The paper "LLbezpek: Leveraging Large Language Models for Vulnerability Detection" by Noble Saji Mathews explores the use of Large Language Models (LLMs) for detecting vulnerabilities in Android applications. Despite advancements in secure system development, Android apps remain susceptible to various vulnerabilities, necessitating effective detection methods. Current static and dynamic analysis tools have limitations such as high false positive rates and limited scope. Machine learning approaches have shown promise but are constrained by data requirements and feature engineering challenges. LLMs, with their vast parameters, have demonstrated significant potential in understanding semantics and programming languages, making them a promising tool for vulnerability detection. The study focuses on building an AI-driven workflow to assist developers in identifying and rectifying vulnerabilities. Experiments using the Ghera benchmark show that LLMs can correctly flag insecure apps in 91.67% of cases. The research also highlights the impact of different configurations on True Positive (TP) and False Positive (FP) rates. The authors propose a Python package called "LLB" to facilitate the scanning of Android projects for security vulnerabilities, offering a command-line interface and multiple scanning mechanisms. The case study on Vuldroid, a vulnerable Android application, demonstrates the effectiveness of the LLB tool in identifying and suggesting fixes for multiple known vulnerabilities. However, the study also identifies limitations, such as the need for continuous updates and retraining of LLMs to address evolving threats and vulnerabilities. The paper concludes that LLMs are powerful tools for Android vulnerability detection but require careful design and optimization of the analysis pipeline to improve their efficacy. The research opens new directions for further exploration, including integrating static analysis and improving the context provided to LLMs.The paper "LLbezpek: Leveraging Large Language Models for Vulnerability Detection" by Noble Saji Mathews explores the use of Large Language Models (LLMs) for detecting vulnerabilities in Android applications. Despite advancements in secure system development, Android apps remain susceptible to various vulnerabilities, necessitating effective detection methods. Current static and dynamic analysis tools have limitations such as high false positive rates and limited scope. Machine learning approaches have shown promise but are constrained by data requirements and feature engineering challenges. LLMs, with their vast parameters, have demonstrated significant potential in understanding semantics and programming languages, making them a promising tool for vulnerability detection. The study focuses on building an AI-driven workflow to assist developers in identifying and rectifying vulnerabilities. Experiments using the Ghera benchmark show that LLMs can correctly flag insecure apps in 91.67% of cases. The research also highlights the impact of different configurations on True Positive (TP) and False Positive (FP) rates. The authors propose a Python package called "LLB" to facilitate the scanning of Android projects for security vulnerabilities, offering a command-line interface and multiple scanning mechanisms. The case study on Vuldroid, a vulnerable Android application, demonstrates the effectiveness of the LLB tool in identifying and suggesting fixes for multiple known vulnerabilities. However, the study also identifies limitations, such as the need for continuous updates and retraining of LLMs to address evolving threats and vulnerabilities. The paper concludes that LLMs are powerful tools for Android vulnerability detection but require careful design and optimization of the analysis pipeline to improve their efficacy. The research opens new directions for further exploration, including integrating static analysis and improving the context provided to LLMs.