GLYPH is a transparent verification layer for Ethereum for trustless on-chain verification of heterogeneous proof systems. It unifies upstream SNARK and STARK settlement through a single packed arity-8 sumcheck verifier over p = 2^128 - 159, while preserving upstream assumptions. The design centers on a universal adapter surface, UCIR compilation, and a chain-bound artifact interface for stateless verification. Benchmark evidence in the whitepaper reports 29.45k total transaction gas in recorded testnet receipts. This record includes the whitepaper and the formal proof appendix.
Airdrops are widely used in blockchain ecosystems as mechanisms for token distribution, user bootstrapping, and governance decentralization. These mechanisms frequently rely on balance or stake snapshots taken at specific blockchain heights or epochs to determine eligibility. However, snapshot-based distribution introduces a critical temporal vulnerability: the assumption that momentary state accurately represents sustained economic participation.This paper defines and analyzes <b><i>Airdrop Snapshot Spoofing</i></b>, a class of temporal state manipulation attacks in which adversaries exploit the gap between snapshot definition, execution, and settlement to illegitimately capture token allocations. We demonstrate that such exploits are not edge cases but structural weaknesses inherent to snapshot-based systems across Proof-of-Stake (PoS) and tokenized networks. We further argue that snapshot spoofing represents a core-layer economic security failure rather than a marketing or distribution flaw, and we outline mitigation strategies based on time-weighted enforcement and validator-level continuity checks.
<b><i>Zombie Validator Resurrection</i></b> is a core consensus-layer exploit in Proof-of-Stake (PoS) and alternative Layer-1 networks where inactive, slashed, or economically abandoned validators regain influence without restoring proportional economic security. Through protocol gaps, state resets, or weak liveness enforcement, validators that should be neutralized re?-enter consensus, undermining safety assumptions and enabling stealth attacks. This paper formalizes the structural conditions enabling zombie validators, analyzes common resurrection mechanisms, and examines systemic risks to consensus integrity. We propose mitigation strategies to enforce validator lifecycle accountability and safeguard decentralized networks against stealth reactivation attacks.
Zero-knowledge důkazy (ZKP) umožňují dokazovateli přesvědčit ověřovatele o správnosti tvrzení, aniž by odhalil podkladový svědek. V posledních letech se ZKP stal klíčovou součástí aplikací chránících soukromí i škálovatelných blockchainových systémů, od stručných rollupů založených na SNARK až po transparentní konstrukce STARK založené na hašovacích funkcích a důkazy vykonání v systémech zkVM/zkEVM. Tato diplomová práce zkoumá hlavní moderní rodiny ZKP, včetně pairing-based zk-SNARKů, Plonkish protokolů, zk-STARKů, Bulletproofs a novějších hash-based přístupů k proximity testingu, jako je WHIR, se zaměřením na jejich kryptografické stavební bloky, bezpečnostní předpoklady a praktické kompromisy. Práce dále mapuje existující benchmarkingové přístupy a identifikuje běžné problémy, které komplikují spravedlivé porovnání, včetně nekonzistentních benchmarkových úloh, heterogenní volby parametrů a nereprodukovatelných běhových \\ prostředí. Na základě této analýzy práce vymezuje klíčové hodnoticí metriky a navrhuje sjednocený benchmarkingový framework typu host-agent s kontejnerizovanými provery, explicitními run manifesty, standardizovanými kontrakty výsledků a podporou jak pro CPU, tak pro GPU výpočetní cesty napříč systémy založenými na obvodech i zkVM. S využitím tohoto frameworku práce hodnotí reprezentativní implementace na sadě sémanticky porovnatelných primárních benchmarkových úloh a sekundárních benchmarkových úloh orientovaných na specializaci. Výsledky ukazují, že neexistuje jeden univerzálně nejlepší proof system: transparentní frameworky založené na obvodech, jako jsou Plonky3 a Winterfell, dosahují u primárních benchmarkových úloh nejlepších výsledků z hlediska doby generování důkazu a paměťových nároků, pairing-based systémy jako Groth16 a ICICLE poskytují nejmenší důkazy a nejrychlejší verifikaci pro scénáře omezené možnostmi ověřovatele nebo pro on-chain nasazení a systémy zkVM, jako jsou RISC Zero a SP1, směňují vyšší spotřebu paměti a větší důkazy za lepší programovatelnost a snazší integraci obecně použitelných výpočtů. Experimenty dále ukazují, že akcelerace pomocí GPU je nejpřínosnější u dostatečně velkých benchmarkových úloh, zatímco inicializační režie a limity VRAM její přínos u menších případů snižují. Celkově práce přispívá jak praktickým jednotným benchmarkingovým frameworkem, tak experimentálně podloženými doporučeními pro volbu ZKP přístupů v různých podmínkách nasazení, hardwaru, důvěryhodnostních modelů a bezpečnostních omezení.
