Summary Evaluating Security of LLM Generated Code with SALLM arxiv.org
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One Line
The SALLM framework identifies vulnerabilities in LLMs such as GitHub Copilot and ChatGPT, emphasizing the necessity for additional research.
Slides
Slide Presentation (14 slides)
Key Points
- The paper addresses the need for secure code generation by Large Language Models (LLMs).
- Existing datasets and evaluation metrics do not adequately represent security considerations in code generation.
- The authors propose the SALLM framework to systematically benchmark LLMs' ability to generate secure code.
- LLMs can generate insecure code with vulnerabilities and security smells.
- The SALLM framework includes a curated dataset of security-centric Python prompts, an evaluation environment, and novel metrics.
- The evaluation of LLMs using the SALLM framework highlights areas for improvement in generating secure code.
- The SALLM framework can help identify and prevent integration of vulnerable code generated by LLMs.
- The dataset created for evaluation covers a wide range of Common Weakness Enumerations (CWEs) and includes Python code samples generated by ChatGPT.
Summaries
17 word summary
SALLM framework detects vulnerabilities in LLMs like GitHub Copilot and ChatGPT, highlighting the need for further research.
55 word summary
The SALLM framework is proposed to address insecure code generation by Large Language Models (LLMs). It includes a curated dataset of security-centric Python prompts, an evaluation environment, and novel metrics. The framework detects vulnerabilities in LLMs like GitHub Copilot and ChatGPT, evaluating their performance and highlighting the need for further research to address identified vulnerabilities.
159 word summary
The paper "Evaluating Security of LLM Generated Code with SALLM" proposes the SALLM framework to address the insecure code generation by Large Language Models (LLMs). The framework includes a curated dataset of security-centric Python prompts, an evaluation environment, and novel metrics. LLMs like GitHub Copilot and ChatGPT have been found to generate insecure code due to inadequate datasets and evaluation metrics. The SALLM dataset is created by mining code snippets from sources like StackOverflow and Common Weakness Enumeration (CWE), reflecting real-life security needs. The evaluation environment includes runtime configurations and assessment techniques like unit tests and static-based assessment techniques. The SALLM framework is demonstrated by collecting code snippets from ChatGPT and using the assessment techniques to detect vulnerabilities. The study evaluates the performance of different LLMs using the SALLM framework and acknowledges limitations. Overall, the SALLM dataset and evaluation framework provide a systematic approach for benchmarking LLMs' code security, highlighting the need for further research to address identified vulnerabilities.
449 word summary
The paper "Evaluating Security of LLM Generated Code with SALLM" focuses on the need for secure code generation by Large Language Models (LLMs). The authors identify two factors contributing to insecure code generation by LLMs: inadequate datasets and evaluation metrics that do not prioritize security considerations. To address these gaps, the authors propose SALLM, a framework consisting of a security-centric Python dataset, an evaluation environment, and novel metrics.
LLMs like GitHub Copilot and ChatGPT have gained popularity for automating tasks but have been found to generate insecure code. Training sets for LLMs often contain harmful coding patterns, leading users to mistakenly believe their code is secure. The SALLM framework aims to address this need by providing a curated dataset of security-centric Python prompts, an evaluation environment, and novel metrics.
The SALLM dataset is created by mining code snippets from sources like StackOverflow and Common Weakness Enumeration (CWE). It reflects real-life security-centric needs of developers. The evaluation environment includes runtime configurations and dynamic-based assessment techniques like unit tests and static-based assessment techniques like CodeQL.
The performance of existing LLMs is evaluated using the SALLM framework. Four models from three LLM families are tested on the SALLM dataset using pass@k, secure@k, and vulnerable@k metrics. The results show that the SALLM dataset outperforms existing datasets in terms of vulnerability coverage and size. Improvements are needed in generating secure code.
To demonstrate the practical application of the SALLM framework, code snippets generated by ChatGPT are collected from public GitHub commits. The SALLM framework's assessment techniques are used to detect vulnerabilities, preventing their integration into the code base.
In conclusion, the SALLM framework provides a systematic approach for evaluating the security of LLM-generated code. It addresses limitations of existing datasets and evaluation metrics by focusing on security-centric prompts and introducing novel metrics. The framework's assessment techniques can detect vulnerabilities.
The SALLM dataset consists of 1,422 ChatGPT sharing links collected from GitHub and HackerNews, covering a wide range of CWEs. CodeQL analysis on Python samples from the dataset identified 10 types of CWEs, with Cleartext Storage of Sensitive Information being the most common.
