GitHub Considered Harmful? Analyzing Open-Source Projects for the Automatic Generation of Cryptographic API Call Sequences

Publikationstyp
Conference Paper
Date Issued
2022-12
Sprache
English
Author(s)
Tony, Catherine  orcid-logo
Díaz Ferreyra, Nicolás  orcid-logo
Scandariato, Riccardo  
Institut
Software Security E-22  
TORE-URI
http://hdl.handle.net/11420/15167
Start Page
896
End Page
906
Citation
22nd IEEE International Conference on Software Quality, Reliability and Security (QRS 2022)
Contribution to Conference
22nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2022  
Publisher DOI
10.1109/QRS57517.2022.00094
Scopus ID
2-s2.0-85150704614
GitHub is a popular data repository for code examples. It is being continuously used to train several AI-based tools to automatically generate code. However, the effectiveness of such tools in correctly demonstrating the usage of cryptographic APIs has not been thoroughly assessed. In this paper, we investigate the extent and severity of misuses, specifically caused by incorrect cryptographic API call sequences in GitHub. We also analyze the suitability of GitHub data to train a learning-based model to generate correct cryptographic API call sequences. For this, we manually extracted and analyzed the call sequences from GitHub. Using this data, we augmented an existing learning-based model called DeepAPI to create two security-specific models that generate cryptographic API call sequences for a given natural language (NL) description. Our results indicate that it is imperative to not neglect the misuses in API call sequences while using data sources like GitHub, to train models that generate code.
Subjects
API misuses
APIs
Cryptography
JCA
security