Sai Teja Reddy Adapala, Yashwanth Reddy Alugubelly
The proliferation of autonomous AI agents marks a paradigm shift toward complex, emergent multi-agent systems. This transition introduces systemic security risks, including control-flow hijacking and cascading failures, that traditional cybersecurity paradigms are ill-equipped to address. This paper introduces the Aegis Protocol, a layered security framework designed to provide strong security guarantees for open agentic ecosystems. The protocol integrates three technological pillars: (1) non-spoofable agent identity via W3C Decentralized Identifiers (DIDs); (2) communication integrity via NIST-standardized post-quantum cryptography (PQC); and (3) verifiable, privacy-preserving policy compliance using the Halo2 zero-knowledge proof (ZKP) system. We formalize an adversary model extending Dolev-Yao for agentic threats and validate the protocol against the STRIDE framework. Our quantitative evaluation used a discrete-event simulation, calibrated against cryptographic benchmarks, to model 1,000 agents. The simulation showed a 0 percent success rate across 20,000 attack trials. For policy verification, analysis of the simulation logs reported a median proof-generation latency of 2.79 seconds, establishing a performance baseline for this class of security. While the evaluation is simulation-based and early-stage, it offers a reproducible baseline for future empirical studies and positions Aegis as a foundation for safe, scalable autonomous AI.
Krishnendu Chatterjee, Jan Matyáš Křišťan, Stefan Schmid, Jakub Svoboda · 5 authors
Payment channel networks (PCNs) are a promising technology that alleviates blockchain scalability by shifting the transaction load from the blockchain to the PCN. Nevertheless, the network topology has to be carefully designed to maximise the transaction throughput in PCNs. Additionally, users in PCNs also have to make optimal decisions on which transactions to forward and which to reject to prolong the lifetime of their channels. In this work, we consider an input sequence of transactions over $p$ parties. Each transaction consists of a transaction size, source, and target, and can be either accepted or rejected (entailing a cost). The goal is to design a PCN topology among the $p$ cooperating parties, along with the channel capacities, and then output a decision for each transaction in the sequence to minimise the cost of creating and augmenting channels, as well as the cost of rejecting transactions. Our main contribution is an $\mathcal{O}(p)$ approximation algorithm for the problem with $p$ parties. We further show that with some assumptions on the distribution of transactions, we can reduce the approximation ratio to $\mathcal{O}(\sqrt{p})$. We complement our theoretical analysis with an empirical study of our assumptions and approach in the context of the Lightning Network.
Vasileios Kouvakis, Stylianos E. Trevlakis, Alexandros-Apostolos A. Boulogeorgos, Hongwu Liu · 6 authors
Security has always been a priority, for researchers, service providers and network operators when it comes to radio access networks (RAN). One wireless access approach that has captured attention is blockchain enabled RAN (B-RAN) due to its secure nature. This research introduces a framework that integrates blockchain technology into RAN while also addressing the limitations of state-of-the-art models. The proposed framework utilizes queuing and Markov chain theory to model the aspects of B-RAN. An extensive evaluation of the models performance is provided, including an analysis of timing factors and a focused assessment of its security aspects. The results demonstrate reduced latency and comparable security making the presented framework suitable for diverse application scenarios.
Elliptic Curve Cryptography (ECC) is a fundamental component of modern public-key cryptosystems that enable efficient and secure digital signatures, key exchanges, and encryption. Its core operation, scalar multiplication, denoted as $k \cdot P$, where $P$ is a base point and $k$ is a private scalar, relies heavily on the secrecy and unpredictability of $k$. Conventionally, $k$ is selected using user input or pseudorandom number generators. However, in resource-constrained environments with weak entropy sources, these approaches may yield low-entropy or biased scalars, increasing susceptibility to side-channel and key recovery attacks. To mitigate these vulnerabilities, we introduce an optimization-driven scalar generation method that explicitly maximizes bit-level entropy. Our approach uses differential evolution (DE), a population-based metaheuristic algorithm, to search for scalars whose binary representations exhibit maximal entropy, defined by an even and statistically uniform distribution of ones and zeros. This reformulation of scalar selection as an entropy-optimization problem enhances resistance to entropy-based cryptanalytic techniques and improves overall unpredictability. Experimental results demonstrate that DE-optimized scalars achieve entropy significantly higher than conventionally generated scalars. The proposed method can be integrated into existing ECC-based protocols, offering a deterministic, tunable alternative to traditional randomness, ideal for applications in blockchain, secure messaging, IoT, and other resource-constrained environments.
