Parsa Hedayatnia, Tina Tavakkoli, Hadi Amini, Mohammad Allahbakhsh · 5 authors
Smart contracts concentrate high value assets and complex logic in small, immutable programs, where even minor bugs can cause major losses. Existing taxonomies and tools remain fragmented, organized around symptoms such as reentrancy rather than structural causes. This paper introduces an attack-centric, program-structure taxonomy that unifies Solidity vulnerabilities into eight root-cause families covering control flow, external calls, state integrity, arithmetic safety, environmental dependencies, access control, input validation, and cross-domain protocol assumptions. Each family is illustrated through concise Solidity examples, exploit mechanics, and mitigations, and linked to the detection signals observable by static, dynamic, and learning-based tools. We further cross-map legacy datasets (SmartBugs, SolidiFI) to this taxonomy to reveal label drift and coverage gaps. The taxonomy provides a consistent vocabulary and practical checklist that enable more interpretable detection, reproducible audits, and structured security education for both researchers and practitioners.
Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract authority. Through signature verification, smart contracts can ensure that signers possess the required permissions, thus enhancing security and scalability. However, lacking checks on signature usage conditions can lead to repeated verifications, increasing the risk of permission abuse and threatening contract assets. We define this issue as the Signature Replay Vulnerability (SRV). In this paper, we conducted the first empirical study to investigate the causes and characteristics of the SRVs. From 1,419 audit reports across 37 blockchain security companies, we identified 108 with detailed SRV descriptions and classified five types of SRVs. To detect these vulnerabilities automatically, we designed LASiR, which utilizes the general semantic understanding ability of Large Language Models (LLMs) to assist in the static taint analysis of the signature state and identify the signature reuse behavior. It also employs path reachability verification via symbolic execution to ensure effective and reliable detection. To evaluate the performance of LASiR, we conducted large-scale experiments on 15,383 contracts involving signature verification, selected from the initial dataset of 918,964 contracts across four blockchains: Ethereum, Binance Smart Chain, Polygon, and Arbitrum. The results indicate that SRVs are widespread, with affected contracts holding $4.76 million in active assets. Among these, 19.63% of contracts that use signatures on Ethereum contain SRVs. Furthermore, manual verification demonstrates that LASiR achieves an F1-score of 87.90% for detection. Ablation studies and comparative experiments reveal that the semantic information provided by LLMs aids static taint analysis, significantly enhancing LASiR's detection performance.
This research proposes a decentralized and cryptographically secure framework to address the most acute issues of privacy, data security, and protection in the ecosystem of medical insurance claim processing. The scope of this study focuses on enabling the management of insurance claims in a transparent, privacy-protecting manner while maintaining the efficiency and trust level needed by the patients, healthcare providers, and insurers. To accomplish this, the proposed system adds blockchain technology to provide an unchangeable, decentralized, and auditable claim transactions ledger which enhances overall claim-related processes and trust among all stakeholders. To protect critical patient information, the framework employs homomorphic encryption a modern form of cryptography to allow authorized insurance providers to perform necessary operations like claim adjudication and reimbursement on encrypted medical records without any decryption during the process. This method significantly reduces the third-party processing privacy risk because patient data can be kept secret even when third-party processing is done. In addition, smart contracts improve automation of the most important procedures in the claim processing pipeline, which decreases manual, operational, and susceptibility towards human blunders or deceitful acts. The integration of these two transformative technologiesblockchain and homomorphic encryption represents the core contribution of this work, enabling the coexistence of transparency and privacy which are usually viewed as competing objectives in traditional systems. As a result, these technologies are expected to foster the creation of a reliable, effective, and privacy safeguarding architecture that could transform the medical claim submission systems paradigm.
Software services are crucial for reliable communication and networking; therefore, Site Reliability Engineering (SRE) is important to ensure these systems stay reliable and perform well in cloud-native environments. SRE leverages tools like Prometheus and Grafana to monitor system metrics, defining critical Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for maintaining high service standards. However, a significant challenge arises as many developers often lack in-depth understanding of these tools and the intricacies involved in defining appropriate SLIs and SLOs. To bridge this gap, we propose a novel SRE platform, called SRE-Llama, enhanced by Generative-AI, Federated Learning, Blockchain, and Non-Fungible Tokens (NFTs). This platform aims to automate and simplify the process of monitoring, SLI/SLO generation, and alert management, offering ease in accessibility and efficy for developers. The system operates by capturing metrics from cloud-native services and storing them in a time-series database, like Prometheus and Mimir. Utilizing this stored data, our platform employs Federated Learning models to identify the most relevant and impactful SLI metrics for different services and SLOs, addressing concerns around data privacy. Subsequently, fine-tuned Meta's Llama-3 LLM is adopted to intelligently generate SLIs, SLOs, error budgets, and associated alerting mechanisms based on these identified SLI metrics. A unique aspect of our platform is the encoding of generated SLIs and SLOs as NFT objects, which are then stored on a Blockchain. This feature provides immutable record-keeping and facilitates easy verification and auditing of the SRE metrics and objectives. The automation of the proposed platform is governed by the blockchain smart contracts. The proposed SRE-Llama platform prototype has been implemented with a use case featuring a customized Open5GS 5G Core.