Alireza Kavousi, Duc V. Le, Philipp Jovanovic, George Danezis
Maximal Extractable Value (MEV) is a crucial challenge in blockchains and cryptocurrencies. A principal countermeasure is using encrypted mempools to hide the transaction payloads until they are committed in a block. However, the existing approaches based on encrypted mempools remain vulnerable to metadata leakage and may not provide sufficient mitigation against block producers due to their sole control in block preparation. In this paper, we propose techniques that utilize randomized permutation on the committed block, offering a multi-layer solution. With a focus on proof-of-stake (PoS) committee-based consensus, we then introduce BlindPerm, a framework that enhances an encrypted mempool with permutation and present various optimizations. Notably, we propose a construction where this enhancement comes at essentially no overhead by piggybacking on the encrypted mempool and without relying on any external entity such as randomness beacon. Further, we illustrate the effectiveness of our solutions by running simulations using historical Ethereum data.
Reentrancy remains one of the most critical vulnerabilities affecting Ethereum smart contracts. While many existing analysis tools focus on detecting classical single-function reentrancy, more complex forms such as cross-function reentrancy are harder to identify because they depend on execution semantics and interactions between multiple functions. In this work, we study reentrancy at the level of Ethereum Virtual Machine (EVM) execution traces. We extend the TxSpector framework with new Datalog-based detection rules designed to capture cross-function reentrancy patterns. To support this analysis, we also modernize the trace extraction component by adapting it to recent versions of the Ethereum client and updated EVM instructions. The proposed approach is evaluated on real Ethereum on-chain transaction traces. The results show that our method is able to detect cross-function reentrancy behaviors that are not captured by the original TxSpector rules, demonstrating the effectiveness of pattern-based logic detection at the EVM execution level.
Public distributed ledgers enforce integrity through radical transparency, creating tension with data minimization principles required for regulatory compliance. While Zero-Knowledge Proofs (ZKPs) offer a theoretical privacy solution, existing constructions often overlook adversarial constraints in smart contract environments. Specifically, the asynchronous decoupling of off-chain proof generation from on-chain submission introduces front-running and proof-reuse risks in public mempools. In this work, we formalize Selective Disclosure Authorization Schemes (SDAS), a cryptographic primitive for granular and revocable compliance checks on public ledgers without revealing the underlying witness. We define a security model for SDAS, introducing Ledger-Bound Attribute Unlinkability and Context-Aware Sender Binding to capture how valid proofs remain bound to their intended authorization context. To validate sender binding, we present ZK-Compliance, an Ethereum-based instantiation that operationalizes a user-controlled "Grant, Verify, Revoke" lifecycle. We implement the sender-binding component using a 14-constraint Circom circuit that anchors the zero-knowledge proof to the executing on-chain sender address. Our Sepolia evaluation confirms practical viability: browser-based proof generation executes in under 200 ms, and on-chain verification costs 240,512 gas, neutralizing proof reuse by different callers while preserving strict attribute privacy.
