Tootfinder

RE: https://indieweb.social/@web3isgreat/115811478189391065
Which leads to the question why a project to register intellectual property even needs millions which then may be stolen?

The Chicago-Kent, Illinois Tech IP Program cordially invites you to: "What’s the Skinny? Drug Labels and Patent Infringement," Tuesday, October 28, 2025 at 12-1 pm.
The panel is open to the public but please RSVP in advance here: https://

On 30 April, 1993, CERN published the complete source code to the #WWW protocols developed by @…, as well as his client and server software with this statement: “CERN relinquishes all intellectual property rights to this code, both source and binary and permission is given to anyon…

Elise Explains IPcast
Provides simple, expert guidance on trade marks, design registrations, copyright, brand strategy, and intellectual property law in Australia...
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/elise-explains-ipcast/

OBsmith: Testing JavaScript Obfuscator using LLM-powered sketching
Shan Jiang, Chenguang Zhu, Sarfraz Khurshid
https://arxiv.org/abs/2510.10066 https://arx…

OBsmith: Testing JavaScript Obfuscator using LLM-powered sketching
JavaScript obfuscators are widely deployed to protect intellectual property and resist reverse engineering, yet their correctness has been largely overlooked compared to performance and resilience. Existing evaluations typically measure resistance to deobfuscation, leaving the critical question of whether obfuscators preserve program semantics unanswered. Incorrect transformations can silently alter functionality, compromise reliability, and erode security-undermining the very purpose of obfusc…

AWM: Accurate Weight-Matrix Fingerprint for Large Language Models
Boyi Zeng, Lin Chen, Ziwei He, Xinbing Wang, Zhouhan Lin
https://arxiv.org/abs/2510.06738 https://

AWM: Accurate Weight-Matrix Fingerprint for Large Language Models
Protecting the intellectual property of large language models (LLMs) is crucial, given the substantial resources required for their training. Consequently, there is an urgent need for both model owners and third parties to determine whether a suspect LLM is trained from scratch or derived from an existing base model. However, the intensive post-training processes that models typically undergo-such as supervised fine-tuning, extensive continued pretraining, reinforcement learning, multi-modal ex…

Reading Between the Lines: Towards Reliable Black-box LLM Fingerprinting via Zeroth-order Gradient Estimation
Shuo Shao, Yiming Li, Hongwei Yao, Yifei Chen, Yuchen Yang, Zhan Qin
https://arxiv.org/abs/2510.06605

Reading Between the Lines: Towards Reliable Black-box LLM Fingerprinting via Zeroth-order Gradient Estimation
The substantial investment required to develop Large Language Models (LLMs) makes them valuable intellectual property, raising significant concerns about copyright protection. LLM fingerprinting has emerged as a key technique to address this, which aims to verify a model's origin by extracting an intrinsic, unique signature (a "fingerprint") and comparing it to that of a source model to identify illicit copies. However, existing black-box fingerprinting methods often fail to generate distinctiv…