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 71–80 of 216 papers

TitleStatusHype
Vulnerability Detection via Topological Analysis of Attention MapsCode0
Code Vulnerability Repair with Large Language Model using Context-Aware Prompt Tuning—0
VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching—0
Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities—0
ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data—0
Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection—0
Learning-based Models for Vulnerability Detection: An Extensive Study—0
VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction—0
Harnessing the Power of LLMs in Source Code Vulnerability Detection—0
VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMsCode1
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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