Tootfinder

Toby Walsh on AI's vs intellectual property - #UNSW
https://www.youtube.com/watch?v=Puo6CmF5V58

27. 28.10.2025 #DCIFJahrestagung in #Darmstadt

Themen:
Data Sourcing & Quality – verlässliche Information in der Datenflut

Intellectual Property & Value Creation – Patentwissen strategisch nutzen
Future-Oriented CI – Foresight & KI für…

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…

EditMF: Drawing an Invisible Fingerprint for Your Large Language Models
Jiaxuan Wu, Yinghan Zhou, Wanli Peng, Yiming Xue, Juan Wen, Ping Zhong
https://arxiv.org/abs/2508.08836 h…

EditMF: Drawing an Invisible Fingerprint for Your Large Language Models
Training large language models (LLMs) is resource-intensive and expensive, making protecting intellectual property (IP) for LLMs crucial. Recently, embedding fingerprints into LLMs has emerged as a prevalent method for establishing model ownership. However, existing back-door-based methods suffer from limited stealth and efficiency. To simultaneously address these issues, we propose EditMF, a training-free fingerprinting paradigm that achieves highly imperceptible fingerprint embedding with min…

Dark prediction:
- Intellectual Property Class Action against AI companies gains traction
- AI companies use this as a plausible excuse to bail on AGI tech (that doesn’t exist)
- We see a pop like we saw “shipping heavy items by mail isn’t sustainable?!” pop in 2001
- AI companies fail, overwhelmingly, blaming only the IP Class Action
- The Money switches to a new/old grift; maybe back to crypto… lots of GPUs available
- In 5 years, everyone forgets and we have…

Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering
Junling Fan, David Koblah, Domenic Forte
https://arxiv.org/abs/2508.08462 ht…

Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering
Integrated circuits (ICs) are essential to modern electronic systems, yet they face significant risks from physical reverse engineering (RE) attacks that compromise intellectual property (IP) and overall system security. While IC camouflage techniques have emerged to mitigate these risks, existing approaches largely focus on localized gate modifications, neglecting comprehensive deception strategies. To address this gap, we present a machine learning (ML)-driven methodology that integrates cryp…

Matrix-Driven Instant Review: Confident Detection and Reconstruction of LLM Plagiarism on PC
Ruichong Zhang
https://arxiv.org/abs/2508.06309 https://arxiv.…

Matrix-Driven Instant Review: Confident Detection and Reconstruction of LLM Plagiarism on PC
In recent years, concerns about intellectual property (IP) in large language models (LLMs) have grown significantly. Plagiarizing other LLMs (through direct weight copying, upcycling, pruning, or continual pretraining) and claiming authorship without properly attributing to the original license, is a serious misconduct that can lead to significant financial and reputational harm to the original developers. However, existing methods for detecting LLM plagiarism fall short in key areas. They fail…

Real-world Music Plagiarism Detection With Music Segment Transcription System
Seonghyeon Go
https://arxiv.org/abs/2509.08282 https://arxiv.org/pdf/2509.082…

Real-world Music Plagiarism Detection With Music Segment Transcription System
As a result of continuous advances in Music Information Retrieval (MIR) technology, generating and distributing music has become more diverse and accessible. In this context, interest in music intellectual property protection is increasing to safeguard individual music copyrights. In this work, we propose a system for detecting music plagiarism by combining various MIR technologies. We developed a music segment transcription system that extracts musically meaningful segments from audio recordin…

A look at the impact of AI voice cloning on India's dubbing and voiceover sector, as artists demand consent, credit, and fair pay amid a lack of laws for AI use (Zico Ghosh/The Hollywood Reporter)
https://www.hollywoodreporter.com/movies/m

AI Is Replacing Voice Artists in India — and There Are No Local Laws to Stop It
As AI voice cloning in films becomes a reality, India’s dubbing artists are demanding consent, credit and fair pay: "You must understand that your voice is your intellectual property."

