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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
Looney Tunes: Exposing the Lack of DRM Protection in Indi...
Ahaan Dabholkar, Sourya Kakarla, Dhiman Saha · 2021-03-30 · via cs.CR updates on arXiv.org

Numerous studies have shown that streaming is now the most preferred way of consuming multimedia content and this is evidenced by the proliferation in the number of streaming service providers as well as the exponential growth in their subscriber base. Riding on the advancements in low cost electronics, high speed communication and extremely cheap data, Over-The-Top (OTT) music streaming is now the norm in the music industry and is worth millions of dollars. This is especially true in India where major players offer the so called freemium models which have active monthly user bases running in to the millions. These services namely, Gaana, Airtel Wynk and JioSaavn attract a significantly bigger audience than their 100% subscription based peers like Amazon Prime Music, Apple Music etc. Given their ubiquity and market dominance, it is pertinent to do a systematic analysis of these platforms so as to ascertain their potential as hotbeds of piracy. This work investigates the resilience of the content protection systems of the four biggest music streaming services (by subscriber base) from India, namely Airtel Wynk, Ganna, JioSaavn and Hungama. By considering the Digital Rights Management (DRM) system employed by Spotify as a benchmark, we analyse the security of these platforms by attempting to steal the streamed content efficiently. Finally, we present a holistic overview of the flaws in their security mechanisms and discuss possible mitigation strategies. To the best of our knowledge, this work constitutes the first attempt to analyze security of OTT music services from India. Our results further confirm the time tested belief that security through obscurity is not a long term solution and leaves such platforms open to piracy and a subsequent loss of revenue for all the stakeholders.