Recent advancements in money laundering detection have demonstrated the potential of using graph neural networks to capture laundering patterns accurately. However, existing models are not explicitly designed to detect the diverse patterns of off-chain cryptocurrency money laundering. Neglecting any laundering pattern introduces critical detection gaps, as each pattern reflects unique transactional structures that facilitate the obfuscation of illicit fund origins and movements. Failure to account for these patterns may result in under-detection or omission of specific laundering activities, diminishing model accuracy and allowing schemes to bypass detection. To address this gap, we propose the MPOCryptoML model to effectively detect multiple laundering patterns in cryptocurrency transactions. MPOCryptoML includes the development of a multi-source Personalized PageRank algorithm to identify random laundering patterns. Additionally, we introduce two novel algorithms by analyzing the timestamp and weight of transactions in high-volume financial networks to detect various money laundering structures, including fan-in, fan-out, bipartite, gather-scatter, and stack patterns. We further examine correlations between these patterns using a logistic regression model. An anomaly score function integrates results from each module to rank accounts by anomaly score, systematically identifying high-risk accounts. Extensive experiments on public datasets including Elliptic++, Ethereum fraud detection, and Wormhole transaction datasets validate the efficacy and efficiency of MPOCryptoML. Results show consistent performance gains, with improvements up to 9.13% in precision, up to 10.16% in recall, up to 7.63% in F1-score, and up to 10.19% in accuracy.
Bitcoin's Proof of Work (PoW) mechanism, while central to achieving decentralized consensus, has long been criticized for excessive energy use and hardware inefficiencies \cite{devries2018bitcoin, truby2018decarbonizing}. This paper introduces a hybrid architecture that replaces Bitcoin's traditional PoW with a centralized, cloud-based collaborative training framework. In this model, miners contribute computing resources to train segments of horizontally scaled machine learning models on preprocessed datasets, ensuring privacy and generating meaningful outputs \cite{li2017securing}. A central server evaluates contributions using two metrics: number of parameters trained and reduction in model loss during each cycle. At the end of every cycle, a weighted lottery selects the winning miner, who receives a digitally signed certificate. This certificate serves as a verifiable substitute for PoW and grants the right to append a block to the blockchain \cite{nakamoto2008bitcoin}. By integrating digital signatures and SHA-256 hashing \cite{nist2015sha}, the system preserves blockchain integrity while redirecting energy toward productive computation. The proposed approach addresses the sustainability concerns of traditional mining by converting resource expenditure into socially valuable work, aligning security incentives with real-world computational progress.
Blockchain address poisoning is an emerging phishing attack that crafts "similar-looking" transfer records in the victim's transaction history, which aims to deceive victims and lure them into mistakenly transferring funds to the attacker. Recent works have shown that millions of Ethereum users were targeted and lost over 100 million US dollars. Ethereum crypto wallets, serving users in browsing transaction history and initiating transactions to transfer funds, play a central role in deploying countermeasures to mitigate the address poisoning attack. However, whether they have done so remains an open question. To fill the research void, in this paper, we design experiments to simulate address poisoning attacks and systematically evaluate the usability and security of 53 popular Ethereum crypto wallets. Our evaluation shows that there exist communication failures between 12 wallets and their transaction activity provider, which renders them unable to download the users' transaction history. Besides, our evaluation also shows that 16 wallets pose a high risk to their users due to displaying fake token phishing transfers. Moreover, our further analysis suggests that most wallets rely on transaction activity providers to filter out phishing transfers. However, their phishing detection capability varies. Finally, we found that only three wallets throw an explicit warning message when users attempt to transfer to the phishing address, implying a significant gap within the broader Ethereum crypto wallet community in protecting users from address poisoning attacks. Overall, our work shows that more efforts are needed by the Ethereum crypto wallet developer community to achieve the highest usability and security standard. Our bug reports have been acknowledged by the developer community, who are currently developing mitigation solutions.