Transaction fees are a core economic mechanism in blockchain systems, intended to price scarce blockspace and align resource consumption with economic cost. However, in Proof-of-Stake (PoS) and decentralized finance (DeFi) environments, fee mechanisms can be exploited to impose asymmetric and persistent economic harm without violating protocol rules. This paper defines <b><i>Gas Fee Drain Loops</i></b>, commonly referred to as <i>grief attacks</i>, as a class of economic exhaustion attacks that weaponize execution costs, transaction ordering, and revert semantics to drain capital from targeted participants. We analyze the structural conditions that enable such attacks, demonstrate why conventional fee market assumptions fail under adversarial strategies, and show how gas griefing degrades security through economic exclusion rather than consensus failure. Finally, we propose a logic-layer mitigation model that bounds execution costs, restores economic symmetry, and preserves open participation under adversarial conditions.
Proof-of-Stake (PoS) and stablecoin systems rely on staking and collateralization mechanisms to represent real economic security. However, an increasing number of protocols permit the creation of <i>synthetic stake</i>—derivative, mirrored, or recursively referenced representations of the same underlying capital. This paper defines Synthetic Stake Inflation as a structural vulnerability in which the apparent quantity of staked or collateralized assets exceeds the realizable economic value securing the system. We analyze how liquid staking derivatives, recursive collateral usage, and cross-protocol composability enable stake amplification without proportional risk exposure. Existing safeguards, including slashing, collateral ratios, and audits, are shown to be insufficient due to their inability to detect stake duplication across domains. We propose a logic-layer enforcement model that constrains stake representation through exclusivity rules, provenance verification, and validator-level accounting. This approach restores the correspondence between economic reality and on-chain security metrics, addressing a critical integrity gap in modern PoS and stablecoin architectures.
Proof-of-Stake (PoS) networks rely on slashing mechanisms to deter validator misbehavior and preserve consensus security. While early designs emphasized cryptographically verifiable conditions, many contemporary PoS systems increasingly incorporate social, governance, or reputation-based enforcement mechanisms to supplement protocol-level slashing. This paper identifies and formalizes a novel exploit class—<b><i>Social Slashing Exploits</i></b>—where subjective reputation signals, off-chain coordination, or governance influence are weaponized to selectively penalize honest validators or shield malicious actors. We analyze how reputation-driven enforcement undermines determinism, enables cartel behavior, and erodes consensus neutrality without requiring protocol violations. The paper argues that reputation-weighted slashing cannot provide reliable security guarantees in adversarial economic environments and proposes a logic-layer enforcement model based on execution-bound, objective misbehavior proofs. This approach restores deterministic accountability, preserves validator neutrality, and improves long-term system survivability.
This paper introduces Bitcoin-IPC, a protocol that scales Bitcoin through a network of permissionless, interconnected, programmable Proof-of-Stake (PoS) Layer-2 chains, called subnets, whose stake is denominated in L1 BTC. These subnets rely on Bitcoin L1 for the communication of critical information, settlement, and security. Subnets can communicate with each other and with Bitcoin: users deposit BTC from Bitcoin to a subnet and withdraw it back, and transfer wBTC directly between subnets. We provide formal definitions of these bridge protocols, incorporating a firewall property that limits the impact of malicious subnets on the security of the broader network. Our design, inspired by SWIFT messaging and embedded within Bitcoin's SegWit mechanism, enables seamless value transfer across L2 subnets. Uniquely, this mechanism reduces the virtual-byte cost per transaction (vB/tx) by up to 23x, compared to transacting natively on Bitcoin L1, effectively increasing monetary-transaction throughput from 7 tps to over 160 tps, without requiring any modifications to Bitcoin L1.
