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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
Evaluating the Impact of AbuseHUB on Botnet Mitigation
Michel van Eeten, Qasim Lone, Giovane Moura, Hadi Asghari, Macie · 2016-12-10 · via cs.CR updates on arXiv.org

This documents presents the final report of a two-year project to evaluate the impact of AbuseHUB, a Dutch clearinghouse for acquiring and processing abuse data on infected machines. The report was commissioned by the Netherlands Ministry of Economic Affairs, a co-funder of the development of AbuseHUB. AbuseHUB is the initiative of 9 Internet Service Providers, SIDN (the registry for the .nl top-level domain) and Surfnet (the national research and education network operator). The key objective of AbuseHUB is to improve the mitigation of botnets by its members. We set out to assess whether this objective is being reached by analyzing malware infection levels in the networks of AbuseHUB members and comparing them to those of other Internet Service Providers (ISPs). Since AbuseHUB members together comprise over 90 percent of the broadband market in the Netherlands, it also makes sense to compare how the country as a whole has performed compared to other countries. This report complements the baseline measurement report produced in December 2013 and the interim report from March 2015. We are using the same data sources as in the interim report, which is an expanded set compared to the earlier baseline report and to our 2011 study into botnet mitigation in the Netherlands.