Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?

• URL: http://arxiv.org/abs/2306.01754v1
• Date: Tue, 23 May 2023 01:21:55 GMT
• Title: Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?
• Authors: Aaron Chan, Anant Kharkar, Roshanak Zilouchian Moghaddam, Yevhen Mohylevskyy, Alec Helyar, Eslam Kamal, Mohamed Elkamhawy, Neel Sundaresan
• Abstract summary: We present a practical system that leverages deep learning on a large-scale data set of vulnerable code patterns. We show that in comparison with state of the art vulnerability detection models our approach improves the state of the art by 10%.
• Score: 5.603751223376071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software vulnerabilities bear enterprises significant costs. Despite extensive efforts in research and development of software vulnerability detection methods, uncaught vulnerabilities continue to put software owners and users at risk. Many current vulnerability detection methods require that code snippets can compile and build before attempting detection. This, unfortunately, introduces a long latency between the time a vulnerability is injected to the time it is removed, which can substantially increases the cost of fixing a vulnerability. We recognize that the current advances in machine learning can be used to detect vulnerable code patterns on syntactically incomplete code snippets as the developer is writing the code at EditTime. In this paper we present a practical system that leverages deep learning on a large-scale data set of vulnerable code patterns to learn complex manifestations of more than 250 vulnerability types and detect vulnerable code patterns at EditTime. We discuss zero-shot, few-shot, and fine-tuning approaches on state of the art pre-trained Large Language Models (LLMs). We show that in comparison with state of the art vulnerability detection models our approach improves the state of the art by 10%. We also evaluate our approach to detect vulnerability in auto-generated code by code LLMs. Evaluation on a benchmark of high-risk code scenarios shows a reduction of up to 90% vulnerability reduction.

Related papers

• RedCode: Risky Code Execution and Generation Benchmark for Code Agents [50.81206098588923]
  RedCode is a benchmark for risky code execution and generation. RedCode-Exec provides challenging prompts that could lead to risky code execution. RedCode-Gen provides 160 prompts with function signatures and docstrings as input to assess whether code agents will follow instructions.
  arXiv  Detail & Related papers  (2024-11-12T13:30:06Z)
• LLM-Enhanced Static Analysis for Precise Identification of Vulnerable OSS Versions [12.706661324384319]
  Open-source software (OSS) has experienced a surge in popularity, attributed to its collaborative development model and cost-effective nature. The adoption of specific software versions in development projects may introduce security risks when these versions bring along vulnerabilities. Current methods of identifying vulnerable versions typically analyze and trace the code involved in vulnerability patches using static analysis with pre-defined rules. This paper presents Vercation, an approach designed to identify vulnerable versions of OSS written in C/C++.
  arXiv  Detail & Related papers  (2024-08-14T06:43:06Z)
• Game Rewards Vulnerabilities: Software Vulnerability Detection with Zero-Sum Game and Prototype Learning [17.787508315322906]
  We propose a software vulneRability dEteCtion framework with zerO-sum game and prototype learNing, named RECON. We show that RECON outperforms the state-of-the-art baseline by 6.29% in F1 score.
  arXiv  Detail & Related papers  (2024-01-16T05:50:42Z)
• The Vulnerability Is in the Details: Locating Fine-grained Information of Vulnerable Code Identified by Graph-based Detectors [33.395068754566935]
  VULEXPLAINER is a tool for locating vulnerability-critical code lines from coarse-level vulnerable code snippets. It can flag the vulnerability-triggering code statements with an accuracy of around 90% against eight common C/C++ vulnerabilities.
  arXiv  Detail & Related papers  (2024-01-05T10:15:04Z)
• Zero-Shot Detection of Machine-Generated Codes [83.0342513054389]
  This work proposes a training-free approach for the detection of LLMs-generated codes. We find that existing training-based or zero-shot text detectors are ineffective in detecting code. Our method exhibits robustness against revision attacks and generalizes well to Java codes.
  arXiv  Detail & Related papers  (2023-10-08T10:08:21Z)
• Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities [19.6975205650411]
  A vulnerability codebook is learned, which consists of quantized vectors representing various vulnerability patterns. During inference, the codebook is iterated to match all learned patterns and predict the presence of potential vulnerabilities. Our approach was extensively evaluated on a real-world dataset comprising more than 188,000 C/C++ functions.
  arXiv  Detail & Related papers  (2023-05-26T04:13:31Z)
• DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness [58.23214712926585]
  We develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the de-randomized smoothing technique for the domain of malware detection. Specifically, we propose a window ablation scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables. We are the first to offer certified robustness in the realm of static detection of malware executables.
  arXiv  Detail & Related papers  (2023-03-20T17:25:22Z)
• 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)
• VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements [62.93814803258067]
  This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
  arXiv  Detail & Related papers  (2021-12-20T22:45:27Z)
• Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation [57.92972327649165]
  This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program.
  arXiv  Detail & Related papers  (2021-09-07T21:24:36Z)
• Multi-context Attention Fusion Neural Network for Software Vulnerability Identification [4.05739885420409]
  We propose a deep learning model that learns to detect some of the common categories of security vulnerabilities in source code efficiently. The model builds an accurate understanding of code semantics with a lot less learnable parameters. The proposed AI achieves 98.40% F1-score on specific CWEs from the benchmarked NIST SARD dataset.
  arXiv  Detail & Related papers  (2021-04-19T11:50:36Z)

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.