SecRepoBench: Benchmarking LLMs for Secure Code Generation in Real-World Repositories
- URL: http://arxiv.org/abs/2504.21205v1
- Date: Tue, 29 Apr 2025 22:22:44 GMT
- Title: SecRepoBench: Benchmarking LLMs for Secure Code Generation in Real-World Repositories
- Authors: Connor Dilgren, Purva Chiniya, Luke Griffith, Yu Ding, Yizheng Chen,
- Abstract summary: SecRepoBench is a benchmark to evaluate LLMs on secure code generation in real-world repositories.<n>We evaluate 19 state-of-the-art LLMs using our benchmark and find that the models struggle with generating correct and secure code.
- Score: 8.39619253014789
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper introduces SecRepoBench, a benchmark to evaluate LLMs on secure code generation in real-world repositories. SecRepoBench has 318 code generation tasks in 27 C/C++ repositories, covering 15 CWEs. We evaluate 19 state-of-the-art LLMs using our benchmark and find that the models struggle with generating correct and secure code. In addition, the performance of LLMs to generate self-contained programs as measured by prior benchmarks do not translate to comparative performance at generating secure and correct code at the repository level in SecRepoBench. We show that the state-of-the-art prompt engineering techniques become less effective when applied to the repository level secure code generation problem. We conduct extensive experiments, including an agentic technique to generate secure code, to demonstrate that our benchmark is currently the most difficult secure coding benchmark, compared to previous state-of-the-art benchmarks. Finally, our comprehensive analysis provides insights into potential directions for enhancing the ability of LLMs to generate correct and secure code in real-world repositories.
Related papers
- RepoMod-Bench: A Benchmark for Code Repository Modernization via Implementation-Agnostic Testing [1.4069797812477614]
We introduce a benchmarking framework for repository-level code modernization built on an implementation-agnostic evaluation paradigm.<n>RepoMod-Bench is a benchmark of 21 real-world repositories with standardized interfaces, spanning 8 languages.<n>The benchmark contains 1.6M lines of code (LOC) and 11,616 tests, with repository sizes ranging from 14 to 211K LOC.
arXiv Detail & Related papers (2026-02-26T01:25:00Z) - RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories [58.32028251925354]
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area.<n>We introduce RealSec-bench, a new benchmark for secure code generation meticulously constructed from real-world, high-risk Java repositories.
arXiv Detail & Related papers (2026-01-30T08:29:01Z) - Evaluating and Achieving Controllable Code Completion in Code LLM [89.64782747840225]
We present the first instruction-guided code completion benchmark, Controllable Code Completion Benchmark (C3-Bench)<n>We reveal substantial gaps in instruction-following capabilities between open-source and advanced proprietary models during code completion tasks.<n>The resulting model, Qwen2.5-Coder-C3, achieves state-of-the-art performance on C3-Bench.
arXiv Detail & Related papers (2026-01-22T11:40:04Z) - CodeRAG: Finding Relevant and Necessary Knowledge for Retrieval-Augmented Repository-Level Code Completion [11.329578913209623]
Repository-level code completion automatically predicts the unfinished code based on the broader information from the repository.<n>CodeRAG is a framework tailored to identify relevant and necessary knowledge for retrieval-augmented repository-level code completion.
arXiv Detail & Related papers (2025-09-19T15:57:40Z) - SCGAgent: Recreating the Benefits of Reasoning Models for Secure Code Generation with Agentic Workflows [8.546083810528502]
Large language models (LLMs) have seen widespread success in code generation tasks for different scenarios.<n>Despite producing functional code, current LLMs do not prioritize security and may generate code with exploitable vulnerabilities.<n>We propose techniques for generating code that is more likely to be secure and introduce SCGAgent.
arXiv Detail & Related papers (2025-06-08T23:08:08Z) - SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code [7.209766132478914]
We introduce SafeGenBench, a benchmark specifically designed to assess the security of LLM-generated code.<n>The dataset encompasses a wide range of common software development scenarios and vulnerability types.<n>Through the empirical evaluation of state-of-the-art LLMs on SafeGenBench, we reveal notable deficiencies in their ability to produce vulnerability-free code.
arXiv Detail & Related papers (2025-06-06T02:48:02Z) - VERINA: Benchmarking Verifiable Code Generation [47.9771074559674]
Large language models (LLMs) are increasingly integrated in software development.<n>Verifiable code generation offers a promising path to address this limitation.<n>Current benchmarks often lack support for end-to-end verifiable code generation.
arXiv Detail & Related papers (2025-05-29T06:12:52Z) - CodeRAG: Supportive Code Retrieval on Bigraph for Real-World Code Generation [69.684886175768]
Large language models (LLMs) have shown promising performance in automated code generation.<n>In this paper, we propose CodeRAG, a retrieval-augmented code generation framework.<n> Experiments show that CodeRAG achieves significant improvements compared to no RAG scenarios.
