Tags → #vulnerability detection

Verite is a new fuzzing tool designed to enhance the security of decentralized finance applications by identifying vulnerabilities and optimizing profit potential in smart contracts.

Recent research highlights innovative methodologies that utilize Artificial Intelligence to enhance vulnerability detection and prioritization in cybersecurity.

A recent study highlights the potential of transformer-based models for automated vulnerability detection in cybersecurity, emphasizing their integration into CI/CD pipelines to enhance early threat identification.

Recent research explores the integration of artificial intelligence with blockchain technologies to enhance smart contract security and improve real-time vulnerability detection.

Researchers have introduced the AF-STip framework, which utilizes graph-based methodologies and attention mechanisms to enhance the accuracy and interpretability of smart contract vulnerability detection in blockchain technology.

Recent research has introduced advancements in directed fuzzing techniques that aim to improve the accuracy and efficiency of vulnerability detection in cybersecurity.

Recent research highlights the importance of advanced modeling and automated tools in enhancing cloud security, particularly in identifying vulnerabilities within Identity and Access Management configurations.

Recent research highlights advancements in software vulnerability detection through enhanced data sanitization techniques, aiming to improve accuracy and risk management for organizations.

Recent advancements in cloud security research have led to improved methodologies for vulnerability detection and threat mitigation, highlighting the importance of continuous monitoring and proactive policy management.

Recent research has investigated the use of Language Models to enhance vulnerability detection in software, highlighting their potential and identifying areas for further development.