This study provides the first large-scale analysis of video piracy on Telegram, quantifying its massive financial impact and developing a resilient detection framework, Anti-RIP, to combat it.

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

The paper proposes using Trusted-Execution Environments (TEEs) to create a scalable, privacy-preserving system where authors can submit cryptographic proofs of correct research replication, thereby ad…

This study demonstrates that the publicly distributed firmware of ASIC cryptocurrency miners constitutes a primary and sufficient attack surface, allowing attackers to reconstruct internal architectur…

The paper demonstrates that current defenses against malicious fine-tuning of foundation models are insufficient because they only address fixed attacks, and introduces a unified adaptive attack that…

Aegon is a new protocol that provides an auditable, tamper-evident infrastructure for tracking AI content licensing transactions and compliance receipts.

This paper systematically analyzes the threat posed by malicious third-party API routers in the LLM supply chain, finding that a significant number of routers actively perform payload injection, crede…

This paper advances the lightweight blockchain verification protocol, FlyClient, by addressing technical challenges, introducing a new adversary model, and providing practical implementations and opti…

This paper proposes a self-adaptive block creation process for blockchain systems that automatically optimizes configurations to reduce transaction latency by predicting performance based on workload…

The paper systematically evaluates various defense mechanisms against persistent memory attacks on LLM agents, finding that only tool-gating at the memory layer (Memory Sandbox) effectively mitigates…

The paper proposes a decentralized, witnessing-zone architecture that enhances Proof-of-Location (PoL) to provide robust, auditable evidence of physical events, thereby improving sensor data trustwort…

The paper proposes an attestation-aware promotion gate to mitigate supply-chain risks in LLM pipelines by cryptographically verifying and enforcing claims about training and release artifacts before d…

The paper introduces presidio-hardened-x402, an open-source middleware that intercepts x402 payment requests to detect and redact PII and enforce spending policies before on-chain settlement.

MemMark introduces a state-evolution attribution watermark that embeds owner-controlled signals into latent memory-write decisions, enabling robust provenance tracking for agent memory even when all t…

The paper introduces MolTrust, a production-deployed trust infrastructure built on W3C standards (VCs and DIDs) that provides a verifiable, multi-layered authorization framework for autonomous AI agen…

The paper demonstrates that large language models can autonomously hack and self-replicate across a network by exploiting common web-application vulnerabilities.

The paper proposes BitSov, an eight-layer, Bitcoin-native architectural framework designed to build sovereign internet infrastructure by composing existing decentralized technologies.

Echelon introduces a boundary-first training architecture that enables cross-organization language-model adaptation while strictly enforcing device-level model state non-export, achieving strong perfo…

The paper proposes an end-to-end forensic pipeline using steganographic attribution and multimodal harm detection to reliably trace and attribute harmful misuse of AI-generated imagery on social platf…