We frame Ethereum transactions reverted by invariants-require(<invariant>)/ assert(<invariant>)/if (<invariant>) revert statements in the contract implementation-as a positive signal of active on-chain defenses. Despite their value, the defensive patterns in these transactions remain undiscovered and underutilized in security research. We present Raven, a framework that aligns reverted transactions to the invariant causing the reversion in the smart contract source code, embeds these invariants using our BERT-based fine-tuned model, and clusters them by semantic intent to mine defensive invariant categories on Ethereum. Evaluated on a sample of 20,000 reverted transactions, Raven achieves cohesive and meaningful clusters of transaction-reverting invariants. Manual expert review of the mined 19 semantic clusters uncovers six new invariant categories absent from existing invariant catalogs, including feature toggles, replay prevention, proof/signature verification, counters, caller-provided slippage thresholds, and allow/ban/bot lists. To demonstrate the practical utility of this invariant catalog mining pipeline, we conduct a case study using one of the newly discovered invariant categories as a fuzzing oracle to detect vulnerabilities in a real-world attack. Raven thus can map Ethereum's successful defenses. These invariant categories enable security researchers to develop analysis tools based on data-driven security oracles extracted from the smart contracts' working defenses.
Modern cloud-based AI training relies on extensive telemetry and logs to ensure accountability. While these audit trails enable retrospective inspection, they struggle to address the inherent non-determinism of deep learning. Stochastic operations, such as dropout, create an ambiguity surface where attackers can mask malicious manipulations as natural random variance, granting them plausible deniability. Consequently, existing logging mechanisms cannot verify whether stochastic values were generated and applied honestly without exposing sensitive training data. To close this integrity gap, we introduce Verifiable Dropout, a privacy-preserving mechanism based on zero-knowledge proofs. We treat stochasticity not as an excuse but as a verifiable claim. Our approach binds dropout masks to a deterministic, cryptographically verifiable seed and proves the correct execution of the dropout operation. This design enables users to audit the integrity of stochastic training steps post-hoc, ensuring that randomness was neither biased nor cherry-picked, while strictly preserving the confidentiality of the model and data.
The proliferation of Internet of Things (IoT) applications in safety-critical domains, such as healthcare, smart transportation, and industrial automation, demands robust solutions for data integrity, traceability, and security that surpass the capabilities of centralized databases. This paper analyzes how blockchain technology can be integrated with core IoT service functions—including data management, security, device management, group coordination, and automated billing—to enhance immutability, trust, and operational efficiency. Our analysis identifies practical use cases such as consensus-driven tamper-proof storage, role-based access control, firmware integrity verification, and automated micropayments. These use cases showcase blockchain’s potential beyond traditional data storage. Building on this, we propose a novel framework that integrates a permissioned distributed ledger with a standardized IoT service layer platform through a Blockchain Interworking Proxy Entity (BlockIPE). This proxy dynamically maps IoT service functions to smart contracts, enabling flexible data routing to conventional databases or blockchains based on the application requirements. We implement a Dockerized prototype that integrates a C-based oneM2M platform with an Ethereum-compatible permissioned ledger (implemented using Hyperledger Besu) via BlockIPE, incorporating security features such as role-based access control. For performance evaluation, we use Ganache to isolate proxy-level overhead and scalability. At the proxy level, the blockchain-integrated path achieves processing latencies (≈86 ms) comparable to, and slightly faster than, the traditional database path. Although the end-to-end latency is inherently governed by on-chain confirmation (≈0.586–1.086 s), the scalability remains high (up to 100,000 TPS). This validates that the architecture secures IoT ecosystems with manageable operational overhead.
