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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
A Comparative Analysis of Port Scanning Tool Efficacy
Jason M. Pittman · 2023-03-21 · via cs.CR updates on arXiv.org

Port scanning refers to the systematic exploration of networked computing systems. The goal of port scanning is to identify active services and associated information. Although this technique is often employed by malicious actors to locate vulnerable systems within a network, port scanning is also a legitimate method employed by IT professionals to troubleshoot network issues and maintain system security. In the case of the latter, cybersecurity practitioners use port scanning catalog exposed systems, identify potential misconfigurations, or test controls that may be running on a system. Existing literature has thoroughly established a taxonomy for port scanning. The taxonomy maps the types of scans as well as techniques. In fact, there are several tools mentioned repeatedly in the literature. Those are Nmap, Zmap, and masscan. Further, the presence of multiple tools signals that how a port scanner interacts with target systems impacts the output of the tool. In other words, the various tools may not behave identically or produce identical output. Yet, no work has been done to quantify the efficacy for these popular tools in a uniform, rigorous manner. Accordingly, we used a comparative experimental protocol to measure the accuracy, false positive, false negative, and efficiency of Nmap, Zmap, and masscan. The results show no difference between port scanners in general performance. However, the results revealed a statistically significant difference in efficiency. This information can be used to guide the selection of port scanning tools based on specific needs and requirements. As well, for researchers, the outcomes may also suggest areas for future work in the development novel port scanning tools.