Avaliação de LLMS na detecção de vulnerabilidades em contratos inteligentes
DOI:
https://doi.org/10.22481/recic.v8i1.18263Palavras-chave:
Contratos inteligentes, LLMs, blockchain, segurança, vulnerabilidadesResumo
A auditoria de contratos inteligentes é essencial para garantir a segurança de aplicações descentralizadas, prevenindo falhas críticas em ambientes imutáveis como blockchains. Este estudo tem como objetivo avaliar a eficácia de Modelos de Linguagem de Grande Escala (LLMs) na detecção automatizada de vulnerabilidades em contratos inteligentes. Para tanto, 40 contratos de diferentes categorias foram analisados por quatro LLMs — GPT-4o, DeepSeek-R1, Llama-3.3 e Gemini 2.0 Flash — utilizando um prompt unificado para extrair e classificar vulnerabilidades por severidade. O desempenho foi mensurado por precisão, recall, F1-Score e erro médio absoluto, comparando as detecções com auditorias de referência. O melhor modelo alcançou 10,36% de precisão e 22,48% de recall, indicando que os LLMs ainda requerem aprimoramentos para uso autônomo confiável.
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