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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
Is Your Data Gone? Comparing Perceived Effectiveness of T...
Sarah Diesburg, C. Adam Feldhaus, Mojtaba Al Fardan, Jonathan Sc · 2015-12-30 · via cs.CR updates on arXiv.org

Previous studies have shown that many users do not use effective data deletion techniques upon sale or surrender of storage devices. A logical assumption is that many users are still confused concerning proper sanitization techniques of devices upon surrender. This paper strives to measure this assumption through a buyback study with a survey component. We recorded participants' thoughts and beliefs concerning deletion, as well as general demographic information, in relation to actual deletion effectiveness on USB thumb drives. Thumb drives were chosen for this study due to their relative low cost, ease of use, and ubiquity. In addition, we also bought used thumb drives from eBay and Amazon Marketplace to use as a comparison to the wider world. We found that there is no statistically significant difference between buyback and market drives in terms of deletion methods nor presence of sensitive data, and thus our study may be predictive of the perceptions of the market sellers. In our combined data sets, we found over 60% of the drives tested still had recoverable sensitive data, and in the buyback group, we found no correlation between users' perceived versus actual effectiveness of deletion methods. Our results suggest the security community may need to take a different approach to increase the usability, availability, and/or necessity of strong deletion methods.