Existing retrieval-augmented generation (RAG) systems typically use a centralized architecture, causing a high cost of data collection, integration, and management, as well as privacy concerns. There is a great need for a decentralized RAG system that enables foundation models to utilize information directly from data owners who maintain full control over their sources. However, decentralization brings a challenge: the numerous independent data sources vary significantly in reliability, which can diminish retrieval accuracy and response quality. To address this, our decentralized RAG system has a novel reliability scoring mechanism that dynamically evaluates each source based on the quality of responses it contributes to generate and prioritizes high-quality sources during retrieval. To ensure transparency and trust, the scoring process is securely managed through blockchain-based smart contracts, creating verifiable and tamper-proof reliability records without relying on a central authority. We evaluate our decentralized system with two Llama models (3B and 8B) in two simulated environments where six data sources have different levels of reliability. Our system achieves a +10.7\% performance improvement over its centralized counterpart in the real world-like unreliable data environments. Notably, it approaches the upper-bound performance of centralized systems under ideally reliable data environments. The decentralized infrastructure enables secure and trustworthy scoring management, achieving approximately 56\% marginal cost savings through batched update operations. Our code and system are open-sourced at github.com/yining610/Reliable-dRAG.
This paper introduces a structured approach to improving decision making in Decentralized Autonomous Organizations (DAO) through the integration of the Question-Option-Criteria (QOC) model and AI agents. We outline a stepwise governance framework that evolves from human led evaluations to fully autonomous, AI-driven processes. By decomposing decisions into weighted, criterion based evaluations, the QOC model enhances transparency, fairness, and explainability in DAO voting. We demonstrate how large language models (LLMs) and stakeholder aligned AI agents can support or automate evaluations, while statistical safeguards help detect manipulation. The proposed framework lays the foundation for scalable and trustworthy governance in the Web3 ecosystem.
Felipe Castaño, Constantinos Patsakis, Francesco Zola, Fran Casino
LockBit has evolved from an obscure Ransomware-as-a-Service newcomer in 2019 to the most prolific ransomware franchise of 2024. Leveraging a recently leaked MySQL dump of the gang's management panel, this study offers an end-to-end reconstruction of LockBit's technical, behavioral, and financial apparatus. We recall the family's version timeline and map its tactics, techniques, and procedures to MITRE ATT&CK, highlighting the incremental hardening that distinguishes LockBit 3.0 from its predecessors. We then analyze 51 negotiation chat logs using natural-language embeddings and clustering to infer a canonical interaction playbook, revealing recurrent rhetorical stages that underpin the double-extortion strategy. Finally, we trace 19 Bitcoin addresses related to ransom payment chains, revealing two distinct patterns based on different laundering phases. In both cases, a small portion of the ransom is immediately split into long-lived addresses (presumably retained by the group as profit and to finance further operations) while the remainder is ultimately aggregated into two high-volume addresses before likely being sent to the affiliate. These two collector addresses appear to belong to distinct exchanges, each processing over 200k BTC. The combined evidence portrays LockBit as a tightly integrated criminal service whose resilience rests on rapid code iteration, script-driven social engineering, and industrial-scale cash-out pipelines.
Introducing Reliablocks, an on-chain reliability index for non-finalized blocks in Optimistic Rollups. This was built during the EigenLayer Infinite Hackathon at the Infinite Hacker House at DevCon 2024. As part of this research, we delivered a working Layer AVS WASMI component, a working Eigen Layer AVS component, EigenLayer Solidity smart contracts that work with the AVS component, a UI dashboard illustrating the reliability score and a derived interest rate for further utilization.
