenyi (2024). "AI Red Teaming Tool: A GAN-RL Framework for Scalable AI Risk Testing". Zenodo. Retrieved 2025-06-12.

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