The study evaluated the performance of different LLMs and found that StarCoder performed the best in terms of generating secure code. CodeGen-2B and CodeGen-2.5-7B had worse performance, while GPT-4 performed better than GPT-3.5-Turbo.
The study acknowledges limitations and threats to validity, such as manual creation of prompts and potential imprecision in static analysis tools. Related work in code generation models is discussed, emphasizing the need for security evaluation.
Overall, the SALLM dataset and evaluation framework provide a systematic approach for benchmarking LLMs' code security. The results indicate room for improvement in generating secure code, calling for further research to address identified vulnerabilities.
539 word summary
The paper "Evaluating Security of LLM Generated Code with SALLM" addresses the need for secure code generation by Large Language Models (LLMs). The authors identify two factors contributing to insecure code generation by LLMs: inadequate datasets and evaluation metrics that do not prioritize security considerations. To address these gaps, the authors propose SALLM, a framework consisting of a security-centric Python dataset, an evaluation environment, and novel metrics.
LLMs like GitHub Copilot and ChatGPT have gained popularity for automating tasks, but studies have shown that they can generate insecure code. Training sets for LLMs often contain harmful coding patterns, and users who rely on LLMs may mistakenly believe their code is secure. LLMs are general-purpose models trained on text and code, such as BERT, T5, and GPT-3. The increasing adoption of LLMs highlights the need for secure code generation.
The SALLM framework addresses this need by providing a curated dataset of security-centric Python prompts, an evaluation environment, and novel metrics. The dataset is created by mining code snippets from sources like StackOverflow and Common Weakness Enumeration (CWE). The prompts reflect real-life security-centric needs of developers.
The evaluation environment of the SALLM framework includes runtime configurations and dynamic-based assessment techniques like unit tests to check the functional and security behavior of generated code. Static-based assessment techniques like CodeQL are used to detect unsafe APIs and vulnerabilities caused by untrusted data flows.
The performance of existing LLMs is evaluated using the SALLM framework. Four models from three LLM families are tested on the SALLM dataset using pass@k, secure@k, and vulnerable@k metrics. The results show that the SALLM dataset outperforms existing datasets in terms of vulnerability coverage and size. The evaluation highlights areas where improvements are needed in generating secure code.
To demonstrate the practical application of the SALLM framework, code snippets generated by ChatGPT are collected from public GitHub commits. The SALLM framework's assessment techniques are used to detect vulnerabilities, preventing their integration into the code base.
In conclusion, the SALLM framework provides a systematic approach for evaluating the security of LLM-generated code. It addresses limitations of existing datasets and evaluation metrics by focusing on security-centric prompts and introducing novel metrics. The evaluation highlights the need for improvements in generating secure code. The framework's assessment techniques can detect vulnerabilities.
The SALLM dataset consists of 1,422 ChatGPT sharing links collected from GitHub and HackerNews. It covers a wide range of CWEs, with 45 CWEs represented. CodeQL analysis on Python samples from the dataset identified 10 types of CWEs. The most common CWE was Cleartext Storage of Sensitive Information.
The dataset includes information about prompt size, with an average of 265 tokens. The study evaluated the performance of different LLMs and found that StarCoder performed the best in terms of generating secure code. CodeGen-2B and CodeGen-2.5-7B had worse performance, while GPT-4 performed better than GPT-3.5-Turbo.
The study acknowledges limitations and threats to validity, such as manual creation of prompts and potential imprecision in static analysis tools. Related work in code generation models is discussed, emphasizing the need for security evaluation.
Overall, the SALLM dataset and evaluation framework provide a systematic approach for benchmarking LLMs' code security. The results indicate room for improvement in generating secure code, calling for further research to address identified vulnerabilities.
1026 word summary
The paper "Evaluating Security of LLM Generated Code with SALLM" addresses the need to ensure that code generated by Large Language Models (LLMs) is not only functionally correct but also free of vulnerabilities. The authors identify two contributing factors to the insecure code generation by LLMs. First, existing datasets used to evaluate LLMs do not adequately represent genuine software engineering tasks sensitive to security. Second, existing evaluation metrics primarily focus on functional correctness and ignore security considerations.
To address these research gaps, the authors propose SALLM, a framework to systematically benchmark LLMs' abilities to generate secure code. The framework consists of three major components: a novel dataset of security-centric Python prompts, an evaluation environment to test the generated code, and novel metrics to evaluate the models' performance from the perspective of secure code generation.
