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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 of Developers' Discussions about Secur...
Roland Croft, Yongzheng Xie, Mansooreh Zahedi, M. Ali Babar, Chr · 2021-07-29 · via cs.CR updates on arXiv.org

Given programming languages can provide different types and levels of security support, it is critically important to consider security aspects while selecting programming languages for developing software systems. Inadequate consideration of security in the choice of a programming language may lead to potential ramifications for secure development. Whilst theoretical analysis of the supposed security properties of different programming languages has been conducted, there has been relatively little effort to empirically explore the actual security challenges experienced by developers. We have performed a large-scale study of the security challenges of 15 programming languages by quantitatively and qualitatively analysing the developers' discussions from Stack Overflow and GitHub. By leveraging topic modelling, we have derived a taxonomy of 18 major security challenges for 6 topic categories. We have also conducted comparative analysis to understand how the identified challenges vary regarding the different programming languages and data sources. Our findings suggest that the challenges and their characteristics differ substantially for different programming languages and data sources, i.e., Stack Overflow and GitHub. The findings provide evidence-based insights and understanding of security challenges related to different programming languages to software professionals (i.e., practitioners or researchers). The reported taxonomy of security challenges can assist both practitioners and researchers in better understanding and traversing the secure development landscape. This study highlights the importance of the choice of technology, e.g., programming language, in secure software engineering. Hence, the findings are expected to motivate practitioners to consider the potential impact of the choice of programming languages on software security.