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cs.CR updates on arXiv.org

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
CryptoScratch: Developing and evaluating a block-based pr...
Nathan Percival, Pranathi Rayavaram, Sashank Narain, Claire Seun · 2023-02-23 · via cs.CR updates on arXiv.org

This paper presents the design, implementation, and evaluation of a new framework called CryptoScratch, which extends the Scratch programming environment with modern cryptographic algorithms (e.g., AES, RSA, SHA-256) implemented as visual blocks. Using the simple interface of CryptoScratch, K-12 students can study how to use cryptographic algorithms for services like confidentiality, authentication, and integrity protection; and then use these blocks to build complex modern cryptographic schemes (e.g., Pretty Good Privacy, Digital Signatures). In addition, we present the design and implementation of a Task Block that provides students instruction on various cryptography problems and verifies that they have successfully completed the problem. The task block also generates feedback, nudging learners to implement more secure solutions for cryptographic problems. An initial usability study was performed with 16 middle-school students where students were taught basic cryptographic concepts and then asked to complete tasks using those concepts. Once students had knowledge of a variety of basic cryptographic algorithms, they were asked to use those algorithms to implement complex cryptographic schemes such as Pretty Good Privacy and Digital Signatures. Using the successful implementation of the cryptographic and task blocks in Scratch, the initial testing indicated that $\approx 60\%$ of the students could quickly grasp and implement complex cryptography concepts using CryptoScratch, while $\approx 90\%$ showed comfort with cryptography concepts and use-cases. Based on the positive results from the initial testing, a larger study of students is being developed to investigate the effectiveness across the socioeconomic spectrum.