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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
Cloudy with a Chance of Cyberattacks: Dangling Resources ...
2024-03-28 · via cs.CR updates on arXiv.org

Recent works showed that it is feasible to hijack resources on cloud platforms. In such hijacks, attackers can take over released resources that belong to legitimate organizations. It was proposed that adversaries could abuse these resources to carry out attacks against customers of the hijacked services, e.g., through malware distribution. However, to date, no research has confirmed the existence of these attacks. We identify, for the first time, real-life hijacks of cloud resources. This yields a number of surprising and important insights. First, contrary to previous assumption that attackers primarily target IP addresses, our findings reveal that the type of resource is not the main consideration in a hijack. Attackers focus on hijacking records that allow them to determine the resource by entering freetext. The costs and overhead of hijacking such records are much lower than those of hijacking IP addresses, which are randomly selected from a large pool. Second, identifying hijacks poses a substantial challenge. Monitoring resource changes, e.g., changes in content, is insufficient, since such changes could also be legitimate. Retrospective analysis of digital assets to identify hijacks is also arduous due to the immense volume of data involved and the absence of indicators to search for. To address this challenge, we develop a novel approach that involves analyzing data from diverse sources to effectively differentiate between malicious and legitimate modifications. Our analysis has revealed 20,904 instances of hijacked resources on popular cloud platforms. While some hijacks are short-lived (up to 15 days), 1/3 persist for more than 65 days. We study how attackers abuse the hijacked resources and find that, in contrast to the threats considered in previous work, the majority of the abuse (75%) is blackhat search engine optimization.