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 201216 of 216 papers

TitleStatusHype
Are Sparse Autoencoders Useful for Java Function Bug Detection?Code0
Old but Gold: Reconsidering the value of feedforward learners for software analyticsCode0
Program Semantic Inequivalence Game with Large Language ModelsCode0
Statement-Level Vulnerability Detection: Learning Vulnerability Patterns Through Information Theory and Contrastive LearningCode0
How to Select Pre-Trained Code Models for Reuse? A Learning PerspectiveCode0
Can You Really Trust Code Copilots? Evaluating Large Language Models from a Code Security PerspectiveCode0
AndroShield: Automated Android Applications Vulnerability Detection, a Hybrid Static and Dynamic Analysis ApproachCode0
Reasoning with LLMs for Zero-Shot Vulnerability DetectionCode0
DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilitiesCode0
eyeballvul: a future-proof benchmark for vulnerability detection in the wildCode0
Responsible Development of Offensive AICode0
Automated Progressive Red TeamingCode0
Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic DatasetsCode0
SV-TrustEval-C: Evaluating Structure and Semantic Reasoning in Large Language Models for Source Code Vulnerability AnalysisCode0
SAFE: Self-Attentive Function Embeddings for Binary SimilarityCode0
SySeVR: A Framework for Using Deep Learning to Detect Software VulnerabilitiesCode0
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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