A lot of the social fallout and collateral damage caused by #AI is actually rooted in unregulated platform capitalism and neoliberal ad-consumer-waste-cycles: social media as mass manipulation, intellectual property laws that haven't been updated to the internet at all, gig economy exploitation... but most importantly:
Tech monopolies that are allowed to program dark pattern mechanisms int…

A look at the impact of AI voice cloning on India's dubbing and voiceover sector, as artists demand consent, credit, and fair pay amid a lack of laws for AI use (Zico Ghosh/The Hollywood Reporter)
https://www.hollywoodreporter.com/movies/m

AI Is Replacing Voice Artists in India — and There Are No Local Laws to Stop It
As AI voice cloning in films becomes a reality, India’s dubbing artists are demanding consent, credit and fair pay: "You must understand that your voice is your intellectual property."

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Guanzhi Deng, Yi Xie, Yu-Keung Ng, Mingyang Liu, Peijun Zheng, Jie Liu, Dapeng Wu, Yinqiao Li, Linqi Song
https://arxiv.org/abs/2509.07860

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Effectively managing intellectual property is a significant challenge. Traditional methods for patent analysis depend on labor-intensive manual searches and rigid keyword matching. These approaches are often inefficient and struggle to reveal the complex relationships hidden within large patent datasets, hindering strategic decision-making. To overcome these limitations, we introduce KLIPA, a novel framework that leverages a knowledge graph and a large language model (LLM) to significantly adva…

Judging Data: Critical Discourse and the Rise of Data Intellectual Property Rights in Chinese Courts
Chanhou Lou
https://arxiv.org/abs/2509.18605 https://a…

Towards Effective Prompt Stealing Attack against Text-to-Image Diffusion Models
Shiqian Zhao, Chong Wang, Yiming Li, Yihao Huang, Wenjie Qu, Siew-Kei Lam, Yi Xie, Kangjie Chen, Jie Zhang, Tianwei Zhang
https://arxiv.org/abs/2508.06837

Towards Effective Prompt Stealing Attack against Text-to-Image Diffusion Models
Text-to-Image (T2I) models, represented by DALL$\cdot$E and Midjourney, have gained huge popularity for creating realistic images. The quality of these images relies on the carefully engineered prompts, which have become valuable intellectual property. While skilled prompters showcase their AI-generated art on markets to attract buyers, this business incidentally exposes them to \textit{prompt stealing attacks}. Existing state-of-the-art attack techniques reconstruct the prompts from a fixed se…

ActiveMark: on watermarking of visual foundation models via massive activations
Anna Chistyakova, Mikhail Pautov
https://arxiv.org/abs/2510.04966 https://a…

ActiveMark: on watermarking of visual foundation models via massive activations
Being trained on large and vast datasets, visual foundation models (VFMs) can be fine-tuned for diverse downstream tasks, achieving remarkable performance and efficiency in various computer vision applications. The high computation cost of data collection and training motivates the owners of some VFMs to distribute them alongside the license to protect their intellectual property rights. However, a dishonest user of the protected model's copy may illegally redistribute it, for example, to make …

DeepV: A Model-Agnostic Retrieval-Augmented Framework for Verilog Code Generation with a High-Quality Knowledge Base
Zahin Ibnat, Paul E. Calzada, Rasin Mohammed Ihtemam, Sujan Kumar Saha, Jingbo Zhou, Farimah Farahmandi, Mark Tehranipoor
https://arxiv.org/abs/2510.05327

DeepV: A Model-Agnostic Retrieval-Augmented Framework for Verilog Code Generation with a High-Quality Knowledge Base
As large language models (LLMs) continue to be integrated into modern technology, there has been an increased push towards code generation applications, which also naturally extends to hardware design automation. LLM-based solutions for register transfer level (RTL) code generation for intellectual property (IP) designs have grown, especially with fine-tuned LLMs, prompt engineering, and agentic approaches becoming popular in literature. However, a gap has been exposed in these techniques, as t…

