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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
Sludge for Good: Slowing and Imposing Costs on Cyber Atta...
Josiah Dykstra, Kelly Shortridge, Jamie Met, Douglas Hough · 2022-11-30 · via cs.CR updates on arXiv.org

Choice architecture describes the design by which choices are presented to people. Nudges are an aspect intended to make "good" outcomes easy, such as using password meters to encourage strong passwords. Sludge, on the contrary, is friction that raises the transaction cost and is often seen as a negative to users. Turning this concept around, we propose applying sludge for positive cybersecurity outcomes by using it offensively to consume attackers' time and other resources. To date, most cyber defenses have been designed to be optimally strong and effective and prohibit or eliminate attackers as quickly as possible. Our complimentary approach is to also deploy defenses that seek to maximize the consumption of the attackers' time and other resources while causing as little damage as possible to the victim. This is consistent with zero trust and similar mindsets which assume breach. The Sludge Strategy introduces cost-imposing cyber defense by strategically deploying friction for attackers before, during, and after an attack using deception and authentic design features. We present the characteristics of effective sludge, and show a continuum from light to heavy sludge. We describe the quantitative and qualitative costs to attackers and offer practical considerations for deploying sludge in practice. Finally, we examine real-world examples of U.S. government operations to frustrate and impose cost on cyber adversaries.