Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.37819

Making Duplicate Reimbursement Unrepresentable: A Verified Ethereum E-Invoice System for Humans and AI Agents

Jia Cai
View original source ↗Read source document ↗Export metadata ↓

Abstract

Electronic invoices are replacing paper invoices worldwide, but today's centralized architectures leave three problems unsolved on the consumption side: an invoice can be submitted for reimbursement repeatedly, authenticity is difficult for recipients to verify, and data is siloed at a central authority that forms both a performance bottleneck and a single point of failure. This paper presents the design, formal analysis, and implementation of a complete blockchain-based electronic invoice system on Ethereum. We formalize the invoice lifecycle as a guarded labeled transition system and prove, under standard cryptographic and consensus assumptions, that the system guarantees: (i) reimbursement uniqueness--an invoice is reimbursed at most once, even across mutually distrusting organizations; (ii) face integrity--any verified invoice matches the recorded one unless keccak256 second-preimage resistance is broken; and (iii) authorization soundness for every lifecycle operation. The core invariants are machine-checked using Solidity SMTChecker, proving inductive validity across all reachable transaction sequences. The architecture models each invoice as a non-fungible, non-tradable token whose state transitions through five guarded subsystems, employing a lock-based protocol that makes duplicate reimbursement unrepresentable rather than merely detectable. We implement the design as a Solidity 0.8 contract with a four-role web application and evaluate it on a private Ethereum network: issuing costs 646,773 gas, full reimbursement costs under 135,000 gas, all operations run in O(1) time, and a single node sustains 137 issuances/s. Finally, the verified contract serves as a safety envelope for LLM-based reimbursement agents, provably rejecting unsafe actions (duplicate, over-limit, or forged-receipt claims) even when the agent's internal policy fails. All code and benchmarks are open-source.

Explore related subjects

cs.CRcs.AIcs.CE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jia Cai. 2026-09-29. Making Duplicate Reimbursement Unrepresentable: A Verified Ethereum E-Invoice System for Humans and AI Agents. https://arxiv.org/abs/2609.37819

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant information from external knowledge bases to provide more accurate, contextually informed, and up-to-date responses. However, this reliance on external knowledge introduces significant security vulnerabilities, as many RAG systems (e.g., Google Search) rely on large and unsanitized data repositories (e.g., Reddit). In this paper, we unveil a novel threat in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base. When a user's query contains attacker-specified trigger words, the RAG retrieves and refers to these malicious passages, enabling the attacker to steer the response without altering the user input or modifying the RAG weights. BadRAG operates in two phases: (i) malicious passages are optimized to be retrieved exclusively when trigger words appear in user queries; (ii) these passages are meticulously crafted to achieve adversarial generation objectives, including denial of service, sentiment manipulation, context leakage, and tool misuse. Our experiments show that injecting just 10 malicious passages (0.04\% of the external corpora) achieves a 98.2\% retrieval success rate and increases negative response rates from 0.22\% to 72\% for queries containing triggers.

cs.CR↗
arXiv2026

Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation

Machine learning-based cybersecurity systems are highly vulnerable to adversarial attacks, while Generative Adversarial Networks (GANs) act as both powerful attack enablers and promising defenses. This survey systematically reviews GAN-based adversarial defenses in cybersecurity (2021--August 31, 2025), consolidating recent progress, identifying gaps, and outlining future directions. Using a PRISMA-compliant systematic literature review protocol, we searched five major digital libraries. From 829 initial records, 185 peer-reviewed studies were retained and synthesized through quantitative trend analysis and thematic taxonomy development. We introduce a four-dimensional taxonomy spanning defensive function, GAN architecture, cybersecurity domain, and adversarial threat model. GANs improve detection accuracy, robustness, and data utility across network intrusion detection, malware analysis, and IoT security. Notable advances include WGAN-GP for stable training, CGANs for targeted synthesis, and hybrid GAN models for improved resilience. Yet, persistent challenges remain such as instability in training, lack of standardized benchmarks, high computational cost, and limited explainability. GAN-based defenses demonstrate strong potential but require advances in stable architectures, benchmarking, transparency, and deployment. We propose a roadmap emphasizing hybrid models, unified evaluation, real-world integration, and defenses against emerging threats such as LLM-driven cyberattacks. This survey establishes the foundation for scalable, trustworthy, and adaptive GAN-powered defenses.

cs.CR↗
arXiv2026

NonTextual Target Attack

Existing gradient-based jailbreak attacks on Large Language Models (LLMs) typically optimize adversarial suffixes to align the LLM output with predefined target responses. However, restricting the objective as inducing fixed targets inherently constrains the adversarial search space, limiting the overall attack efficacy. Furthermore, existing methods typically require numerous optimization iterations to fulfill the large gap between the fixed target and the original LLM output, resulting in low attack efficiency. To overcome these limitations, we propose NonTextual Target Attack (NTA),the first gradient-based attack that relies on a non-textual constrained objective to maximize the unsafety probability of the LLM output, without enforcing any response patterns. For tractable optimization, we further decompose this objective into two constrained sub-objectives, which can be approximated by two differentiable unconstrained losses, to iteratively optimize the response and the adversarial prompt in the neighborhood of the original prompt, with a theoretical analysis to validate the decomposition. In contrast to existing attacks, NTA first realizes gradient-based prompt optimization on a non-textual target and significantly expands the attack space, enabling more flexible and efficient exploration of LLM vulnerabilities. Extensive evaluations show that NTA achieves an average attack success rate of 96.8% against recent safety-aligned LLMs with only 100 optimization iterations on AdvBench, outperforming state-of-the-art gradient-based attacks by over 40%.

cs.CR↗