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…

Principle-Guided Verilog Optimization: IP-Safe Knowledge Transfer via Local-Cloud Collaboration
Jing Wang, Zheng Li, Lei Li, Fan He, Liyu Lin, Yao Lai, Yan Li, Xiaoyang Zeng, Yufeng Guo
https://arxiv.org/abs/2508.05675

Principle-Guided Verilog Optimization: IP-Safe Knowledge Transfer via Local-Cloud Collaboration
Recent years have witnessed growing interest in adopting large language models (LLMs) for Register Transfer Level (RTL) code optimization. While powerful cloud-based LLMs offer superior optimization capabilities, they pose unacceptable intellectual property (IP) leakage risks when processing proprietary hardware designs. In this paper, we propose a new scenario where Verilog code must be optimized for specific attributes without leaking sensitive IP information. We introduce the first IP-preser…

Towards Human-Centered RegTech: Unpacking Professionals' Strategies and Needs for Using LLMs Safely
Siying Hu, Yaxing Yao, Zhicong Lu
https://arxiv.org/abs/2510.01638 https:…

Towards Human-Centered RegTech: Unpacking Professionals' Strategies and Needs for Using LLMs Safely
Large Language Models are profoundly changing work patterns in high-risk professional domains, yet their application also introduces severe and underexplored compliance risks. To investigate this issue, we conducted semi-structured interviews with 24 highly-skilled knowledge workers from industries such as law, healthcare, and finance. The study found that these experts are commonly concerned about sensitive information leakage, intellectual property infringement, and uncertainty regarding the …

Dont use We Transfer

"Creative Commons is a license that lets the creators of intellectual property define how that property may be used by others. Partially this license is a reaction to the poor way #copyright law is being viewed today...
...gives creators a way to provide for more interaction with their ideas. And this interaction is a great way to market, in the right circumstances. More managers shou…

The NFL strikes a licensing deal with Front Office Sports, the first time the NFL has partnered with a sports business-focused news outlet to share content (Sara Fischer/Axios)
https://www.axios.com/2025/09/02/nfl-front-office-sports-content-deal

Exclusive: NFL strikes content deal with Front Office Sports
The yearlong deal will give FOS access to NFL intellectual property.

Patents as Knowledge Artifacts: An Information Science Perspective on Global Innovation
M. S. Rajeevan, B. Mini Devi
https://arxiv.org/abs/2508.00871 https://

Patents as Knowledge Artifacts: An Information Science Perspective on Global Innovation
In an age of fast-paced technological change, patents have evolved into not only legal mechanisms of intellectual property, but also structured storage containers of knowledge full of metadata, categories, and formal innovation. This chapter proposes to reframe patents in the context of information science, by focusing on patents as knowledge artifacts, and by seeing patents as fundamentally tied to the global movement of scientific and technological knowledge. With a focus on three areas, the …

Agency, Affordances, and Enculturation of Augmentation Technologies
Ann Hill Duin, Isabel Pedersen
https://arxiv.org/abs/2508.04725 https://arxiv.org/pdf/2…

Agency, Affordances, and Enculturation of Augmentation Technologies
Augmentation technologies are undergoing a process of enculturation due to many factors, one being the rise of artificial intelligence (AI), or what the World Intellectual Property Organization (WIPO) terms the AI wave or AI boom. Chapter 3 focuses critical attention on the hyped assumption that sophisticated, emergent, and embodied augmentation technologies will improve lives, literacy, cultures, arts, economies, and social contexts. The chapter begins by discussing the problem of ambiguity wi…

Latent Diffusion Unlearning: Protecting Against Unauthorized Personalization Through Trajectory Shifted Perturbations
Naresh Kumar Devulapally, Shruti Agarwal, Tejas Gokhale, Vishnu Suresh Lokhande
https://arxiv.org/abs/2510.03089

