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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. 

In 2022, the Anti-Phishing Working Group reported a 70% increase in SMS and voice phishing attacks. Hard data on SMS phishing is hard to come by, as are insights into how SMS phishers operate. Lack of visibility prevents law enforcement, regulators, providers, and researchers from understanding and confronting this growing problem. In this paper, we present the results of extracting phishing messages from over 200 million SMS messages posted over several years on 11 public SMS gateways on the web. From this dataset we identify 67,991 phishing messages, link them together into 35,128 campaigns based on sharing near-identical content, then identify related campaigns that share infrastructure to identify over 600 distinct SMS phishing operations. This expansive vantage point enables us to determine that SMS phishers use commodity cloud and web infrastructure in addition to self-hosted URL shorteners, their infrastructure is often visible days or weeks on certificate transparency logs earlier than their messages, and they reuse existing phishing kits from other phishing modalities. We are also the first to examine in-place network defenses and identify the public forums where abuse facilitators advertise openly. These methods and findings provide industry and researchers new directions to explore to combat the growing problem of SMS phishing. 

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. 

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.