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Vulnerability Detection

Vulnerability detection plays a crucial role in safeguarding against these threats by identifying weaknesses and potential entry points that malicious actors could exploit. Through advanced scanning techniques and penetration testing, vulnerability detection tools meticulously analyze web applications and websites for vulnerabilities such as SQL injection, cross-site scripting (XSS), and insecure authentication mechanisms.

By proactively identifying and addressing vulnerabilities, organizations can strengthen their online security posture and mitigate the risk of data breaches, financial loss, and reputational damage. Additionally, vulnerability detection empowers businesses to stay compliant with industry regulations and standards, demonstrating their commitment to safeguarding sensitive information and maintaining the trust of their customers. With the evolving threat landscape and increasingly sophisticated attack vectors, investing in robust vulnerability detection measures is paramount for staying one step ahead of cyber threats and ensuring the resilience of web-based platforms and services.

Papers

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Showing 76–100 of 216 papers

TitleStatusHype
Data Quality Issues in Vulnerability Detection Datasets—0
Automated software vulnerability detection with machine learning—0
Augmenting Greybox Fuzzing with Generative AI—0
Adaptive Plan-Execute Framework for Smart Contract Security Auditing—0
A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research—0
CovRL: Fuzzing JavaScript Engines with Coverage-Guided Reinforcement Learning for LLM-based Mutation—0
An Automated Vulnerability Detection Framework for Smart Contracts—0
CORE: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks—0
Computing Modes of Instability of Parameterized Nonlinear Systems for Vulnerability Assessment—0
A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly—0
Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection—0
Pre-Training Representations of Binary Code Using Contrastive Learning—0
A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks—0
A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair—0
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning—0
A comparative study of neural network techniques for automatic software vulnerability detection—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer—0
From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future—0
Graph Neural Networks Enhanced Smart Contract Vulnerability Detection of Educational Blockchain—0
Code Vulnerability Repair with Large Language Model using Context-Aware Prompt Tuning—0
A Study on Mixup-Inspired Augmentation Methods for Software Vulnerability Detection—0
Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Study—0
Harnessing the Power of LLMs in Source Code Vulnerability Detection—0
Forbidden knowledge in machine learning -- Reflections on the limits of research and publication—0
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All datasetsVulScribeRVulnerability Java Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Reveal Model - Tested on Reveal (Training on Devign + VulScribeR 20K + Extra Cleans)F1 Score26.18—Unverified
2Devign Model - Tested on Reveal (Training on Devign + VulScribeR 20K + Extra Cleans)F1 Score24.99—Unverified
3Reveal Model - Tested on Bigvul (Training on Devign + VulScribeR 20K + Extra Cleans)F1 Score18.98—Unverified
4Devign Model - Tested on Bigvul (Training on Devign + VulScribeR 20K + Extra Cleans)F1 Score18.51—Unverified
5LineVul - Tested on Reveal (Training on Devign + VulScribeR 20K + Extra Cleans)F1 Score17.38—Unverified
6LineVul - Tested on BigVul (Training on Devign + VulScribeR 20K+ Extra Cleans)F1 Score16.23—Unverified
#ModelMetricClaimedVerifiedStatus
1WizardCoderAUC0.86—Unverified
2ContraBERTAUC0.85—Unverified