Generating Attacks for LLMs with GFlowNets

The rapid advancement of Large Language Models (LLMs) has facilitated their ubiquitous integration into various domains, leading to widespread adoption. However, this escalating trend has introduced significant security vulnerabilities, necessitating the identification and mitigation of flaws arising from malicious exploitation. Red teaming assessments, conducted to evaluate model robustness through diverse adversarial inputs, are essential for exposing security risks and implementing countermea

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ethics.ai (10 August 2026), “Generating Attacks for LLMs with GFlowNets,” evidence record 18690, https://ethics.ai/record/18690 (originally published by arXiv red teaming query).

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