Due to HIPAA and other privacy regulations, it is imperative to maintain patient privacy while conducting research on patient health records. In this paper, we propose AegisBlock, a patient-centric access controlled framework to share medical records with researchers such that the anonymity of the patient is maintained while ensuring the trustworthiness of the data provided to researchers. AegisBlock allows for patients to provide access to their medical data, verified by miners. A researcher submits a time-based range query to request access to records from a certain patient, and upon patient approval, access will be granted. Our experimental evaluation results show that AegisBlock is scalable with respect to the number of patients and hospitals in the system, and efficient with up to 50% of malicious miners.
The global financial system stands at an inflection point. Stablecoins represent the most significant evolution in banking since the abandonment of the gold standard, positioned to enable "Banking 2.0" by seamlessly integrating cryptocurrency innovation with traditional finance infrastructure. This transformation rivals artificial intelligence as the next major disruptor in the financial sector. Modern fiat currencies derive value entirely from institutional trust rather than physical backing, creating vulnerabilities that stablecoins address through enhanced stability, reduced fraud risk, and unified global transactions that transcend national boundaries. Recent developments demonstrate accelerating institutional adoption: landmark U.S. legislation including the GENIUS Act of 2025, strategic industry pivots from major players like JPMorgan's crypto-backed loan initiatives, and PayPal's comprehensive "Pay with Crypto" service. Widespread stablecoin implementation addresses critical macroeconomic imbalances, particularly the inflation-productivity gap plaguing modern monetary systems, through more robust and diversified backing mechanisms. Furthermore, stablecoins facilitate deregulation and efficiency gains, paving the way for a more interconnected international financial system. This whitepaper comprehensively explores how stablecoins are poised to reshape banking, supported by real-world examples, current market data, and analysis of their transformative potential.
Bernhard Kauer, Aleksandr Petrosyan, Benjamin Livshits
The fundamental basis for maintaining integrity within contemporary blockchain systems is provided by authenticated databases. Our analysis indicates that a significant portion of the approaches applied in this domain fail to sufficiently meet the stringent requirements of systems processing transactions at rates of multi-million TPS. AlDBaran signifies a substantial advancement in authenticated databases. By eliminating disk I/O operations from the critical path, implementing prefetching strategies, and refining the update mechanism of the Merkle tree, we have engineered an authenticated data structure capable of handling state updates efficiently at a network throughput of 50 Gbps. This throughput capacity significantly surpasses any empirically documented blockchain throughput, guaranteeing the ability of even the most high-throughput blockchains to generate state commitments effectively. AlDBaran provides support for historical state proofs, which facilitates a wide array of novel applications. For instance, the deployment of AlDBaran could enable blockchains that do not currently support state commitments to offer functionalities for light clients and/or implement rollups. When benchmarked against alternative authenticated data structure projects, AlDBaran exhibits superior performance and simplicity. In particular, AlDBaran achieves speeds of approximately 48 million updates per second using an identical machine configuration. This characteristic renders AlDBaran an attractive solution for resource-limited environments, as its historical data capabilities can be modularly isolated (and deactivated), which further enhances performance. On consumer-level portable hardware, it achieves approximately 8 million updates/s in an in-memory setting and 5 million updates/s with snapshots at sub-second intervals, illustrating compelling and cost-effective scalability.
