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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
Incentivizing Secure Software Development: the Role of Vo...
Ziyuan Huang, Gergely Biczók, Mingyan Liu · 2024-01-17 · via cs.CR updates on arXiv.org

Misaligned incentives in secure software development have long been the focus of research in the economics of security. Product liability, a powerful legal framework in other industries, has been largely ineffective for software products until recent times. However, the rapid regulatory responses to recent global cyber attacks by both the United States and the European Union, together with the (relative) success of the General Data Protection Regulation in defining both duty and standard of care for software vendors, may enable regulators to use liability to re-align incentives for the benefit of the digital society. Specifically, the recent United States National Cybersecurity Strategy suggests shifting responsibility for cyber incidents back to software vendors. In doing so, the strategy also puts forward the concept of the liability waiver: if a software company voluntarily undergoes and passes an IT security audit, its future product liability is (fully or partially) waived. In this paper, we analyze this audit scenario from the perspective of the software vendor and the auditor, respectively. From the vendor's view, this is formulated as a sequential decision problem: a vendor with a product or process needs to pass a mandatory audit to release the product onto the market; it is allowed to go through the audit repeatedly, and thus the vendor needs to determine what level of effort to put into the product following each failed test. We show that the optimal strategy for an opt-in vendor is to never quit and to exert cumulative investments in either a ``one-and-done'' or ``incremental'' manner. From the auditor's view, we examine what type of audit might be the most effective in incentivizing voluntary participation and, at the same time, a more desirable effort from the vendor. We also showed how dynamic audits can be exploited to increase the vendor's incentivizable investment.