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Soham Sanjay Deo

Publications and source records attributed to Soham Sanjay Deo.

2 recordsLinked to original sources

Is Your Private Information Logged? An Empirical Study on Android App Logs

With the rapid growth of mobile apps, users' concerns about their privacy have become increasingly prominent. Android app logs serve as crucial computer resources, aiding developers in debugging and monitoring the status of Android apps, while also containing a wealth of software system information. Previous studies have acknowledged privacy leaks in software logs and Android apps as significant issues without providing a comprehensive view of the privacy leaks in Android app logs. In this study, we build a comprehensive dataset of Android app logs and conduct an empirical study to analyze the status and severity of privacy leaks in Android app logs. Our study comprises three aspects: (1) Understanding real-world developers' concerns regarding privacy issues related to software logs; (2) Studying privacy leaks in the Android app logs; (3) Investigating the characteristics of privacy-leaking Android app logs and analyzing the reasons behind them. Our study reveals five different categories of concerns from real-world developers regarding privacy issues related to software logs and the prevalence of privacy leaks in Android app logs, with the majority stemming from developers' unawareness of such leaks. Additionally, our study provides developers with suggestions to safeguard their privacy from being logged.

cs.SE↗

Comprehensive Evaluation of ChatGPT Reliability Through Multilingual Inquiries

ChatGPT is currently the most popular large language model (LLM), with over 100 million users, making a significant impact on people's lives. However, due to the presence of jailbreak vulnerabilities, ChatGPT might have negative effects on people's lives, potentially even facilitating criminal activities. Testing whether ChatGPT can cause jailbreak is crucial because it can enhance ChatGPT's security, reliability, and social responsibility. Inspired by previous research revealing the varied performance of LLMs in different language translations, we suspected that wrapping prompts in multiple languages might lead to ChatGPT jailbreak. To investigate this, we designed a study with a fuzzing testing approach to analyzing ChatGPT's cross-linguistic proficiency. Our study includes three strategies by automatically posing different formats of malicious questions to ChatGPT: (1) each malicious question involving only one language, (2) multilingual malicious questions, (3) specifying that ChatGPT responds in a language different from the prompts. In addition, we also combine our strategies by utilizing prompt injection templates to wrap the three aforementioned types of questions. We examined a total of 7,892 Q&A data points, discovering that multilingual wrapping can indeed lead to ChatGPT's jailbreak, with different wrapping methods having varying effects on jailbreak probability. Prompt injection can amplify the probability of jailbreak caused by multilingual wrapping. This work provides insights for OpenAI developers to enhance ChatGPT's support for language diversity and inclusion.

cs.SE↗