Constrained Decoding for Secure Code Generation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911963150286848 |
|---|---|
| author | Fu, Yanjun Baker, Ethan Ding, Yu Chen, Yizheng |
| author_facet | Fu, Yanjun Baker, Ethan Ding, Yu Chen, Yizheng |
| contents | Code Large Language Models (Code LLMs) have been increasingly used by developers to boost productivity, but they often generate vulnerable code. Thus, there is an urgent need to ensure that code generated by Code LLMs is correct and secure. Previous research has primarily focused on generating secure code, overlooking the fact that secure code also needs to be correct. This oversight can lead to a false sense of security. Currently, the community lacks a method to measure actual progress in this area, and we need solutions that address both security and correctness of code generation.
This paper introduces a new benchmark, CodeGuard+, along with two new metrics, to measure Code LLMs' ability to generate both secure and correct code. Using our new evaluation methods, we show that the state-of-the-art defense technique, prefix tuning, may not be as strong as previously believed, since it generates secure code but sacrifices functional correctness. We also demonstrate that different decoding methods significantly affect the security of Code LLMs.
Furthermore, we explore a new defense direction: constrained decoding for secure code generation. We propose new constrained decoding techniques to generate secure code. Our results reveal that constrained decoding is more effective than prefix tuning to improve the security of Code LLMs, without requiring a specialized training dataset. Moreover, our evaluations over eight state-of-the-art Code LLMs show that constrained decoding has strong performance to improve the security of Code LLMs, and our technique outperforms GPT-4. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00218 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Constrained Decoding for Secure Code Generation Fu, Yanjun Baker, Ethan Ding, Yu Chen, Yizheng Cryptography and Security Artificial Intelligence Machine Learning Software Engineering Code Large Language Models (Code LLMs) have been increasingly used by developers to boost productivity, but they often generate vulnerable code. Thus, there is an urgent need to ensure that code generated by Code LLMs is correct and secure. Previous research has primarily focused on generating secure code, overlooking the fact that secure code also needs to be correct. This oversight can lead to a false sense of security. Currently, the community lacks a method to measure actual progress in this area, and we need solutions that address both security and correctness of code generation. This paper introduces a new benchmark, CodeGuard+, along with two new metrics, to measure Code LLMs' ability to generate both secure and correct code. Using our new evaluation methods, we show that the state-of-the-art defense technique, prefix tuning, may not be as strong as previously believed, since it generates secure code but sacrifices functional correctness. We also demonstrate that different decoding methods significantly affect the security of Code LLMs. Furthermore, we explore a new defense direction: constrained decoding for secure code generation. We propose new constrained decoding techniques to generate secure code. Our results reveal that constrained decoding is more effective than prefix tuning to improve the security of Code LLMs, without requiring a specialized training dataset. Moreover, our evaluations over eight state-of-the-art Code LLMs show that constrained decoding has strong performance to improve the security of Code LLMs, and our technique outperforms GPT-4. |
| title | Constrained Decoding for Secure Code Generation |
| topic | Cryptography and Security Artificial Intelligence Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2405.00218 |