CPU-Auth is a novel authentication method that uses unique variations in a device’s CPU, measured via the Dynamic Voltage and Frequency Scaling (DVFS) governor from a browser, to create a hardware-based device fingerprint. The approach is evaluated on over 50,000 data traces using distance‑based and deep learning techniques. The study highlights how CPU-Auth could serve as a secondary authentication factor, addressing flaws in existing methods such as SMS, biometrics, and token‑based systems.
arXiv:2608. 16334v1 Announce Type: new Abstract: Keystroke dynamics (typing patterns) can be used as a behavioural biometric modality for user authentication, with applications such as fraud prevention.
The paper introduces BIND, a framework that binds a human’s biometric data to an AI agent’s identity and task-specific authority, enabling secure, real‑time delegation of control. By generating a token that an AI agent presents to an Identity Auditor, the system performs biometric authentication and recovers the agent’s ID and scope, providing non‑repudiable proof of human oversight. A practical implementation using face features and a fuzzy commitment scheme with turbo error‑correcting codes achieves a 96% true match rate at zero false match rate and supports 1024‑bit agent tokens.
arXiv:2608.20884v1 Announce Type: new Abstract: Face recognition systems (FRSs) are increasingly deployed in critical real-world services for authentication, such as banking applications and airport...
arXiv:2606. 31272v1 Announce Type: cross Abstract: AI agents increasingly acquire and execute skills at runtime: bundles of prompt instructions, executable code, and tool declarations fetched from marketplaces and other agents.
arXiv:2606. 21900v2 Announce Type: replace-cross Abstract: Continuous authentication for mobile and zero-trust systems requires nonintrusive evidence confirming the enrolled user-device context remains valid after initial login.
RouteScan is a non‑intrusive auditing framework that detects harmful behavior in Mixture‑of‑Experts (MoE) large language models by analyzing expert‑routing telemetry captured from GPU execution. It uses the number of active GPU threads during the prefilling phase as a micro‑architectural fingerprint to isolate cross‑domain risk indicators and precisely identify malicious prompts. Evaluations on four open‑source MoE LLMs show strong generalization with AUROC > 0.91 on unseen harmful domains, while privacy tests indicate that full prompts cannot be reliably recovered from aggregated telemetry.
arXiv:2607. 20890v1 Announce Type: new Abstract: On-device federated learning (FL) enables privacy-preserving and personalized model training on resource-constrained devices such as smartphones and IoT nodes.
arXiv:2607. 23389v1 Announce Type: cross Abstract: Conventional CAPTCHAs pose puzzles that modern AI systems increasingly solve, while behavioral and cryptographic-attestation defenses carry privacy or enrollment costs.
arXiv:2407. 10887v4 Announce Type: replace-cross Abstract: Growing concerns over the theft and misuse of Large Language Models (LLMs) underscore the need for effective fingerprinting to link a model to its original version and detect misuse.
arXiv:2609.06271v1 Announce Type: cross Abstract: Some low-cost Internet of Things (IoT) sensor deployments lack device-level source authentication, leaving them vulnerable to impersonation or inject...
arXiv:2608. 10760v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) has become the de-facto interface for connecting LLM agents to enterprise tools, and adoption has been explosive: within a year, large organizations went from zero to dozens of internally built MCP servers.
arXiv:2607. 05518v1 Announce Type: cross Abstract: AI agents issue tool calls on the basis of text they cannot verify, so any party who controls part of the context can forge the appearance of authority.