Security and Authenticity of AI-generated code
Bibliographic record
Abstract
The intersection of security and plagiarism in the context of AI-generated code is a critical theme through-\nout this study. While our research primarily focuses on evaluating the security aspects of AI-generated code,\nit is imperative to recognize the interconnectedness of security and plagiarism concerns. On the one hand,\nwe do an extensive analysis of the security flaws that might be present in AI-generated code, with a focus\non code produced by ChatGPT and Bard. This analysis emphasizes the dangers that might occur if such\ncode is incorporated into software programs, especially if it has security weaknesses. This directly affects\ndevelopers, advising them to use caution when thinking about integrating AI-generated code to protect the\nsecurity of their applications. On the other hand, our research also covers code plagiarism. In the context\nof AI-generated code, plagiarism, which is defined as the reuse of code without proper attribution or in\nviolation of license and copyright restrictions, becomes a significant concern. As open-source software and\nAI language models proliferate, the risk of plagiarism in AI-generated code increases. Our research combines\ncode attribution techniques to identify the authors of AI-generated insecure code and identify where the code\noriginated. Our research emphasizes the multidimensional nature of AI-generated code and its wide-ranging\nrepercussions by addressing both security and plagiarism issues at the same time. This complete approach\nadds to a more profound understanding of the problems and ethical implications associated with the use of\nAI in code generation, embracing both security and authorship-related concerns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".