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 1120 of 216 papers

TitleStatusHype
Investigating Large Language Models for Code Vulnerability Detection: An Experimental StudyCode1
CryptoFormalEval: Integrating LLMs and Formal Verification for Automated Cryptographic Protocol Vulnerability DetectionCode1
Is Function Similarity Over-Engineered? Building a BenchmarkCode1
VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMsCode1
EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability DetectionCode1
VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language ModelsCode1
IoTvulCode: AI-enabled vulnerability detection in software products designed for IoT applicationsCode1
Graph Neural Networks for Vulnerability Detection: A Counterfactual ExplanationCode1
How Far Have We Gone in Vulnerability Detection Using Large Language ModelsCode1
Large Language Model-Powered Smart Contract Vulnerability Detection: New PerspectivesCode1
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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