Widespread reuse of open-source code in smart contract development boosts programming efficiency but significantly amplifies bug propagation across contracts, while dedicated methods for detecting similar smart contract functions remain very limited. Conventional abstract-syntax-tree (AST) based methods for smart contract similarity detection face challenges in handling intricate tree structures, which impedes detailed semantic comparison of code. Recent deep-learning based approaches tend to overlook code syntax and detection interpretability, resulting in suboptimal performance. To fill this research gap, we introduce SmartDetector, a novel approach for computing similarity between smart contract functions, explainable at the fine-grained statement level. Technically, SmartDetector decomposes the AST of a smart contract function into a series of smaller statement trees, each reflecting a structural element of the source code. Then, SmartDetector uses a classifier to compute the similarity score of two functions by comparing each pair of their statement trees. To address the infinite hyperparameter space of the classifier, we mathematically derive a cosine-wise diffusion process to efficiently search optimal hyperparameters. Extensive experiments conducted on three large real-world datasets demonstrate that SmartDetector outperforms current state-of-the-art methods by an average improvement of 14.01% in F1-score, achieving an overall average F1-score of 95.88%.
Aydin Abadi, Basil Aldali, Benito Vincent, Elliot A. J. Hurley · 13 authors
Weather forecasting plays a vital role in disaster preparedness, agriculture, and resource management, yet current centralized forecasting systems are increasingly strained by security vulnerabilities, limited scalability, and susceptibility to single points of failure. To address these challenges, we propose a decentralized weather forecasting framework that integrates Federated Learning (FL) with blockchain technology. FL enables collaborative model training without exposing sensitive local data; this approach enhances privacy and reduces data transfer overhead. Meanwhile, the Ethereum blockchain ensures transparent and dependable verification of model updates. To further enhance the system's security, we introduce a reputation-based voting mechanism that assesses the trustworthiness of submitted models while utilizing the Interplanetary File System (IPFS) for efficient off-chain storage. Experimental results demonstrate that our approach not only improves forecasting accuracy but also enhances system resilience and scalability, making it a viable candidate for deployment in real-world, security-critical environments.
In this paper, we present NetMoniAI, an agentic AI framework for automatic network monitoring and security that integrates decentralized analysis with lightweight centralized coordination. The framework consists of two layers: autonomous micro-agents at each node perform local traffic analysis and anomaly detection. A central controller then aggregates insights across nodes to detect coordinated attacks and maintain system-wide situational awareness. We evaluated NetMoniAI on a local micro-testbed and through NS-3 simulations. Results confirm that the two-tier agentic-AI design scales under resource constraints, reduces redundancy, and improves response time without compromising accuracy. To facilitate broader adoption and reproducibility, the complete framework is available as open source. This enables researchers and practitioners to replicate, validate, and extend it across diverse network environments and threat scenarios. Github link: https://github.com/pzambare3/NetMoniAI
Federico Calandra, Marco Bernardo, Andrea Esposito, Francesco Fabris
Blockchains are widely recognized for their immutability, which provides robust guarantees of data integrity and transparency. However, this same feature poses significant challenges in real-world situations that require regulatory compliance, correction of erroneous data, or removal of sensitive information. Redactable blockchains address the limitations of traditional ones by enabling controlled, auditable modifications to blockchain data, primarily through cryptographic mechanisms such as chameleon hash functions and alternative redaction schemes. This report examines the motivations for introducing redactability, surveys the cryptographic primitives that enable secure edits, and analyzes competing approaches and their shortcomings. Special attention is paid to the practical deployment of redactable blockchains in private settings, with discussions of use cases in healthcare, finance, Internet of drones, and federated learning. Finally, the report outlines further challenges, also in connection with reversible computing, and the future potential of redactable blockchains in building law-compliant, trustworthy, and scalable digital infrastructures.
Digital payments play a pivotal role in the burgeoning digital economy. Moving forward, the enhancement of digital payment systems necessitates programmability, going beyond just efficiency and convenience, to meet the evolving needs and complexities. Smart contract platforms like Central Bank Digital Currency (CBDC) networks and blockchains support programmable digital payments. However, the prevailing paradigm of programming payment logics involves coding smart contracts with programming languages, leading to high costs and significant security challenges. A novel and versatile method for payment programming on DLTs was presented in this paper - transforming digital currencies into token streams, then pipelining smart contracts to authorize, aggregate, lock, direct, and dispatch these streams efficiently from source to target accounts. By utilizing a small set of configurable templates, a few specialized smart contracts could be generated, and support most of payment logics through configuring and composing them. This approach could substantially reduce the cost of payment programming and enhance security, self-enforcement, adaptability, and controllability, thus hold the potential to become an essential component in the infrastructure of digital economy.
