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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
SSO-Monitor: Fully-Automatic Large-Scale Landscape, Secur...
Maximilian Westers, Tobias Wich, Louis Jannett, Vladislav Mladen · 2023-02-02 · via cs.CR updates on arXiv.org

Single Sign-On (SSO) shifts the crucial authentication process on a website to to the underlying SSO protocols and their correct implementation. To strengthen SSO security, organizations, such as IETF and W3C, maintain advisories to address known threats. One could assume that these security best practices are widely deployed on websites. We show that this assumption is a fallacy. We present SSO-MONITOR, an open-source fully-automatic large-scale SSO landscape, security, and privacy analysis tool. In contrast to all previous work, SSO-MONITOR uses a highly extensible, fully automated workflow with novel visual-based SSO detection techniques, enhanced security and privacy analyses, and continuously updated monitoring results. It receives a list of domains as input to discover the login pages, recognize the supported Identity Providers (IdPs), and execute the SSO. It further reveals the current security level of SSO in the wild compared to the security best practices on paper. With SSO-MONITOR, we automatically identified 1,632 websites with 3,020 Apple, Facebook, or Google logins within the Tranco 10k. Our continuous monitoring also revealed how quickly these numbers change over time. SSO-MONITOR can automatically login to each SSO website. It records the logins by tracing HTTP and in-browser communication to detect widespread security and privacy issues automatically. We introduce a novel deep-level inspection of HTTP parameters that we call SMARTPARMS. Using SMARTPARMS for security analyses, we uncovered URL parameters in 5 Client Application (Client) secret leakages and 337 cases with weak CSRF protection. We additionally identified 447 cases with no CSRF protection, 342 insecure SSO flows and 9 cases with nested URL parameters, leading to an open redirect in one case. SSO-MONITOR reveals privacy leakages that deanonymize users in 200 cases.