Artificial intelligence (AI) agents are increasingly capable of initiating financial transactions on behalf of users or other agents. This evolution introduces a fundamental challenge: verifying both the authenticity of an autonomous agent and the true intent behind its transactions in a decentralized, trustless environment. Traditional payment systems assume human authorization, but autonomous, agent-led payments remove that safeguard. This paper presents a blockchain-based framework that cryptographically authenticates and verifies the intent of every AI-initiated transaction. The proposed system leverages decentralized identity (DID) standards and verifiable credentials to establish agent identities, on-chain intent proofs to record user authorization, and zero-knowledge proofs (ZKPs) to preserve privacy while ensuring policy compliance. Additionally, secure execution environments (TEE-based attestations) guarantee the integrity of agent reasoning and execution. The hybrid on-chain/off-chain architecture provides an immutable audit trail linking user intent to payment outcome. Through qualitative analysis, the framework demonstrates strong resistance to impersonation, unauthorized transactions, and misalignment of intent. This work lays the foundation for secure, auditable, and intent-aware autonomous economic agents, enabling a future of verifiable trust and accountability in AI-driven financial ecosystems.
Azhar Hussain Mozumder, M. John Basha, Chayapathi A. R
With more and more existing networks being transformed to Software-Defined Networking (SDN), they need to be more secure and demand smarter ways of traffic control. This work, SmartSecChain-SDN, is a platform that combines machine learning based intrusion detection, blockchain-based storage of logs, and application-awareness-based priority in SDN networks. To detect network intrusions in a real-time, precision and low-false positives setup, the framework utilizes the application of advanced machine learning algorithms, namely Random Forest, XGBoost, CatBoost, and CNN-BiLSTM. SmartSecChain-SDN is based on the Hyperledger Fabric, which is a permissioned blockchain technology, to provide secure, scalable, and privacy-preserving storage and, thus, guarantee that the Intrusion Detection System (IDS) records cannot be altered and can be analyzed comprehensively. The system also has Quality of Service (QoS) rules and traffic shaping based on applications, which enables prioritization of critical services, such as VoIP, video conferencing, and business applications, as well as de-prioritization of non-essential traffic, such as downloads and updates. Mininet can simulate real-time SDN scenarios because it is used to prototype whole architectures. It is also compatible with controllers OpenDaylight and Ryu. It has tested the framework using the InSDN dataset and proved that it can identify different kinds of cyberattacks and handle bandwidth allocation efficiently under circumstances of resource constraints. SmartSecChain-SDN comprehensively addresses SDN system protection, securing and enhancing. The proposed study offers an innovative, extensible way to improve cybersecurity, regulatory compliance, and the administration of next-generation programmable networks.
Blockchain interoperability is increasingly recognized as the centerpiece for robust interactions among decentralized services. Blockchain ledgers are generally tamper-proof and thus enforce non-repudiation for transactions recorded within the same network. However, such a guarantee does not hold for cross blockchain transactions. When disruptions occur due to malicious activities or system failures within one blockchain network, foreign networks can take advantage by denying legitimate claims or mounting fraudulent liabilities against the defenseless network. In response, this paper introduces InterSnap, a novel blockchain snapshot archival methodology, for enabling auditability of cross blockchain transactions, enforcing non-repudiation. InterSnap introduces cross-chain transaction receipts that ensure their irrefutability. Snapshots of ledger data along with these receipts are utilized as non-repudiable proof of bilateral agreements among different networks. InterSnap enhances system resilience through a distributed snapshot generation process, need-based snapshot scheduling process, and archival storage and sharing via decentralized platforms. Through a prototype implementation based on Hyperledger Fabric, we conducted experiments using on-premise machines, AWS public cloud instances, as well as a private cloud infrastructure. We establish that InterSnap can recover from malicious attacks while preserving cross chain transaction receipts. Additionally, our proposed solution demonstrates adaptability to increasing loads while securely transferring snapshot archives with minimal overhead.
Giacomo Zonneveld, Giulia Rafaiani, Massimo Battaglioni, Marco Baldi
The use of blockchains for data certification and traceability is now well established in both the literature and practical applications. However, while blockchain-based certification of individual data is clear and straightforward, the use of blockchain to certify large amounts of data produced on a nearly continuous basis still poses some challenges. In such a case, in fact, it is first necessary to collect the data in an off-chain buffer, and then to organize it, e.g., via Merkle trees, in order to keep the size and quantity of certification data to be written to the blockchain small. In this paper, we consider a typical system for blockchain-based traceability of a production process, and propose and comparatively analyze some strategies for certifying the data of such a process on blockchain, while maintaining the possibility of verifying their certification in a decentralized way.
