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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 In-depth Analysis of Spam and Spammers
Dhinaharan Nagamalai, Beatrice Cynthia Dhinakaran, Jae Kwang Lee · 2010-12-08 · via cs.CR updates on arXiv.org

Electronic mail services have become an important source of communication for millions of people all over the world. Due to this tremendous growth, there has been a significant increase in spam traffic. Spam messes up user's inbox, consumes network resources and spread worms and viruses. In this paper we study the characteristics of spam and the technology used by spammers. In order to counter anti spam technology, spammers change their mode of operation, therefore continues evaluation of the characteristics of spam and spammers technology has become mandatory. These evaluations help us to enhance the existing anti spam technology and thereby help us to combat spam effectively. In order to characterize spam, we collected four hundred thousand spam mails from a corporate mail server for a period of 14 months from January 2006 to February 2007. For analysis we classified spam based on attachment and contents. We observed that spammers use software tools to send spam with attachment. The main features of this software are hiding sender's identity, randomly selecting text messages, identifying open relay machines, mass mailing capability and defining spamming duration. Spammers do not use spam software to send spam without attachment. From our study we observed that, four years old heavy users email accounts attract more spam than four years old light users mail accounts. Relatively new email accounts which are 14 months old do not receive spam. But in some special cases like DDoS attacks, we found that new email accounts receive spam and 14 months old heavy users email accounts have attracted more spam than 14 months old light users. We believe that this analysis could be useful to develop more efficient anti spam techniques.