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On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
Gone in Six Characters: Short URLs Considered Harmful for...
Martin Georgiev, Vitaly Shmatikov · 2016-04-11 · via cs.CR updates on arXiv.org

Modern cloud services are designed to encourage and support collaboration. To help users share links to online documents, maps, etc., several services, including cloud storage providers such as Microsoft OneDrive and mapping services such as Google Maps, directly integrate URL shorteners that convert long, unwieldy URLs into short URLs, consisting of a domain such as 1drv.ms or goo.gl and a short token. In this paper, we demonstrate that the space of 5- and 6-character tokens included in short URLs is so small that it can be scanned using brute-force search. Therefore, all online resources that were intended to be shared with a few trusted friends or collaborators are effectively public and can be accessed by anyone. This leads to serious security and privacy vulnerabilities. In the case of cloud storage, we focus on Microsoft OneDrive. We show how to use short-URL enumeration to discover and read shared content stored in the OneDrive cloud, including even files for which the user did not generate a short URL. 7% of the OneDrive accounts exposed in this fashion allow anyone to write into them. Since cloud-stored files are automatically copied into users' personal computers and devices, this is a vector for large-scale, automated malware injection. In the case of online maps, we show how short-URL enumeration reveals the directions that users shared with each other. For many individual users, this enables inference of their residential addresses, true identities, and extremely sensitive locations they visited that, if publicly revealed, would violate medical and financial privacy.