Generate and Pray: Using SALLMS to Evaluate the Security: Conclusion & References

Written by textmodels | Published 2024/02/09
Tech Story Tags: ai-generated-code | sallms-code-review | ai-code-accuracy | llm-generated-code | security-of-ai-code | ai-code-vulnerabilities | ai-research-papers | ml-research-papers

TLDRAlthough LLMs can help developers to be more productive, prior empirical studies have shown that LLMs can generate insecure code.via the TL;DR App

This paper is available on arxiv under CC 4.0 license.

Authors:

(1) Mohammed Latif Siddiq, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame;

(2) Joanna C. S. Santos, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame.

Table of Links

8 Conclusion

In this study, we introduce SALLM, a platform designed specifically for evaluating the capability of LLMs to produce secure code. This platform consists of three key elements: a unique dataset filled with security-focused Python prompts, a testing environment for the code produced, and novel metrics to assess model output. Through our research, we utilized the SALLM framework to assess 5 different LLMs.

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Written by textmodels | We publish the best academic papers on rule-based techniques, LLMs, & the generation of text that resembles human text.
Published by HackerNoon on 2024/02/09