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cs.CR updates on arXiv.org

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
#h00t: Censorship Resistant Microblogging
Dustin Bachrach, Christopher Nunu, Dan S. Wallach, Matthew Wrigh · 2011-10-01 · via cs.CR updates on arXiv.org

Microblogging services such as Twitter are an increasingly important way to communicate, both for individuals and for groups through the use of hashtags that denote topics of conversation. However, groups can be easily blocked from communicating through blocking of posts with the given hashtags. We propose #h00t, a system for censorship resistant microblogging. #h00t presents an interface that is much like Twitter, except that hashtags are replaced with very short hashes (e.g., 24 bits) of the group identifier. Naturally, with such short hashes, hashtags from different groups may collide and #h00t users will actually seek to create collisions. By encrypting all posts with keys derived from the group identifiers, #h00t client software can filter out other groups' posts while making such filtering difficult for the adversary. In essence, by leveraging collisions, groups can tunnel their posts in other groups' posts. A censor could not block a given group without also blocking the other groups with colliding hashtags. We evaluate the feasibility of #h00t through traces collected from Twitter, showing that a single modern computer has enough computational throughput to encrypt every tweet sent through Twitter in real time. We also use these traces to analyze the bandwidth and anonymity tradeoffs that would come with different variations on how group identifiers are encoded and hashtags are selected to purposefully collide with one another.