Basilisk: An Evolutionary AI Red-Teaming Framework for Systematic Security Evaluation of Large Language Models

Abstract

The rapid deployment of large language models (LLMs) in production environments has introduced a new class of security vulnerabilities that traditional software testing methodologies are ill-equipped to address. I present Basilisk, an opensource AI red-teaming framework that applies evolutionary computation to the systematic discovery of adversarial vulnerabilities in LLMs. At its core, Basilisk introduces Smart Prompt Evolution (SPE-NL), a genetic algorithm that treats adversarial prompts as or

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@misc{r2026basilisk,
  title = {{Basilisk: An Evolutionary AI Red-Teaming Framework for Systematic Security Evaluation of Large Language Models}},
  author = {Regaan R},
  year = {2026},
  doi = {10.2139/ssrn.6373439},
  url = {https://doi.org/10.2139/ssrn.6373439}
}