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…

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…

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 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 zero-trust supply-chain assurance rubric for O-RAN RIC applications to secure the entire lifecycle, from development to runtime.

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…

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…

This study conducts a large-scale longitudinal analysis of CodeQL, finding that while the tool is effective at detecting vulnerabilities, its detection capabilities are not guaranteed to be stable acr…

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 analyzes GitHub security advisories for LLM-integrated open-source systems, finding that while most vulnerabilities map to existing code-level weaknesses, the architectural risks like Supply…

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 introduces Neutral Prompting Attacks (NPA), a stealthy method showing that semantically benign prompts can covertly increase package hallucination in coding agents, creating new software sup…

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…

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

This experience report details the process and developer perceptions of integrating log-based fraud detection into an Agile workflow, providing practical best practices for embedding security analytic…

The paper proposes an attestation-aware promotion gate to mitigate supply-chain risks in LLM pipelines by cryptographically verifying and enforcing claims about training and release artifacts before d…

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 an evidence-driven protocol combining Deterministic Build Systems and Trusted Execution Environments to provide cryptographically verifiable guarantees of software artifact integrit…

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…

PyFEX introduces a resilient forced-execution engine to exhaustively analyze Python code, successfully detecting previously unknown malicious packages and binaries in the Python ecosystem.