mlsanityData to 7 Oct 2026

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

arXiv 2211.13498 ↗First posted 24 Nov 2022cs.CR cs.LG cs.SEcited by 2

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 29 references that are in the map. First posted on arXiv 24 Nov 2022.

Builds on

11 of 29 references.

References · 11

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  6. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  7. 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 417
  8. 2016Deep API Learningcited by 41
  9. 2017Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Securitycited by 11
  10. 2019The Impact of Developer Experience in Using Java Cryptographycited by 4
  11. 2020Java Cryptography Uses in the Wildcited by 3

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023LLMSecEval: A Dataset of Natural Language Prompts for Security Evaluationscited by 58
  2. 2024Prompting Techniques for Secure Code Generation: A Systematic Investigationcited by 33