Latent Diffusion Unlearning: Protecting Against Unauthorized Personalization Through Trajectory Shifted Perturbations
Text-to-image diffusion models have demonstrated remarkable effectiveness in rapid and high-fidelity personalization, even when provided with only a few user images. However, the effectiveness of personalization techniques has lead to concerns regarding data privacy, intellectual property protection, and unauthorized usage. To mitigate such unauthorized usage and model replication, the idea of generating ``unlearnable'' training samples utilizing image poisoning techniques has emerged. Existing…

Now, even if you do want next token prediction:
Was it really worth the billions of dollars, the pollution, the pure disdain for the intellectual property of authors, to get here?
*I* don't think so. Predicting text is cute, it's fun, it's a party trick I would value at ~$100. I wouldn't make it my fucking life's work, but I guess we all have different value frameworks.

ein noch relativ frischer Aufsatz zum italienischen Sonderweg in Sachen freier Zugang zu gemeinfreiem Kulturgut: Sappa, C., Bossi, L. Postcards from Italy – The Art of Controlling Images of Cultural Goods Better Than Copyright Could. IIC 56, 320–368 (2025). https://doi.org/10.1007/s40319-025-01565-w

Have you read the new Wetransfer's terms of service?
"In order to allow us to operate, provide you with, and improve the Service and our technologies, we must obtain from you certain rights related to Content that is covered by intellectual property rights. You hereby grant us royalty a-free license to use your Content for the purposes of operating, developing, and improving the Service, all in accordance with our Privacy & Cookie Policy. (...) You hereby grant other users…

CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing
Boyu Zhang, Ping He, Tianyu Du, Xuhong Zhang, Lei Yun, Kingsum Chow, Jianwei Yin
https://arxiv.org/abs/2509.13982

CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing
With the widespread adoption of open-source code language models (code LMs), intellectual property (IP) protection has become an increasingly critical concern. While current watermarking techniques have the potential to identify the code LM to protect its IP, they have limitations when facing the more practical and complex demand, i.e., offering the individual user-level tracing in the black-box setting. This work presents CLMTracing, a black-box code LM watermarking framework employing the rul…

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…

Disney, Universal, and WBD sue China's MiniMax, alleging that its image- and video-generating service Hailuo AI was built from stolen intellectual property (Reuters)
https://www.reuters.com/legal/litigation/d

JSProtect: A Scalable Obfuscation Framework for Mini-Games in WeChat
Zhihao Li, Chaozheng Wang, Zongjie Li, Xinyong Peng, Zelin Su, Qun Xia, Haochuan Lu, Ting Xiong, Man Ho Lam, Shuzheng Gao, Yuchong Xie, Cuiyun Gao, Shuai Wang, Yuetang Deng, Huafeng Ma
https://arxiv.org/abs/2509.24498

JSProtect: A Scalable Obfuscation Framework for Mini-Games in WeChat
The WeChat mini-game ecosystem faces rampant intellectual property theft to other platforms via secondary development, yet existing JavaScript obfuscation tools are ill-equipped for large-scale applications, suffering from prohibitive processing times, severe runtime performance degradation, and unsustainable code size inflation. This paper introduces JSProtect, a high-throughput parallelized obfuscation framework designed to overcome these fundamental limitations. At the core of our framework …

Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses
Lincan Li, Bolin Shen, Chenxi Zhao, Yuxiang Sun, Kaixiang Zhao, Shirui Pan, Yushun Dong
https://arxiv.org/abs/2508.19641

Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses
Graph-structured data, which captures non-Euclidean relationships and interactions between entities, is growing in scale and complexity. As a result, training state-of-the-art graph machine learning (GML) models have become increasingly resource-intensive, turning these models and data into invaluable Intellectual Property (IP). To address the resource-intensive nature of model training, graph-based Machine-Learning-as-a-Service (GMLaaS) has emerged as an efficient solution by leveraging third-…

