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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
Catch Me (On Time) If You Can: Understanding the Effectiv...
Simon Bell, Kenny Paterson, Lorenzo Cavallaro · 2019-12-05 · via cs.CR updates on arXiv.org

With more than 500 million daily tweets from over 330 million active users, Twitter constantly attracts malicious users aiming to carry out phishing and malware-related attacks against its user base. It therefore becomes of paramount importance to assess the effectiveness of Twitter's use of blacklists in protecting its users from such threats. We collected more than 182 million public tweets containing URLs from Twitter's Stream API over a 2-month period and compared these URLs against 3 popular phishing, social engineering, and malware blacklists, including Google Safe Browsing (GSB). We focus on the delay period between an attack URL first being tweeted to appearing on a blacklist, as this is the timeframe in which blacklists do not warn users, leaving them vulnerable. Experiments show that, whilst GSB is effective at blocking a number of social engineering and malicious URLs within 6 hours of being tweeted, a significant number of URLs go undetected for at least 20 days. For instance, during one month, we discovered 4,930 tweets containing URLs leading to social engineering websites that had been tweeted to over 131 million Twitter users. We also discovered 1,126 tweets containing 376 blacklisted Bitly URLs that had a combined total of 991,012 clicks, posing serious security and privacy threats. In addition, an equally large number of URLs contained within public tweets remain in GSB for at least 150 days, raising questions about potential false positives in the blacklist. We also provide evidence to suggest that Twitter may no longer be using GSB to protect its users.