The paper analyzes a large dataset of JavaScript packages to demonstrate that a small number of vulnerable dependencies can propagate vulnerabilities across a disproportionately large number of packag…

The paper proposes a comprehensive cryptographic distribution provenance system to structurally defend against dependency confusion attacks in software package ecosystems.

The paper introduces SynthChain, a comprehensive, multi-source synthetic testbed and dataset that demonstrates that detecting advanced software supply chain attacks requires fusing evidence from multi…

The paper introduces 'software supply chain smells,' structural indicators of security risks in third-party dependencies, and presents Dirty-Waters, a tool that detects these smells, finding that diff…

This report summarizes the key takeaways from the S3C2 Summit 2025-09, a gathering of industry practitioners focused on identifying best practices and challenges in securing modern software supply cha…

The paper proposes a graph-learning approach to predict multi-vulnerability attack chains within software supply chains, achieving high accuracy on both component classification and cascade prediction…

This study provides an ecosystem-scale measurement of commit signing on GitHub, finding that current signing adoption rates are misleading and that developers struggle to maintain consistent, long-ter…

The paper introduces LLMVD.js, a multi-stage LLM agent pipeline that effectively detects and confirms taint-style vulnerabilities in Node.js packages, achieving significantly higher confirmation rates…

The paper argues that current Software Bills of Materials (SBOMs) are fundamentally flawed due to a lack of shared understanding regarding what constitutes a 'component,' demonstrating that existing t…

This paper systematically analyzes the threat posed by malicious third-party API routers in the LLM supply chain, finding that a significant number of routers actively perform payload injection, crede…

This paper introduces Dependency Steering, a novel attack paradigm demonstrating that malicious agent skills can actively bias LLM coding agents to use attacker-controlled packages, posing a significa…

The paper introduces a provenance-aware vulnerability analysis approach that accurately identifies cross-ecosystem vulnerabilities in Python applications by resolving vendored native libraries to spec…

The paper proposes a dynamic queueing framework that estimates an organization's cyber resources and attack surface dynamics by analyzing the timestamps of vulnerabilities and fixes, achieving high ac…

This paper empirically demonstrates that current Static Application Security Testing (SAST) tools are fundamentally unreliable against common JavaScript obfuscation techniques, showing that obfuscatio…

The paper proposes a novel semi-automated method to perform continuous threat modeling by inferring the actual system architecture from combined static configuration and dynamic network flow data, sig…

This paper analyzes RPKI specifications, demonstrating that vague or conflicting requirements in dozens of RFCs cause systemic vulnerabilities in real-world implementations, leading to 61 undocumented…

VIPER-MCP is a novel, end-to-end automated framework that detects and dynamically confirms the exploitability of taint-style vulnerabilities in Model Context Protocol (MCP) servers, achieving high-fid…

This paper conducts a large-scale empirical study demonstrating that Java library exploits can accurately identify affected versions, achieving high recall and precision, and proposes strategies for e…

The paper reframes manufacturing ransomware recovery from a simple backup restoration task to a complex critical-infrastructure continuity problem, proposing Minimum Viable Factory Recovery (MVF Recov…

This paper provides the first comprehensive security analysis of the Agent Skills framework, identifying severe structural vulnerabilities that require fundamental architectural changes rather than si…