Papers › Detect Llama -- Finding Vulnerabilities in Smart Contracts using Large Language Models
Detect Llama -- Finding Vulnerabilities in Smart Contracts using Large Language Models
Peter Ince, Xiapu Luo, Jiangshan Yu, Joseph K. Liu, Xiaoning Du
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
In this paper, we test the hypothesis that although OpenAI's GPT-4 performs well generally, we can fine-tune open-source models to outperform GPT-4 in smart contract vulnerability detection. We fine-tune two models from Meta's Code Llama and a dataset of 17k prompts, Detect Llama - Foundation and Detect Llama - Instruct, and we also fine-tune OpenAI's GPT-3.5 Turbo model (GPT-3.5FT). We then evaluate these models, plus a random baseline, on a testset we develop against GPT-4, and GPT-4 Turbo's, detection of eight vulnerabilities from the dataset and the two top identified vulnerabilities - and their weighted F1 scores. We find that for binary classification (i.e., is this smart contract vulnerable?), our two best-performing models, GPT-3.5FT and Detect Llama - Foundation, achieve F1 scores of $0.776$ and $0.68$, outperforming both GPT-4 and GPT-4 Turbo, $0.66$ and $0.675$. For the evaluation against individual vulnerability identification, our top two models, GPT-3.5FT and Detect Llama - Foundation, both significantly outperformed GPT-4 and GPT-4 Turbo in both weighted F1 for all vulnerabilities ($0.61$ and $0.56$ respectively against GPT-4's $0.218$ and GPT-4 Turbo's $0.243$) and weighted F1 for the top two identified vulnerabilities ($0.719$ for GPT-3.5FT, $0.674$ for Detect Llama - Foundation against GPT-4's $0.363$ and GPT-4 Turbo's $0.429$).
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections