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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
Pre-hijacked accounts: An Empirical Study of Security Fai...
Avinash Sudhodanan, Andrew Paverd · 2022-05-20 · via cs.CR updates on arXiv.org

The ubiquity of user accounts in websites and online services makes account hijacking a serious security concern. Although previous research has studied various techniques through which an attacker can gain access to a victim's account, relatively little attention has been directed towards the process of account creation. The current trend towards federated authentication (e.g., Single Sign-On) adds an additional layer of complexity because many services now support both the classic approach in which the user directly sets a password, and the federated approach in which the user authenticates via an identity provider. Inspired by previous work on preemptive account hijacking [Ghasemisharif et al., USENIX SEC 2018], we show that there exists a whole class of account pre-hijacking attacks. The distinctive feature of these attacks is that the attacker performs some action before the victim creates an account, which makes it trivial for the attacker to gain access after the victim has created/recovered the account. Assuming a realistic attacker who knows only the victim's email address, we identify and discuss five different types of account pre-hijacking attacks. To ascertain the prevalence of such vulnerabilities in the wild, we analyzed 75 popular services and found that at least 35 of these were vulnerable to one or more account pre-hijacking attacks. Whilst some of these may be noticed by attentive users, others were completely undetectable from the victim's perspective. Finally, we investigated the root cause of these vulnerabilities and present a set of security requirements to prevent such vulnerabilities arising in future.