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On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety
Unlocking Reproducibility: Automating re-Build Process fo...
Behnaz Hassanshahi, Trong Nhan Mai, Benjamin Selwyn Smith, Nicho · 2025-09-10 · via cs.CR updates on arXiv.org

Software ecosystems like Maven Central play a crucial role in modern software supply chains by providing repositories for libraries and build plugins. However, the separation between binaries and their corresponding source code in Maven Central presents a significant challenge, particularly when it comes to linking binaries back to their original build environment. This lack of transparency poses security risks, as approximately 84% of the top 1200 commonly used artifacts are not built using a transparent CI/CD pipeline. Consequently, users must place a significant amount of trust not only in the source code but also in the environment in which these artifacts are built. Rebuilding software artifacts from source provides a robust solution to improve supply chain security. This approach allows for a deeper review of code, verification of binary-source equivalence, and control over dependencies. However, challenges arise due to variations in build environments, such as JDK versions and build commands, which can lead to build failures. Additionally, ensuring that all dependencies are rebuilt from source across large and complex dependency graphs further complicates the process. In this paper, we introduce an extension to Macaron, an industry-grade open-source supply chain security framework, to automate the rebuilding of Maven artifacts from source. Our approach improves upon existing tools, by offering better performance in source code detection and automating the extraction of build specifications from GitHub Actions workflows. We also present a comprehensive root cause analysis of build failures in Java projects and propose a scalable solution to automate the rebuilding of artifacts, ultimately enhancing security and transparency in the open-source supply chain.