Staining and locking computer vision models without retraining
Oliver J. Sutton, Qinghua Zhou, George Leete, Alexander N. Gorban, Ivan Y. Tyukin
https://arxiv.org/abs/2507.22000

Staining and locking computer vision models without retraining
We introduce new methods of staining and locking computer vision models, to protect their owners' intellectual property. Staining, also known as watermarking, embeds secret behaviour into a model which can later be used to identify it, while locking aims to make a model unusable unless a secret trigger is inserted into input images. Unlike existing methods, our algorithms can be used to stain and lock pre-trained models without requiring fine-tuning or retraining, and come with provable, comput…

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design
Dong Ben, Hui Feng, Qian Wang
https://arxiv.org/abs/2507.16226

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design
Large Language Models (LLMs) are increasingly used in circuit design tasks and have typically undergone multiple rounds of training. Both the trained models and their associated training data are considered confidential intellectual property (IP) and must be protected from exposure. Confidential Computing offers a promising solution to protect data and models through Trusted Execution Environments (TEEs). However, existing TEE implementations are not designed to support the resource-intensive n…

Watermarking Large Language Model-based Time Series Forecasting
Wei Yuan, Chaoqun Yang, Yu Xing, Tong Chen, Nguyen Quoc Viet Hung, Hongzhi Yin
https://arxiv.org/abs/2507.20762 h…

Watermarking Large Language Model-based Time Series Forecasting
Large Language Model-based Time Series Forecasting (LLMTS) has shown remarkable promise in handling complex and diverse temporal data, representing a significant step toward foundation models for time series analysis. However, this emerging paradigm introduces two critical challenges. First, the substantial commercial potential and resource-intensive development raise urgent concerns about intellectual property (IP) protection. Second, their powerful time series forecasting capabilities may be …

Legal Matters in Research Software: A Few Things Worth Discussing
Giuditta Parolini
https://arxiv.org/abs/2509.24646 https://arxiv.org/pdf/2509.24646

Legal Matters in Research Software: A Few Things Worth Discussing
The paper discusses legal aspects relevant to the development of research software and practical approaches taken by research software engineers to deal with them. Intellectual Property Rights on software are considered alongside licensing choices made by the research community. The discussion addresses the ambiguities in the identification of the copyright holder of research software, the uncertainty surrounding liability, and remarks the varying level of support on legal matters provided by r…

Disney, Universal, and WBD sue China's MiniMax, alleging that its image- and video-generating service Hailuo AI was built from stolen intellectual property (Reuters)
https://www.reuters.com/legal/litigation/d

Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
Ines Akaichi, Giorgos Flouris, Irini Fundulaki, Sabrina Kirrane
https://arxiv.org/abs/2510.04652

Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
In the digital age, data frequently crosses organizational and jurisdictional boundaries, making effective governance essential. Usage control policies have emerged as a key paradigm for regulating data usage, safeguarding privacy, protecting intellectual property, and ensuring compliance with regulations. A central mechanism for usage control is the handling of obligations, which arise as a side effect of using and sharing data. Effective monitoring of obligations requires capturing usage trac…

DNA-DetectLLM: Unveiling AI-Generated Text via a DNA-Inspired Mutation-Repair Paradigm
Xiaowei Zhu, Yubing Ren, Fang Fang, Qingfeng Tan, Shi Wang, Yanan Cao
https://arxiv.org/abs/2509.15550

DNA-DetectLLM: Unveiling AI-Generated Text via a DNA-Inspired Mutation-Repair Paradigm
The rapid advancement of large language models (LLMs) has blurred the line between AI-generated and human-written text. This progress brings societal risks such as misinformation, authorship ambiguity, and intellectual property concerns, highlighting the urgent need for reliable AI-generated text detection methods. However, recent advances in generative language modeling have resulted in significant overlap between the feature distributions of human-written and AI-generated text, blurring class…

Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey
Jie Cao, Qi Li, Zelin Zhang, Jianbing Ni
https://arxiv.org/abs/2510.02384 https://

Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey
The rapid advancement of generative artificial intelligence (Gen-AI) has facilitated the effortless creation of high-quality images, while simultaneously raising critical concerns regarding intellectual property protection, authenticity, and accountability. Watermarking has emerged as a promising solution to these challenges by distinguishing AI-generated images from natural content, ensuring provenance, and fostering trustworthy digital ecosystems. This paper presents a comprehensive survey of…

Investigating Training Data Detection in AI Coders
Tianlin Li, Yunxiang Wei, Zhiming Li, Aishan Liu, Qing Guo, Xianglong Liu, Dongning Sun, Yang Liu
https://arxiv.org/abs/2507.17389

Investigating Training Data Detection in AI Coders
Recent advances in code large language models (CodeLLMs) have made them indispensable tools in modern software engineering. However, these models occasionally produce outputs that contain proprietary or sensitive code snippets, raising concerns about potential non-compliant use of training data, and posing risks to privacy and intellectual property. To ensure responsible and compliant deployment of CodeLLMs, training data detection (TDD) has become a critical task. While recent TDD methods have…

VLA-Mark: A cross modal watermark for large vision-language alignment model
Shuliang Liu, Qi Zheng, Jesse Jiaxi Xu, Yibo Yan, He Geng, Aiwei Liu, Peijie Jiang, Jia Liu, Yik-Cheung Tam, Xuming Hu
https://arxiv.org/abs/2507.14067

VLA-Mark: A cross modal watermark for large vision-language alignment model
Vision-language models demand watermarking solutions that protect intellectual property without compromising multimodal coherence. Existing text watermarking methods disrupt visual-textual alignment through biased token selection and static strategies, leaving semantic-critical concepts vulnerable. We propose VLA-Mark, a vision-aligned framework that embeds detectable watermarks while preserving semantic fidelity through cross-modal coordination. Our approach integrates multiscale visual-textua…

An automatic patent literature retrieval system based on LLM-RAG
Yao Ding, Yuqing Wu, Ziyang Ding
https://arxiv.org/abs/2508.14064 https://arxiv.org/pdf/25…

An automatic patent literature retrieval system based on LLM-RAG
With the acceleration of technological innovation efficient retrieval and classification of patent literature have become essential for intellectual property management and enterprise RD Traditional keyword and rulebased retrieval methods often fail to address complex query intents or capture semantic associations across technical domains resulting in incomplete and lowrelevance results This study presents an automated patent retrieval framework integrating Large Language Models LLMs with Retri…

PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement
Xubin Yue, Zhenhua Xu, Wenpeng Xing, Jiahui Yu, Mohan Li, Meng Han
https://arxiv.org/abs/2509.00918

PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement
Addressing the intellectual property protection challenges in commercial deployment of large language models (LLMs), existing black-box fingerprinting techniques face dual challenges from incremental fine-tuning erasure and feature-space defense due to their reliance on overfitting high-perplexity trigger patterns. Recent work has revealed that model editing in the fingerprinting domain offers distinct advantages, including significantly lower false positive rates, enhanced harmlessness, and su…

Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models
Zhenhua Xu, Zhaokun Yan, Binhan Xu, Xin Tong, Haitao Xu, Yourong Chen, Meng Han
https://arxiv.org/abs/2509.00820

Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models
With the rapid advancement of large language models (LLMs), safeguarding intellectual property (IP) has become increasingly critical. To address the challenges of high costs and potential contamination in fingerprint integration, we propose LoRA-FP, a lightweight, plug-and-play framework that embeds backdoor fingerprints into LoRA adapters through constrained fine-tuning. This design enables seamless fingerprint transplantation via parameter fusion, eliminating the need for full-parameter updat…

Stealing AI Model Weights Through Covert Communication Channels
Valentin Barbaza, Alan Rodrigo Diaz-Rizo, Hassan Aboushady, Spyridon Raptis, Haralampos-G. Stratigopoulos
https://arxiv.org/abs/2510.00151

