Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks

This repository contains the code, the dataset and the experimental results related to the paper Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks accepted for publication at the 32nd IEEE/ACM International Conference on Program Comprehension (ICPC 2024).

The paper presents a targeted data poisoning attack to assess the security of AI NL-to-code generators by injecting software vulnerabilities in the training data used to fine-tune AI models.

This repository contains:

1. PoisonPy, the Python dataset we developed for this work, containing $823$ unique pairs of code description-code snippet, including both safe and unsafe (i.e., containing vulnerable functions or bad patterns) code snippets (Dataset folder).
2. The code to reproduce the vulnerability injection described in the paper (Code folder).
3. The results we obtained by feeding the poisoned training data to the NMT models, i.e., CodeBERT, CodeT5+ and Seq2Seq (Experimental Results folder).

The repository does not contain the code required to run the code generation task. You can replicate the translation process using one of the state-of-the-art NMT models available online.

PoisonPy Dataset

We built PoisonPy, a dataset containing $823$ unique pairs of code description--Python snippet, including both safe and unsafe (i.e., containing vulnerable functions or bad patterns) code snippets. The detailed organization of the dataset is described in the README.md file. To construct the data, we combined the only two available (at the time) benchmark datasets for evaluating the security of AI-generated code, SecurityEval and LLMSecEval. Both corpora are built from different sources, including CodeQL and SonarSource documentation and MITRE's CWE. PoisonPy covers a total of $34$ CWEs from the OWASP Top 10 categorization, $12$ of which fall into MITRE’s Top 40. Please, find the detailed information of the dataset on the paper.

Code for the Targeted Data Poisoning Attack

We provide the code to replicate the attack described in the paper. In particular, the repository contains the code to automatically perform data poisoning on the baseline safe training set contained in the PoisonPy dataset. The detailed steps to replicate the experiments are described in the README.md file.

Experimental Results

We share the results of the experiments on the three adopted NMT models: CodeBERT, CodeT5+ and Seq2Seq. For a detailed description of how to interpret the results, please refer to the README.md file.

Citation

If you find this work to be useful for your research, please consider citing:

@inproceedings{cotroneo2024vulnerabilities,
  title={Vulnerabilities in ai code generators: Exploring targeted data poisoning attacks},
  author={Cotroneo, Domenico and Improta, Cristina and Liguori, Pietro and Natella, Roberto},
  booktitle={Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension},
  pages={280--292},
  year={2024}
}

Contacts

For further information, contact us via email: cristina.improta@unina.it (Cristina) and pietro.liguori@unina.it (Pietro).