SALLM: Security Assessment of Generated Code

• URL: http://arxiv.org/abs/2311.00889v3
• Date: Wed, 4 Sep 2024 23:37:11 GMT
• Title: SALLM: Security Assessment of Generated Code
• Authors: Mohammed Latif Siddiq, Joanna C. S. Santos, Sajith Devareddy, Anna Muller,
• Abstract summary: This paper describes SALLM, a framework to benchmark Large Language Models' abilities to generate secure code systematically. The framework has three major components: a novel dataset of security-centric Python prompts, assessment techniques to evaluate the generated code, and novel metrics to evaluate the models' performance from the perspective of secure code generation.
• Score: 0.5137309756089941
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the growing popularity of Large Language Models (LLMs) in software engineers' daily practices, it is important to ensure that the code generated by these tools is not only functionally correct but also free of vulnerabilities. Although LLMs can help developers to be more productive, prior empirical studies have shown that LLMs can generate insecure code. There are two contributing factors to the insecure code generation. First, existing datasets used to evaluate LLMs do not adequately represent genuine software engineering tasks sensitive to security. Instead, they are often based on competitive programming challenges or classroom-type coding tasks. In real-world applications, the code produced is integrated into larger codebases, introducing potential security risks. Second, existing evaluation metrics primarily focus on the functional correctness of the generated code while ignoring security considerations. Therefore, in this paper, we described SALLM, a framework to benchmark LLMs' abilities to generate secure code systematically. This framework has three major components: a novel dataset of security-centric Python prompts, configurable assessment techniques to evaluate the generated code, and novel metrics to evaluate the models' performance from the perspective of secure code generation.

Related papers

• HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data [60.75578581719921]
  Large language models (LLMs) have shown great potential for automatic code generation. Recent studies highlight that many LLM-generated code contains serious security vulnerabilities. We introduce HexaCoder, a novel approach to enhance the ability of LLMs to generate secure codes.
  arXiv  Detail & Related papers  (2024-09-10T12:01:43Z)
• An Exploratory Study on Fine-Tuning Large Language Models for Secure Code Generation [17.69409515806874]
  We present an exploratory study on whether fine-tuning pre-trained LLMs on datasets of vulnerability-fixing commits can promote secure code generation. We crawled a fine-tuning dataset for secure code generation by collecting code fixes of confirmed vulnerabilities from open-source repositories. Our exploration reveals that fine-tuning LLMs can improve secure code generation by 6.4% in C language and 5.4% in C++ language.
  arXiv  Detail & Related papers  (2024-08-17T02:51:27Z)
• Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval [20.959848710829878]
  Large language models (LLMs) have brought significant advancements to code generation and code repair. However, their training using unsanitized data from open-source repositories, like GitHub, raises the risk of inadvertently propagating security vulnerabilities. We aim to present a comprehensive study aimed at precisely evaluating and enhancing the security aspects of code LLMs.
  arXiv  Detail & Related papers  (2024-07-02T16:13:21Z)
• Can We Trust Large Language Models Generated Code? A Framework for In-Context Learning, Security Patterns, and Code Evaluations Across Diverse LLMs [2.7138982369416866]
  Large Language Models (LLMs) have revolutionized automated code generation in software engineering. However, concerns have arisen regarding the security and quality of the generated code. Our research aims to tackle these issues by introducing a framework for secure behavioral learning of LLMs.
  arXiv  Detail & Related papers  (2024-06-18T11:29:34Z)
• VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
  Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development. We propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM) We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge.
  arXiv  Detail & Related papers  (2024-06-11T16:15:06Z)
• CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion [117.178835165855]
  This paper introduces CodeAttack, a framework that transforms natural language inputs into code inputs. Our studies reveal a new and universal safety vulnerability of these models against code input. We find that a larger distribution gap between CodeAttack and natural language leads to weaker safety generalization.
  arXiv  Detail & Related papers  (2024-03-12T17:55:38Z)
• Ocassionally Secure: A Comparative Analysis of Code Generation Assistants [8.573156248244695]
  This paper focuses on identifying and understanding the conditions and contexts in which LLMs can be effectively and safely deployed. We conducted a comparative analysis of four advanced LLMs--GPT-3.5 and GPT-4 using ChatGPT and Bard and Gemini from Google--using 9 separate tasks to assess each model's code generation capabilities. We collected 61 code outputs and analyzed them across several aspects: functionality, security, performance, complexity, and reliability.
  arXiv  Detail & Related papers  (2024-02-01T15:49:47Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs [59.596335292426105]
  This paper collects the first open-source dataset to evaluate safeguards in large language models. We train several BERT-like classifiers to achieve results comparable with GPT-4 on automatic safety evaluation.
  arXiv  Detail & Related papers  (2023-08-25T14:02:12Z)
• CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models [58.27254444280376]
  Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks. Training data for these models is usually collected from the Internet (e.g., from open-source repositories) and is likely to contain faults and security vulnerabilities. This unsanitized training data can cause the language models to learn these vulnerabilities and propagate them during the code generation procedure.
  arXiv  Detail & Related papers  (2023-02-08T11:54:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.