List decoding of codes can be seen as the generalization of unique decoding of codes while list decoding over finite fields has been extensively studied, extending these results to more general algebraic structures such as Galois rings remains an important challenge. Due to recent progress in zero knowledge systems, there is a growing demand to investigate the proximity gap of codes over Galois rings in Yizhou Yao(2025). The proximity gap is closely related to the decoding capability of codes. It was shown in Eli Ben-Sasson(2020) that the proximity gap for RS codes over finite field can be improved to $1-\sqrt{r}$ if one consider list decoding instead of unique decoding. However, we know very little about RS codes over Galois ring which might hinder the development of zero knowledge proof system for ring-based arithmetic circuit. In this work, we first extend the list decoding procedure of Guruswami and Sudan to Reed-Solomon codes over Galois rings, which shows that RS codes with rate $r$ can be list decoded up to radius $1-\sqrt{r}$. Then, we investigate the list decoding of folded Reed-Solomon codes over Galois rings. We show that the list decoding radius of folded Reed-Solomon codes can reach the Singlton bound as its counterpart over finite field. We also extend the deterministic pruning method of Vikrant Ashvinkumar(2026) to Galois rings, showing how to prune the affine free module obtained from the linear-algebraic decoder and recover the candidate codewords. Finally, we improve the list size of our folded Reed-Solomon code to $O(1/\varepsilon^2)$ by extending recent work in Shashank Srivastava(2025) to Galois Rings. By developing the recent work of Yeyuan Chen(2025), we show that folded Reed-Solomon codes over Galois rings satisfy the relaxed generalized Singleton bound in the average-radius sense with optimal list size $O(1/\varepsilon)$.
Barrett's algorithm is one of the most widely used methods for performing modular multiplication, a critical nonlinear operation in modern privacy computing techniques such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). Since modular multiplication dominates the processing time in these applications, computational complexity and memory limitations significantly impact performance. Computing-in-Memory (CiM) is a promising approach to tackle this problem. However, existing schemes currently suffer from two main problems: 1) Most works focus on low bit-width modular multiplication, which is inadequate for mainstream cryptographic algorithms such as elliptic curve cryptography (ECC) and the RSA algorithm, both of which require high bit-width operations; 2) Recent efforts targeting large number modular multiplication rely on inefficient in-memory logic operations, resulting in high scaling costs for larger bit-widths and increased latency. To address these issues, we propose LaMoS, an efficient SRAM-based CiM design for large-number modular multiplication, offering high scalability and area efficiency. First, we analyze the Barrett's modular multiplication method and map the workload onto SRAM CiM macros for high bit-width cases. Additionally, we develop an efficient CiM architecture and dataflow to optimize large-number modular multiplication. Finally, we refine the mapping scheme for better scalability in high bit-width scenarios using workload grouping. Experimental results show that LaMoS achieves a $7.02\times$ speedup and reduces high bit-width scaling costs compared to existing SRAM-based CiM designs.
The oracle problem refers to the inability of an agent to know if the information coming from an oracle is authentic and unbiased. In ancient times, philosophers and historians debated on how to evaluate, increase, and secure the reliability of oracle predictions, particularly those from Delphi, which pertained to matters of state. Today, we refer to data carriers for automatic machines as oracles, but establishing a secure channel between these oracles and the real world still represents a challenge. Despite numerous efforts, this problem remains mostly unsolved, and the recent advent of blockchain oracles has added a layer of complexity because of the decentralization of blockchains. This paper conceptually connects Delphic and modern blockchain oracles, developing a comparative framework. Leveraging blockchain oracle taxonomy, lexical analysis is also performed on 167 Delphic queries to shed light on the relationship between oracle answer quality and question type. The presented framework aims first at revealing commonalities between classical and computational oracles and then at enriching the oracle analysis within each field. This study contributes to the computer science literature by proposing strategies to improve the reliability of blockchain oracles based on insights from Delphi and to classical literature by introducing a framework that can also be applied to interpret and classify other ancient oracular mechanisms.
