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1. Pairing Security Advisories with Vulnerable Functions Using Open-Source LLMs

   Dunlap, Trevor; Meyers, John S; Reaves, Brad; Enck, William (July 2024, Proceedings of the Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA))

   As the reliance on open-source software dependencies increases, managing the security vulnerabilities in these dependencies becomes complex. State-of-the-art industry tools use reachability analysis of code to alert developers when security vulnerabilities in dependencies are likely to impact their projects. These tools heavily rely on precisely identifying the location of the vulnerability within the dependency, specifically vulnerable functions. However, the process of identifying vulnerable functions is currently either manual or uses a naive automated approach that falsely assumes all changed functions in a security patch link are vulnerable. In this paper, we explore using open-source large language models (LLMs) to improve pairing security advisories with vulnerable functions. We explore various prompting strategies, learning paradigms (i.e., zero-shot vs. few-shot), and show our approach generalizes to other open-source LLMs. Compared to the naive automated approach, we show a 173% increase in precision while only having an 18% decrease in recall. The significant increase in precision to enhance vulnerable function identification lays the groundwork for downstream techniques that depend on this critical information for security analysis and threat mitigation. 

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2. Argus: A Framework for Staged Static Taint Analysis of GitHub Workflows and Actions

   Muralee, Siddharth Muralee; Koishybayev, Igibek; Nahapetyan, Aleksandr; Tystahl, Greg; Reaves, Brad; Bianchi, Antonio; Enck, William; Kapravelos, Alexandros; Machiry, Aravind (August 2023, Proceedings of the USENIX conference)

   Millions of software projects leverage automated workflows, like GitHub Actions, for performing common build and deploy tasks. While GitHub Actions have greatly improved the software build process for developers, they pose significant risks to the software supply chain by adding more dependencies and code complexity that may introduce security bugs. This paper presents ARGUS, the first static taint analysis system for identifying code injection vulnerabilities in GitHub Actions. We used ARGUS to perform a large-scale evaluation on 2,778,483 Workflows referencing 31,725 Actions and discovered critical code injection vulnerabilities in 4,307 Workflows and 80 Actions. We also directly compared ARGUS to two existing pattern-based GitHub Actions vulnerability scanners, demonstrating that our system exhibits a marked improvement in terms of vulnerability detection, with a discovery rate more than seven times (7x) higher than the state-of-the-art approaches. These results demonstrate that command injection vulnerabilities in the GitHub Actions ecosystem are pervasive and require taint analysis to be detected. 

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3. ARGUS: A Framework for Staged Static Taint Analysis of GitHub Workflows and Actions

   Muralee, Siddharth; Koishybayev, Igibek; Nahapetyan, Aleksandr; Tystahl, Greg; Reaves, Brad; Bianchi, Antonio; Enck, William; Kapravelos, Alexandros; Machiry, Aravind (August 2023, Proceedings of the USENIX conference)

   Millions of software projects leverage automated workflows, like GitHub Actions, for performing common build and deploy tasks. While GitHub Actions have greatly improved the software build process for developers, they pose significant risks to the software supply chain by adding more dependencies and code complexity that may introduce security bugs. This paper presents ARGUS, the first static taint analysis system for identifying code injection vulnerabilities in GitHub Actions. We used ARGUS to perform a large-scale evaluation on 2,778,483 Workflows referencing 31,725 Actions and discovered critical code injection vulnerabilities in 4,307 Workflows and 80 Actions. We also directly compared ARGUS to two existing pattern-based GitHub Actions vulnerability scanners, demonstrating that our system exhibits a marked improvement in terms of vulnerability detection, with a discovery rate more than seven times (7x) higher than the state-of-the-art approaches. These results demonstrate that command injection vulnerabilities in the GitHub Actions ecosystem are not only pervasive but also require taint analysis to be detected. 

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4. 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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5. 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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