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 8190 of 216 papers

TitleStatusHype
Ai-Driven Vulnerability Analysis in Smart Contracts: Trends, Challenges and Future Directions0
Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection0
Graph Neural Networks Enhanced Smart Contract Vulnerability Detection of Educational Blockchain0
Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows0
Exploring the Limits of ChatGPT in Software Security Applications0
Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks0
Fine-grained Software Vulnerability Detection via Information Theory and Contrastive Learning0
C2RUST-BENCH: A Minimized, Representative Dataset for C-to-Rust Transpilation Evaluation0
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning0
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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