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cs.CR updates on arXiv.org

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 An AI Agent Execution Environment to Safeguard User Data 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 Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
An Empirical Study on Workflows and Security Policies in ...
Jessy Ayala, Joshua Garcia · 2023-05-25 · via cs.CR updates on arXiv.org

In open-source projects, anyone can contribute, so it is important to have an active continuous integration and continuous delivery (CI/CD) pipeline in addition to a protocol for reporting security concerns, especially in projects that are widely used and belong to the software supply chain. Many of these projects are hosted on GitHub, where maintainers can create automated workflows using GitHub Actions, introduced in 2019, for inspecting proposed changes to source code and defining a security policy for reporting vulnerabilities. We conduct an empirical study to measure the usage of GitHub workflows and security policies in thousands of popular repositories based on the number of stars. After querying the top one-hundred and top one-thousand repositories from all 181 trending GitHub topics, and the top 4,900 overall repositories, totaling just over 173 thousand projects, we find that 37% of projects have workflows enabled and 7% have a security policy in place. Using the top 60 repositories from each of the 34 most popular programming languages on GitHub, 2,040 projects total, we find that 57% of projects have workflows enabled and 17% have a security policy in place. Furthermore, from those top repositories that have support for GitHub CodeQL static analysis, which performs bug and vulnerability checks, only 13.5% have it enabled; in fact, we find that only 1.7% of the top repositories using Kotlin have an active CodeQL scanning workflow. These results highlight that open-source project maintainers should prioritize configuring workflows, enabling automated static analysis whenever possible, and defining a security policy to prevent vulnerabilities from being introduced or remaining in source code.