Jane Carney, Kushal Upreti, Gaby G. Dagher, Tim Andersen
Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that degrade model performance and integrity. Current poisoning detection methods in federated learning lack a standardized detection method or take significant liberties with trust. In this paper, we present \Sys, a novel blockchain-enabled poison detection framework in federated learning. The framework decentralizes the role of the global server across participating clients. We introduce a judge model used to detect data poisoning in model updates. The judge model is produced by each client and verified to reach consensus on a single judge model. We implement our solution to show \Sys is robust against data poisoning attacks and the creation of our judge model is scalable.
Proof-of-work allows Bitcoin to boast security amidst arbitrary fluctuations in participation of miners throughout time, so long as, at any point in time, a majority of hash power is honest. In recent years, however, the pendulum has shifted in favor of proof-of-stake-based consensus protocols. There, the sleepy model is the most prominent model for handling fluctuating participation of nodes. However, to date, no protocol in the sleepy model rivals Bitcoin in its robustness to drastic fluctuations in participation levels, with state-of-the-art protocols making various restrictive assumptions. In this work, we present a new adversary model, called external adversary. Intuitively, in our model, corrupt nodes do not divulge information about their secret keys. In this model, we show that protocols in the sleepy model can meaningfully claim to remain secure against fully fluctuating participation, without compromising efficiency or corruption resilience. Our adversary model is quite natural, and arguably naturally captures the process via which malicious behavior arises in protocols, as opposed to traditional worst-case modeling. On top of which, the model is also theoretically appealing, circumventing a barrier established in a recent work of Malkhi, Momose, and Ren.
We argue that the technical foundations of non-fungible tokens (NFTs) remain inadequately understood. Prior research has focused on market dynamics, user behavior, and isolated security incidents, yet systematic analysis of the standards underpinning NFT functionality is largely absent. We present the first study of NFTs through the lens of Ethereum Improvement Proposals (EIPs). We conduct a large-scale empirical analysis of 191 NFT-related EIPs and 10K+ Ethereum Magicians discussions (as of July, 2025). We integrate multi-dimensional analyses including the automated parsing of Solidity interfaces, graph-based modeling of inheritance structures, contributor profiling, and mining of community discussion data. We distinguish foundational from emerging standards, expose poor cross-version interoperability, and show that growing functional complexity heightens security risks.
Dan Ivanov, Tristan Freiberg, Shahabi, Shirin, Jonathan Gold · 5 authors
DSperse is a modular framework for distributed machine learning inference with strategic cryptographic verification. Operating within the emerging paradigm of distributed zero-knowledge machine learning, DSperse avoids the high cost and rigidity of full-model circuitization by enabling targeted verification of strategically chosen subcomputations. These verifiable segments, or "slices", may cover part or all of the inference pipeline, with global consistency enforced through audit, replication, or economic incentives. This architecture supports a pragmatic form of trust minimization, localizing zero-knowledge proofs to the components where they provide the greatest value. We evaluate DSperse using multiple proving systems and report empirical results on memory usage, runtime, and circuit behavior under sliced and unsliced configurations. By allowing proof boundaries to align flexibly with the model's logical structure, DSperse supports scalable, targeted verification strategies suited to diverse deployment needs.
Open access
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Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
This paper introduces a structural game-theoretic model to value decentralized digital assets like Bitcoin. Instead of relying on speculative beliefs, it frames the asset's price within a Rational-Expectations Security-Utility Nash Equilibrium (RESUNE). This equilibrium is a fixed point where the market-clearing price dictates the hash rate through a free-entry mining model, which in turn endogenously sets the network's security. The security, defined as one minus the probability of a 51% attack, is determined via a global games model of attacker coordination, providing a unique and continuous security function. We prove the existence of a RESUNE and offer conditions for its uniqueness and stability. The model predicts that the stabilizing direct effect of price on demand must outweigh the potentially destabilizing feedback from price to security. The framework generates testable predictions, such as a protocol halving causing a contraction in both hash rate and price. A structural Vector Autoregression (VAR) model is proposed to test this mechanism. The model decomposes Bitcoin's value into transactional utility, security, and speculative components and explains the observed unidirectional causality from price to hash rate.