Artur Iasenovets, Fei Tang, Huihui Zhu, Ping Wang · 5 authors
Permissioned blockchains ensure integrity and auditability of shared data but expose query parameters to peers during read operations, creating privacy risks for organizations querying sensitive records. This paper proposes a Private Information Retrieval (PIR) mechanism to enable private reads from Hyperledger Fabric's world state, allowing endorsing peers to process encrypted queries without learning which record is accessed. We implement and benchmark a PIR-enabled chaincode that performs ciphertext-plaintext (ct-pt) homomorphic multiplication directly within evaluate transactions, preserving Fabric's endorsement and audit semantics. The prototype achieves an average end-to-end latency of 113 ms and a peer-side execution time below 42 ms, with approximately 2 MB of peer network traffic per private read in development mode--reducible by half under in-process deployment. Storage profiling across three channel configurations shows near-linear growth: block size increases from 77 kilobytes to 294 kilobytes and world-state from 112 kilobytes to 332 kilobytes as the ring dimension scales from 8,192 to 32,768 coefficients. Parameter analysis further indicates that ring size and record length jointly constrain packing capacity, supporting up to 512 records of 64 bytes each under the largest configuration. These results confirm the practicality of PIR-based private reads in Fabric for smaller, sensitive datasets and highlight future directions to optimize performance and scalability.
The potential of agricultural data (AgData) to drive efficiency and sustainability is stifled by the "AgData Paradox": a pervasive lack of trust and interoperability that locks data in silos, despite its recognized value. This paper introduces AgriTrust, a federated semantic governance framework designed to resolve this paradox. AgriTrust integrates a multi-stakeholder governance model, built on pillars of Data Sovereignty, Transparent Data Contracts, Equitable Value Sharing, and Regulatory Compliance, with a semantic digital layer. This layer is realized through the AgriTrust Core Ontology, a formal OWL ontology that provides a shared vocabulary for tokenization, traceability, and certification, enabling true semantic interoperability across independent platforms. A key innovation is a blockchain-agnostic, multi-provider architecture that prevents vendor lock-in. The framework's viability is demonstrated through case studies across three critical Brazilian supply chains: coffee (for EUDR compliance), soy (for mass balance), and beef (for animal tracking). The results show that AgriTrust successfully enables verifiable provenance, automates compliance, and creates new revenue streams for data producers, thereby transforming data sharing from a trust-based dilemma into a governed, automated operation. This work provides a foundational blueprint for a more transparent, efficient, and equitable agricultural data economy.
Vivi Andersson, Sofia Bobadilla, Harald Hobbelhagen, Martin Monperrus
Smart contracts operate in a highly adversarial environment, where vulnerabilities can lead to substantial financial losses. Thus, smart contracts are subject to security audits. In auditing, proof-of-concept (PoC) exploits play a critical role by demonstrating to the stakeholders that the reported vulnerabilities are genuine, reproducible, and actionable. However, manually creating PoCs is time-consuming, error-prone, and often constrained by tight audit schedules. We introduce PoCo, an agentic framework that automatically generates executable PoC exploits from natural-language vulnerability descriptions written by auditors. PoCo autonomously generates PoC exploits in an agentic manner by interacting with a set of code-execution tools in a Reason–Act–Observe loop. It produces fully executable exploits compatible with the Foundry testing framework, ready for integration into audit reports and other security tools. We evaluate PoCo on a dataset of 23 real-world vulnerability reports. PoCo consistently outperforms the Zero-shot and Workflow baselines, generating well-formed and logically correct PoCs. Our results demonstrate that agentic frameworks can significantly reduce the effort required for high-quality PoCs in smart contract audits. Our contribution provides actionable knowledge for the smart contract security community.
M.Z. Haider, M.U. Ghouri, Tayyaba Noreen, M. Salman
Blockchain systems face persistent challenges of scalability, latency, and energy inefficiency. Existing consensus protocols such as Proof-of-Work (PoW) and Proof-of-Stake (PoS) either consume excessive resources or risk centralization. This paper proposes \textit{Proof-of-Spiking-Neurons (PoSN)}, a neuromorphic consensus protocol inspired by spiking neural networks. PoSN encodes transactions as spike trains, elects leaders through competitive firing dynamics, and finalizes blocks via neural synchronization, enabling parallel and event-driven consensus with minimal energy overhead. A hybrid system architecture is implemented on neuromorphic platforms, supported by simulation frameworks such as Nengo and PyNN. Experimental results show significant gains in energy efficiency, throughput, and convergence compared to PoB and PoR. PoSN establishes a foundation for sustainable, adaptive blockchains suitable for IoT, edge, and large-scale distributed systems.