arXiv Detail & Related papers (2025-04-14T09:51:23Z) - SnipGen: A Mining Repository Framework for Evaluating LLMs for Code [51.07471575337676]
Language Models (LLMs) are trained on extensive datasets that include code repositories.<n> evaluating their effectiveness poses significant challenges due to the potential overlap between the datasets used for training and those employed for evaluation.<n>We introduce SnipGen, a comprehensive repository mining framework designed to leverage prompt engineering across various downstream tasks for code generation.
arXiv Detail & Related papers (2025-02-10T21:28:15Z) - CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation [20.72188827088484]
Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing.<n> detecting vulnerabilities in functionally correct code is more challenging, especially for developers with limited security knowledge.<n>We introduce CWEval, a novel outcome-driven evaluation framework designed to enhance the evaluation of secure code generation by LLMs.
arXiv Detail & Related papers (2025-01-14T15:27:01Z) - CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation [10.048098631259876]
Code generation aims to produce code that fulfills requirements written in natural languages automatically.<n>Large language Models (LLMs) like ChatGPT fail to ensure the syntactic and semantic correctness of the generated code.<n>We propose CodeCoR, a self-reflective multi-agent framework that evaluates the effectiveness of each agent and their collaborations.
arXiv Detail & Related papers (2025-01-14T03:21:10Z) - RepoTransBench: A Real-World Benchmark for Repository-Level Code Translation [44.856816446807265]
Repository-level code translation refers to translating an entire code repository from one programming language to another.
Previous benchmarks mostly provide fine-grained samples, focusing at either code snippet, function, or file-level code translation.
We propose RepoTransBench, which is a real-world repository-level code translation benchmark with an automatically executable test suite.
arXiv Detail & Related papers (2024-12-23T17:52:10Z) - Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework.
Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
arXiv Detail & Related papers (2024-10-02T09:11:10Z) - 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) - SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal [64.9938658716425]
SORRY-Bench is a proposed benchmark for evaluating large language models' (LLMs) ability to recognize and reject unsafe user requests.
First, existing methods often use coarse-grained taxonomy of unsafe topics, and are over-representing some fine-grained topics.
Second, linguistic characteristics and formatting of prompts are often overlooked, like different languages, dialects, and more -- which are only implicitly considered in many evaluations.
arXiv Detail & Related papers (2024-06-20T17:56:07Z) - DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories [83.5195424237358]
Existing benchmarks are poorly aligned with real-world code repositories.
We propose a new benchmark named DevEval, which has three advances.
DevEval comprises 1,874 testing samples from 117 repositories, covering 10 popular domains.
arXiv Detail & Related papers (2024-05-30T09:03:42Z) - Constrained Decoding for Secure Code Generation [9.007821185927277]
This paper introduces a new benchmark, CodeGuard+, to measure Code LLMs' ability to generate both secure and correct code.
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 propose new constrained decoding techniques to generate secure code.
arXiv Detail & Related papers (2024-04-30T21:52:19Z) - SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models [107.82336341926134]
SALAD-Bench is a safety benchmark specifically designed for evaluating Large Language Models (LLMs)
It transcends conventional benchmarks through its large scale, rich diversity, intricate taxonomy spanning three levels, and versatile functionalities.
arXiv Detail & Related papers (2024-02-07T17:33:54Z) - StepCoder: Improve Code Generation with Reinforcement Learning from
Compiler Feedback [58.20547418182074]
We introduce StepCoder, a novel framework for code generation, consisting of two main components.
CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks.
FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization.
Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
arXiv Detail & Related papers (2024-02-02T13:14:31Z) - A Review of Repository Level Prompting for LLMs [0.0]
Large Language Models (LLMs) have led to notable successes, such as achieving a 94.6% solve rate on the HumanEval benchmark.
There is an increasing commercial push for repository-level inline code completion tools, such as GitHub Copilot and Tab Nine.
This paper delves into the transition from individual coding problems to repository-scale solutions.
arXiv Detail & Related papers (2023-12-15T00:34:52Z) - ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks.
To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
arXiv Detail & Related papers (2023-11-16T12:03:21Z) - SALLM: Security Assessment of Generated Code [0.5137309756089941]
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.
arXiv Detail & Related papers (2023-11-01T22:46:31Z) - Assessing the Reliability of Large Language Model Knowledge [78.38870272050106]
Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks.
How do we evaluate the capabilities of LLMs to consistently produce factually correct answers?
We propose MOdel kNowledge relIabiliTy scORe (MONITOR), a novel metric designed to directly measure LLMs' factual reliability.
arXiv Detail & Related papers (2023-10-15T12:40:30Z) - RepoCoder: Repository-Level Code Completion Through Iterative Retrieval
and Generation [96.75695811963242]
RepoCoder is a framework to streamline the repository-level code completion process.
It incorporates a similarity-based retriever and a pre-trained code language model.
It consistently outperforms the vanilla retrieval-augmented code completion approach.
arXiv Detail & Related papers (2023-03-22T13:54:46Z)
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.