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 4150 of 216 papers

TitleStatusHype
D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential AnalysisCode1
Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart ContractsCode1
Stack-based Buffer Overflow Detection using Recurrent Neural NetworksCode1
Trex: Learning Execution Semantics from Micro-Traces for Binary SimilarityCode1
CORE: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks0
SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models0
Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection0
Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches0
Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study0
Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning DataCode0
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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