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1. Understanding security mistakes developers make: Qualitative analysis from Build It, Break It, Fix It

   Daniel Votipka, Kelsey R. ( August 2020 , USENIX Security Symposium)

   null (Ed.)

   Secure software development is a challenging task requiring consideration of many possible threats and mitigations.This paper investigates how and why programmers, despite a baseline of security experience, make security-relevant errors.To do this, we conducted an in-depth analysis of 94 submissions to a secure-programming contest designed to mimic real-world constraints: correctness, performance, and security.In addition to writing secure code, participants were asked to search for vulnerabilities in other teams’ programs; in total, teams submitted 866 exploits against the submissions we considered. Over an intensive six-month period, we used iterative open coding to manually, but systematically, characterize each submitted project and vulnerability (including vulnerabilities we identified ourselves). We labeled vulnerabilities by type, attacker control allowed, and ease of exploitation,and projects according to security implementation strategy.Several patterns emerged. For example, simple mistakes were least common: only 21% of projects introduced such an error.Conversely, vulnerabilities arising from a misunderstanding of security concepts were significantly more common, appearing in 78% of projects. Our results have implications for improving secure-programming APIs, API documentation,vulnerability-finding tools, and security education. 

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2. Understanding security mistakes developers make: Qualitative analysis from Build It, Break It, Fix It

   Daniel Votipka, Kelsey R. ( January 2020 , USENIX Security Symposium)

   Secure software development is a challenging task requiring consideration of many possible threats and mitigations. This paper investigates how and why programmers, despite a baseline of security experience, make security-relevant errors. To do this, we conducted an in-depth analysis of 94 submissions to a secure-programming contest designed to mimic real-world constraints: correctness, performance, and security. In addition to writing secure code, participants were asked to search for vulnerabilities in other teams’ programs; in total, teams submitted 866 exploits against the submissions we considered. Over an intensive six-month period, we used iterative open coding to manually, but systematically, characterize each submitted project and vulnerability (including vulnerabilities we identified ourselves). We labeled vulnerabilities by type, attacker control allowed, and ease of exploitation, and projects according to security implementation strategy. Several patterns emerged. For example, simple mistakes were least common: only 21% of projects introduced such an error. Conversely, vulnerabilities arising from a misunderstanding of security concepts were significantly more common, appearing in 78% of projects. Our results have implications for improving secure-programming APIs, API documentation, vulnerability-finding tools, and security education. 

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3. In-IDE Code Generation from Natural Language: Promise and Challenges

   https://doi.org/10.1145/3487569  

   Xu, Frank F. ; Vasilescu, Bogdan ; Neubig, Graham ( April 2022 , ACM Transactions on Software Engineering and Methodology)

   A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs. One major difficulty of programming is turning concept into code , especially when dealing with the APIs of unfamiliar libraries. Recently, there has been a proliferation of machine learning methods for code generation and retrieval from natural language queries , but these have primarily been evaluated purely based on retrieval accuracy or overlap of generated code with developer-written code, and the actual effect of these methods on the developer workflow is surprisingly unattested. In this article, we perform the first comprehensive investigation of the promise and challenges of using such technology inside the PyCharm IDE, asking, “At the current state of technology does it improve developer productivity or accuracy, how does it affect the developer experience, and what are the remaining gaps and challenges?” To facilitate the study, we first develop a plugin for the PyCharm IDE that implements a hybrid of code generation and code retrieval functionality, and we orchestrate virtual environments to enable collection of many user events (e.g., web browsing, keystrokes, fine-grained code edits). We ask developers with various backgrounds to complete 7 varieties of 14 Python programming tasks ranging from basic file manipulation to machine learning or data visualization, with or without the help of the plugin. While qualitative surveys of developer experience are largely positive, quantitative results with regards to increased productivity, code quality, or program correctness are inconclusive. Further analysis identifies several pain points that could improve the effectiveness of future machine learning-based code generation/retrieval developer assistants and demonstrates when developers prefer code generation over code retrieval and vice versa. We release all data and software to pave the road for future empirical studies on this topic, as well as development of better code generation models. 

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4. An Ethnographic Understanding of Software (In)Security and a Co-Creation Model to Improve Secure Software Development

   Palombo, Hernan ; Ziaie Tabari, Armin ; Lende, Daniel ; Ligatti, Jay ; Ou, Xinming ( August 2020 , Proceedings of the Sixteenth Symposium on Usable Privacy and Security)

   We present an ethnographic study of secure software development processes in a software company using the anthropological research method of participant observation. Two PhD students in computer science trained in qualitative methods were embedded in a software company for 1.5 years of total research time. The researchers participated in everyday work activities such as coding and meetings, and observed software (in)security phenomena both through investigating historical data (code repositories and ticketing system records), and through pen-testing the developed software and observing developers’ and management’s reactions to the discovered vulnerabilities. Our study found that 1) security vulnerabilities are sometimes intentionally introduced and/or overlooked due to the difficulty in managing the various stakeholders’ responsibilities in an economic ecosystem, and cannot be simply blamed on developers’ lack of knowledge or skills; 2) accidental vulnerabilities discovered in the pen-testing process produce different reactions in the development team, often times contrary to what a security researcher would predict. These findings highlight the nuanced nature of the root causes of software vulnerabilities and indicate the need to take into account a significant amount of contextual information to understand how and why software vulnerabilities emerge during software development. Rather than simply addressing deficits in developer knowledge or practice, this research sheds light on at times forgotten human factors that significantly impact the security of software developed by actual companies. Our analysis also shows that improving software security in the development process can benefit from a co-creation model, where security experts work side by side with software developers to better identify security concerns and provide tools that are readily applicable within the specific context of the software development workflow. 

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5. CRYLOGGER: Detecting Crypto Misuses Dynamically

   Piccolboni, Luca ; Di Guglielmo, Giuseppe ; Carloni, Luca P. ; Sethumadhavan, Simha ( January 2021 , Proceedings of the IEEE Symposium on Security and Privacy)

   null (Ed.)

   Cryptographic (crypto) algorithms are the essential ingredients of all secure systems: crypto hash functions and encryption algorithms, for example, can guarantee properties such as integrity and confidentiality. Developers, however, can misuse the application programming interfaces (API) of such algorithms by using constant keys and weak passwords. This paper presents CRYLOGGER, the first open-source tool to detect crypto misuses dynamically. CRYLOGGER logs the parameters that are passed to the crypto APIs during the execution and checks their legitimacy offline by using a list of crypto rules. We compare CRYLOGGER with CryptoGuard, one of the most effective static tools to detect crypto misuses. We show that our tool complements the results of CryptoGuard, making the case for combining static and dynamic approaches. We analyze 1780 popular Android apps downloaded from the Google Play Store to show that CRYLOGGER can detect crypto misuses on thousands of apps dynamically and automatically. We reverse-engineer 28 Android apps and confirm the issues flagged by CRYLOGGER. We also disclose the most critical vulnerabilities to app developers and collect their feedback. 

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