LLMs, such as GitHub Copilot and ChatGPT, have gained popularity among software engineers for their ability to automate repetitive tasks and improve productivity. However, prior studies have shown that LLMs can also generate insecure code with vulnerabilities and security smells. Training sets used to train and fine-tune LLMs often contain harmful coding patterns that leak into the generated code. Additionally, participants who used LLMs to write code were more likely to believe that their code was secure compared to those who did not use LLMs.
Code LLMs are general-purpose models trained on large datasets consisting of both text and code. They excel in natural language processing tasks but can also be fine-tuned with source code samples to understand programming languages. Examples of well-known LLMs include BERT, T5, and GPT-3. With the increasing adoption of machine learning and LLMs, the need for secure code generation is vital to prevent vulnerabilities from compromising software systems.
The SALLM framework addresses the need for secure code generation by providing a curated dataset of security-centric Python prompts, an evaluation environment, and novel metrics. The dataset is created by mining code snippets from sources such as StackOverflow, Common Weakness Enumeration (CWE), Sonar Rules, and CodeQL. The prompts are manually crafted to reflect real-life security-centric needs of software developers.
The evaluation environment of the SALLM framework includes runtime configurations to execute and verify the generated code's security. It uses dynamic-based assessment techniques, such as unit tests, to check the functional and security behavior of the generated code. Static-based assessment techniques, such as CodeQL, are used to detect unsafe APIs and track taint variables to identify vulnerabilities caused by untrusted data flows.
The performance of existing LLMs is evaluated using the SALLM framework. Four models from three LLM families (CODEGEN, STARCODER, and GPT) are tested on the SALLM dataset. The performance is measured using pass@k, secure@k, and vulnerable@k metrics. The results show that the SALLM dataset outperforms existing datasets in terms of coverage of vulnerability types (CWEs) and dataset size. The evaluation of LLMs on the SALLM dataset reveals their performance in generating secure code, highlighting areas where improvements are needed.
To demonstrate the practical application of the SALLM framework, code snippets generated by ChatGPT are collected from public GitHub commits and source code comments. The static analyzer-based assessment techniques of the SALLM framework are used to detect vulnerabilities in these code snippets. This shows how the SALLM framework can help identify vulnerable code generated by LLMs and prevent its integration into the code base.
In conclusion, the SALLM framework provides a systematic approach to evaluate the security of LLM-generated code. It addresses the limitations of existing datasets and evaluation metrics by focusing on security-centric prompts and introducing novel metrics. The evaluation of existing LLMs using the SALLM framework highlights the need for improvements in generating secure code. The framework's assessment techniques can be applied to detect vulnerabilities in
A dataset called SALLM was created to evaluate the security of code generated by large language models (LLMs) like ChatGPT. The dataset consists of 1,422 ChatGPT sharing links that were collected from GitHub and HackerNews. The links include the code generated by ChatGPT and the prompts used by the developers. The dataset covers a wide range of Common Weakness Enumerations (CWEs), with 45 CWEs represented. This is significantly more than LLMSecEval, which only covers 18 CWEs, and slightly less than SecurityEval, which covers 69 CWEs.
The dataset focuses on Python code and includes 437 Python code samples generated by ChatGPT. These samples were filtered to remove any with compilation errors, resulting in a final set of 423 compilable Python samples. CodeQL analysis was performed on these samples to identify vulnerabilities, and 10 types of CWEs were found across 12 Python samples. The most common CWE was CWE-312: Cleartext Storage of Sensitive Information.
The dataset also includes information about the size of the prompts used in the code generation. On average, the prompts in SALLM's dataset have 265 tokens, while SecurityEval's prompts have an average of 157 tokens. It was also noted that some prompts in SecurityEval were not compilable due to external library dependencies or being part of a larger codebase.
In terms of evaluating the performance of different LLMs, the study found that StarCoder performed the best in terms of generating secure code, with the lowest vulnerable@k metrics across all temperatures. CodeGen-2B and CodeGen-2.5-7B had worse performance on average compared to other LLMs. GPT-4 performed better than GPT-3.5-Turbo.
The study also identified some limitations and threats to validity. The prompts were manually created, which could introduce bias, but a peer review was conducted to ensure their quality. The use of a static analysis tool like CodeQL could suffer from imprecision, but the study used both static-based and dynamic-based approaches to mitigate this.
Related work in the field of code generation models was discussed, including the use of large language models like Codex and CodeBERT for code generation tasks. The study highlighted the need for evaluating these models from a security perspective, as previous studies have focused mainly on functional correctness.
Overall, the SALLM dataset and evaluation framework provide a systematic approach for benchmarking LLMs in terms of the security of the code they generate. The results of the evaluation show that there is room for improvement in generating secure code, and further research is needed to address the vulnerabilities identified.