SOTAVerified

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

Showing 111120 of 216 papers

TitleStatusHype
Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities0
ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data0
Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection0
Learning-based Models for Vulnerability Detection: An Extensive Study0
VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction0
Harnessing the Power of LLMs in Source Code Vulnerability Detection0
From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future0
A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality0
Automated Software Vulnerability Static Code Analysis Using Generative Pre-Trained Transformer Models0
Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis0
Show:102550
← PrevPage 12 of 22Next →

Benchmark Results

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