Academic credential fraud threatens educational integrity, especially in developing countries like Bangladesh, where verification methods are primarily manual and inefficient. To address this challenge, we present ShikkhaChain, a blockchain-powered certificate management platform designed to securely issue, verify, and revoke academic credentials in a decentralized and tamper-proof manner. Built on Ethereum smart contracts and utilizing IPFS for off-chain storage, the platform offers a transparent, scalable solution accessible through a React-based DApp with MetaMask integration. ShikkhaChain enables role-based access for governments, regulators, institutions, and public verifiers, allowing QR-based validation and on-chain revocation tracking. Our prototype demonstrates enhanced trust, reduced verification time, and improved international credibility for Bangladeshi degrees, promoting a more reliable academic and employment ecosystem.
Cross-chain asset exchange is crucial for blockchain interoperability. Existing solutions rely on trusted third parties and risk asset loss, or use decentralized alternatives like atomic swaps, which suffer from grief attacks. Griefing occurs when a party prematurely exits, locking the counterparty's assets until a timelock expires. Hedged Atomic Swaps mitigate griefing by introducing a penalty premium; however, they increase the number of transactions from four (as in Tier Nolan's swap) to six, which in turn introduces new griefing risks. Grief-Free (GF) Swap reduces this to five transactions by consolidating assets and premiums on a single chain. However, no existing protocol achieves grief-free asset exchange in just four transactions. This paper presents 4-Swap, the first cross-chain atomic swap protocol that is both grief-free and bribery-safe, while completing asset exchange in just four transactions. By combining the griefing premium and principal into a single transaction per chain, 4-Swap reduces on-chain transactions, leading to faster execution compared to previous grief-free solutions. It is fully compatible with Bitcoin and operates without the need for any new opcodes. A game-theoretic analysis shows that rational participants have no incentive to deviate from the protocol, ensuring robust compliance and security.
Nicknames for Group Signatures (NGS) is a new signature scheme that extends Group Signatures (GS) with Signatures with Flexible Public Keys (SFPK). Via GS, each member of a group can sign messages on behalf of the group without revealing his identity, except to a designated auditor. Via SFPK, anyone can create new identities for a particular user, enabling anonymous transfers with only the intended recipient able to trace these new identities. To prevent the potential abuses that this anonymity brings, NGS integrates flexible public keys into the GS framework to support auditable transfers. In addition to introducing NGS, we describe its security model and provide a mathematical construction proved secure in the Random Oracle Model. As a practical NGS use case, we build NickHat, a blockchain-based token-exchange prototype system on top of Ethereum.
S M Mostaq Hossain, Sheikh Ghafoor, Kumar Yelamarthi, Venkata Prasanth Yanambaka
Physically Unclonable Function (PUF) offers a secure and lightweight alternative to traditional cryptography for authentication due to their unique device fingerprint. However, their dependence on specialized hardware hinders their adoption in diverse applications. This paper proposes a novel blockchain framework that leverages SoftPUF, a software-based approach mimicking PUF. SoftPUF addresses the hardware limitations of traditional PUF, enabling secure and efficient authentication for a broader range of devices within a blockchain network. The framework utilizes a machine learning model trained on PUF data to generate unique, software-based keys for each device. These keys serve as secure identifiers for authentication on the blockchain, eliminating the need for dedicated hardware. This approach facilitates the integration of legacy devices from various domains, including cloud-based solutions, into the blockchain network. Additionally, the framework incorporates well-established defense mechanisms to ensure robust security against various attacks. This combined approach paves the way for secure and scalable authentication in diverse blockchain-based applications. Additionally, to ensure robust security, the system incorporates well-established defense mechanisms against various attacks, including 51%, phishing, routing, and Sybil attacks, into the blockchain network. This combined approach paves the way for secure and efficient authentication in a wider range of blockchain-based applications.