Liang, Hanzhong, Yue Duan, Xing Su, Xiao Li · 8 authors
As the Web3 ecosystem evolves toward a multi-chain architecture, cross-chain bridges have become critical infrastructure for enabling interoperability between diverse blockchain networks. However, while connecting isolated blockchains, the lack of cross-chain transaction pairing records introduces significant challenges for security analysis like cross-chain fund tracing, advanced vulnerability detection, and transaction graph-based analysis. To address this gap, we introduce ConneX, an automated and general-purpose system designed to accurately identify corresponding transaction pairs across both ends of cross-chain bridges. Our system leverages Large Language Models (LLMs) to efficiently prune the semantic search space by identifying semantically plausible key information candidates within complex transaction records. Further, it deploys a novel examiner module that refines these candidates by validating them against transaction values, effectively addressing semantic ambiguities and identifying the correct semantics. Extensive evaluations on a dataset of about 500,000 transactions from five major bridge platforms demonstrate that ConneX achieves an average F1 score of 0.9746, surpassing baselines by at least 20.05\%, with good efficiency that reduces the semantic search space by several orders of magnitude (1e10 to less than 100). Moreover, its successful application in tracing illicit funds (including a cross-chain transfer worth $1 million) in real-world hacking incidents underscores its practical utility for enhancing cross-chain security and transparency.
The growth in IoT devices means an ongoing risk of data vulnerability. The transition from centralized ecosystems to decentralized ecosystems is of paramount importance due to security, privacy, and data use concerns. Since the majority of IoT devices will be used by consumers in peer-to-peer applications, a centralized approach raises many issues of trust related to privacy, control, and censorship. Identity and access management lies at the heart of any user-facing system. Blockchain technologies can be leveraged to augment user authority, transparency, and decentralization. This study proposes a decentralized identity management framework for IoT environments using Hyperledger Fabric and Decentralized Identifiers (DIDs). The system was simulated using Node-RED to model IoT data streams, and key functionalities including device onboarding, authentication, and secure asset querying were successfully implemented. Results demonstrated improved data integrity, transparency, and user control, with reduced reliance on centralized authorities. These findings validate the practicality of blockchain-based identity management in enhancing the security and trustworthiness of IoT infrastructures.
x402 enables Hypertext Transfer Protocol (HTTP) services like application programming interfaces (APIs), data feeds, and inference providers to accept cryptocurrency payments for access. As agents increasingly consume these services, discovery becomes critical: which swap interface should an agent trust? Which data provider is the most reliable? We introduce TraceRank, a reputation-weighted ranking algorithm where payment transactions serve as endorsements. TraceRank seeds addresses with precomputed reputation metrics and propagates reputation through payment flows weighted by transaction value and temporal recency. Applied to x402's payment graph, this surfaces services preferred by high-reputation users rather than those with high transaction volume. Our system combines TraceRank with semantic search to respond to natural language queries with high quality results. We argue that reputation propagation resists Sybil attacks by making spam services with many low-reputation payers rank below legitimate services with few high-reputation payers. Ultimately, we aim to construct a search method for x402 enabled services that avoids infrastructure bias and has better performance than purely volume based or semantic methods.
Watermarking schemes for large language models (LLMs) have been proposed to identify the source of the generated text, mitigating the potential threats emerged from model theft. However, current watermarking solutions hardly resolve the trust issue: the non-public watermark detection cannot prove itself faithfully conducting the detection. We observe that it is attributed to the secret key mostly used in the watermark detection -- it cannot be public, or the adversary may launch removal attacks provided the key; nor can it be private, or the watermarking detection is opaque to the public. To resolve the dilemma, we propose PVMark, a plugin based on zero-knowledge proof (ZKP), enabling the watermark detection process to be publicly verifiable by third parties without disclosing any secret key. PVMark hinges upon the proof of `correct execution' of watermark detection on which a set of ZKP constraints are built, including mapping, random number generation, comparison, and summation. We implement multiple variants of PVMark in Python, Rust and Circom, covering combinations of three watermarking schemes, three hash functions, and four ZKP protocols, to show our approach effectively works under a variety of circumstances. By experimental results, PVMark efficiently enables public verifiability on the state-of-the-art LLM watermarking schemes yet without compromising the watermarking performance, promising to be deployed in practice.
The rising importance of cryptocurrencies as financial assets pushed their applicability from an object of speculation closer to standard financial instruments such as loans. In this work, we initiate the study of secure protocols that enable fiat-denominated loans collateralized by cryptocurrencies such as Bitcoin. We provide limited-custodial protocols for such loans relying only on trusted arbitration and provide their game-theoretical analysis. We also highlight various interesting directions for future research.