Stealing AI Model Weights Through Covert Communication Channels
AI models are often regarded as valuable intellectual property due to the high cost of their development, the competitive advantage they provide, and the proprietary techniques involved in their creation. As a result, AI model stealing attacks pose a serious concern for AI model providers. In this work, we present a novel attack targeting wireless devices equipped with AI hardware accelerators. The attack unfolds in two phases. In the first phase, the victim's device is compromised with a hardw…

ZK-WAGON: Imperceptible Watermark for Image Generation Models using ZK-SNARKs
Aadarsh Anantha Ramakrishnan, Shubham Agarwal, Selvanayagam S, Kunwar Singh
https://arxiv.org/abs/2510.01967

ZK-WAGON: Imperceptible Watermark for Image Generation Models using ZK-SNARKs
As image generation models grow increasingly powerful and accessible, concerns around authenticity, ownership, and misuse of synthetic media have become critical. The ability to generate lifelike images indistinguishable from real ones introduces risks such as misinformation, deepfakes, and intellectual property violations. Traditional watermarking methods either degrade image quality, are easily removed, or require access to confidential model internals - making them unsuitable for secure and …

Integrated Security Mechanisms for Weight Protection in Memristive Crossbar Arrays
Muhammad Faheemur Rahman, Wayne Burleson
https://arxiv.org/abs/2510.01350 https://

Integrated Security Mechanisms for Weight Protection in Memristive Crossbar Arrays
Memristive crossbar arrays enable in-memory computing by performing parallel analog computations directly within memory, making them well-suited for machine learning, neural networks, and neuromorphic systems. However, despite their advantages, non-volatile memristors are vulnerable to security threats (such as adversarial extraction of stored weights when the hardware is compromised. Protecting these weights is essential since they represent valuable intellectual property resulting from length…

Logic Solver Guided Directed Fuzzing for Hardware Designs
Raghul Saravanan, Sai Manoj P D
https://arxiv.org/abs/2509.26509 https://arxiv.org/pdf/2509.26509…

Logic Solver Guided Directed Fuzzing for Hardware Designs
The ever-increasing complexity of design specifications for processors and intellectual property (IP) presents a formidable challenge for early bug detection in the modern IC design cycle. The recent advancements in hardware fuzzing have proven effective in detecting bugs in RTL designs of cutting-edge processors. The modern IC design flow involves incremental updates and modifications to the hardware designs necessitating rigorous verification and extending the overall verification period. To …

I Stolenly Swear That I Am Up to (No) Good: Design and Evaluation of Model Stealing Attacks
Daryna Oliynyk, Rudolf Mayer, Kathrin Grosse, Andreas Rauber
https://arxiv.org/abs/2508.21654

I Stolenly Swear That I Am Up to (No) Good: Design and Evaluation of Model Stealing Attacks
Model stealing attacks endanger the confidentiality of machine learning models offered as a service. Although these models are kept secret, a malicious party can query a model to label data samples and train their own substitute model, violating intellectual property. While novel attacks in the field are continually being published, their design and evaluations are not standardised, making it challenging to compare prior works and assess progress in the field. This paper is the first to address…

Hot-Swap MarkBoard: An Efficient Black-box Watermarking Approach for Large-scale Model Distribution
Zhicheng Zhang, Peizhuo Lv, Mengke Wan, Jiang Fang, Diandian Guo, Yezeng Chen, Yinlong Liu, Wei Ma, Jiyan Sun, Liru Geng
https://arxiv.org/abs/2507.20650

Hot-Swap MarkBoard: An Efficient Black-box Watermarking Approach for Large-scale Model Distribution
Recently, Deep Learning (DL) models have been increasingly deployed on end-user devices as On-Device AI, offering improved efficiency and privacy. However, this deployment trend poses more serious Intellectual Property (IP) risks, as models are distributed on numerous local devices, making them vulnerable to theft and redistribution. Most existing ownership protection solutions (e.g., backdoor-based watermarking) are designed for cloud-based AI-as-a-Service (AIaaS) and are not directly applicab…

