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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
Characterizing Phishing Pages by JavaScript Capabilities
[Submitted on 16 Sep 2025 (v1), last revised 17 Jul 2026 (this v · 2025-09-16 · via cs.CR updates on arXiv.org

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Abstract:Phishers achieve large-scale attacks by using ready-to-deploy phishing websites (phishing kits) to rapidly launch campaigns that leverage specific data exfiltration, evasion, or mimicry techniques. In contrast, researchers and defenders continue to rely on manual analysis to identify features for kit fingerprinting. In this paper, we examine the link between a page's client-side behavior and the underlying phishing kit used, enabling automated aggregation of phishing pages. Our key insight is that client-side techniques make heavy use of browser APIs, which, in turn, differentiate underlying kits based on their feature sets. Using an instrumented browser and a URL fuzzing utility, we collected traces from 1,328,917 pages and recovered kit archives for 4,180 pages between August 2023 and January 2025. For the labeled subset, we find that clustering based on the set of browser APIs executed yields 98% accuracy in grouping them by the underlying kit. We also find that 434,495 phishing pages execute enough browser APIs to cluster into 9,306 clusters, compressing multi-lingual phishing pages across various domains into a single cluster. Our findings show that analysts and researchers can leverage the complexity of client-side phishing code to track phishers' kit deployments in the wild.

Submission history

From: Aleksandr Nahapetyan [view email]
[v1] Tue, 16 Sep 2025 15:39:23 UTC (1,815 KB)
[v2] Fri, 17 Jul 2026 04:17:05 UTC (1,853 KB)