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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
MobChain: Three-Way Collusion Resistance in Witness-Orien...
Faheem Zafar, Abid Khan, Saif Ur Rehman Malik, Adeel Anjum, Mans · 2020-11-17 · via cs.CR updates on arXiv.org

Smart devices have accentuated the importance of geolocation information. Geolocation identification using smart devices has paved the path for incentive-based location-based services (LBS). A location proof is a digital certificate of the geographical location of a user, which can be used to access various LBS. However, a user full control over a device allows the tampering of location proof. Initially, to resist false proofs, two-party trusted centralized location proof systems (LPS) were introduced to aid the users in generating secure location proofs mutually. However, two-party protocols suffered from the collusion attacks by the participants of the protocol. Consequently, many witness-oriented LPS have emerged to mitigate collusion attacks in two-party protocols. However, witness-oriented LPS presented the possibility of three-way collusion attacks (involving the user, location authority, and the witness). The three-way collusion attacks are inevitable in all existing witness-oriented schemes. To mitigate the inability to resist three-way collusion of existing schemes, in this paper, we introduce a decentralized consensus protocol called as MobChain, where the selection of a witness and location authority is achieved through a distributed consensus of nodes in an underlying P2P network of a private blockchain. The persistent provenance data over the blockchain provides strong security guarantees, as a result, the forging and manipulation become impractical. MobChain provides secure location provenance architecture, relying on decentralized decision making for the selection of participants of the protocol to resist three-way collusion problem. Our prototype implementation and comparison with the state-of-the-art solutions show that MobChain is computationally efficient, highly available while improving the security of LPS.