SoK: Large Language Model Copyright Auditing via Fingerprinting
Shuo Shao, Yiming Li, Yu He, Hongwei Yao, Wenyuan Yang, Dacheng Tao, Zhan Qin
https://arxiv.org/abs/2508.19843 ht…

SoK: Large Language Model Copyright Auditing via Fingerprinting
The broad capabilities and substantial resources required to train Large Language Models (LLMs) make them valuable intellectual property, yet they remain vulnerable to copyright infringement, such as unauthorized use and model theft. LLM fingerprinting, a non-intrusive technique that extracts and compares the distinctive features from LLMs to identify infringements, offers a promising solution to copyright auditing. However, its reliability remains uncertain due to the prevalence of diverse mod…

NWaaS: Nonintrusive Watermarking as a Service for X-to-Image DNN
Haonan An, Guang Hua, Yu Guo, Hangcheng Cao, Susanto Rahardja, Yuguang Fang
https://arxiv.org/abs/2507.18036 htt…

NWaaS: Nonintrusive Watermarking as a Service for X-to-Image DNN
The intellectual property of deep neural network (DNN) models can be protected with DNN watermarking, which embeds copyright watermarks into model parameters (white-box), model behavior (black-box), or model outputs (box-free), and the watermarks can be subsequently extracted to verify model ownership or detect model theft. Despite recent advances, these existing methods are inherently intrusive, as they either modify the model parameters or alter the structure. This natural intrusiveness raise…

Removing Box-Free Watermarks for Image-to-Image Models via Query-Based Reverse Engineering
Haonan An, Guang Hua, Hangcheng Cao, Zhengru Fang, Guowen Xu, Susanto Rahardja, Yuguang Fang
https://arxiv.org/abs/2507.18034

Removing Box-Free Watermarks for Image-to-Image Models via Query-Based Reverse Engineering
The intellectual property of deep generative networks (GNets) can be protected using a cascaded hiding network (HNet) which embeds watermarks (or marks) into GNet outputs, known as box-free watermarking. Although both GNet and HNet are encapsulated in a black box (called operation network, or ONet), with only the generated and marked outputs from HNet being released to end users and deemed secure, in this paper, we reveal an overlooked vulnerability in such systems. Specifically, we show that t…

Train to Defend: First Defense Against Cryptanalytic Neural Network Parameter Extraction Attacks
Ashley Kurian, Aydin Aysu
https://arxiv.org/abs/2509.16546 https://

Train to Defend: First Defense Against Cryptanalytic Neural Network Parameter Extraction Attacks
Neural networks are valuable intellectual property due to the significant computational cost, expert labor, and proprietary data involved in their development. Consequently, protecting their parameters is critical not only for maintaining a competitive advantage but also for enhancing the model's security and privacy. Prior works have demonstrated the growing capability of cryptanalytic attacks to scale to deeper models. In this paper, we present the first defense mechanism against cryptanalyti…

Turning Hearsay into Discovery: Industrial 3D Printer Side Channel Information Translated to Stealing the Object Design
Aleksandr Dolgavin, Jacob Gatlin, Moti Yung, Mark Yampolskiy
https://arxiv.org/abs/2509.18366

Trustworthy and Confidential SBOM Exchange
Eman Abu Ishgair, Chinenye Okafor, Marcela S. Melara, Santiago Torres-Arias
https://arxiv.org/abs/2509.13217 https://

Trustworthy and Confidential SBOM Exchange
Software Bills of Materials (SBOMs) have become a regulatory requirement for improving software supply chain security and trust by means of transparency regarding components that make up software artifacts. However, enterprise and regulated software vendors commonly wish to restrict who can view confidential software metadata recorded in their SBOMs due to intellectual property or security vulnerability information. To address this tension between transparency and confidentiality, we propose Pe…