Aaron Chan, Alex Ding, Frank Sicong Chen, Alan Wu · 6 authors
The rapid integration of Large Language Models (LLMs) into decentralized physical infrastructure networks (DePIN) is currently bottlenecked by the Verifiability Trilemma, which posits that a decentralized inference system cannot simultaneously achieve high computational integrity, low latency, and low cost. Existing cryptographic solutions, such as Zero-Knowledge Machine Learning (ZKML), suffer from superlinear proving overheads (O(k NlogN)) that render them infeasible for billionparameter models. Conversely, optimistic approaches (opML) impose prohibitive dispute windows, preventing real-time interactivity, while recent "Proof of Quality" (PoQ) paradigms sacrifice cryptographic integrity for subjective semantic evaluation, leaving networks vulnerable to model downgrade attacks and reward hacking. In this paper, we introduce Optimistic TEE-Rollups (OTR), a hybrid verification protocol that harmonizes these constraints. OTR leverages NVIDIA H100 Confidential Computing Trusted Execution Environments (TEEs) to provide sub-second Provisional Finality, underpinned by an optimistic fraud-proof mechanism and stochastic Zero-Knowledge spot-checks to mitigate hardware side-channel risks. We formally define Proof of Efficient Attribution (PoEA), a consensus mechanism that cryptographically binds execution traces to hardware attestations, thereby guaranteeing model authenticity. Extensive simulations demonstrate that OTR achieves 99% of the throughput of centralized baselines with a marginal cost overhead of $0.07 per query, maintaining Byzantine fault tolerance against rational adversaries even in the presence of transient hardware vulnerabilities.
How users adapt after being sandwiched remains unclear; this paper provides an empirical quantification. Using transaction level data from November 2024 to February 2025, enriched with mempool visibility and ZeroMEV labels, we track user outcomes after their n-th public sandwich: (i) reactivation, i.e., the resumption of on-chain activity within a 60-day window, and (ii) first-time adoption of private routing. We refer to users who do not reactivate within this window as churned, and to users experiencing multiple attacks (n>1) as undergoing repeated exposure. Our analysis reveals measurable behavioral adaptation: around 40% of victims migrate to private routing within 60 days, rising to 54% with repeated exposures. Churn peaks at 7.5% after the first sandwich but declines to 1-2%, consistent with survivor bias. In Nov-Dec 2024 we confirm 2,932 private sandwich attacks affecting 3,126 private victim transactions, producing \$409,236 in losses and \$293,786 in attacker profits. A single bot accounts for nearly two-thirds of private frontruns, and private sandwich activity is heavily concentrated on a small set of DEX pools. These results highlight that private routing does not guarantee protection from MEV extraction: while execution failures push users toward private channels, these remain exploitable and highly concentrated, demanding continuous monitoring and protocol-level defenses.
Abstract This paper revisits Virgo, a well-known transparent zero-knowledge proof system that has been used in many subsequent studies. Through our analysis, we uncover previously overlooked limitations and several exploitable security vulnerabilities within Virgo’s zkVPD protocol design and implementation. We subsequently address these issues and improve Virgo’s zkVPD protocol. Our improvements feature simplified but more efficient VPD and zkVPD algorithms, offering enhanced support for computations over binary fields and their extension fields.
The rapid growth of IoT devices in smart home environments has introduced significant challenges in ensuring secure, scalable, and efficient communication among heterogeneous devices. Centralized architectures suffer from a single point of failure, while blockchain-only solutions face high latency, limiting their use in real-time control. To address these issues, we propose a multi-layered decentralized framework that combines a consortium blockchain, a trusted off-chain coordinator, group-based zero-knowledge proofs (ZKPs), and a two-tiered access control policy (ACP) architecture. The consortium blockchain provides an immutable ledger for device identities and foundational, coarse-grained ACP enforcement through smart contracts, ensuring tamper-proof trust. For privacy-preserving mutual authentication, a group-based ZKP protocol enables collective device authorization without revealing sensitive keys. The off-chain coordinator complements this by enforcing dynamic security mechanisms, including fine-grained ACPv2 checks—such as rate limits, time-of-day restrictions, and device telemetry—in addition to anomaly detection for behavioral risk assessment. This proposed hybrid structure achieves both immutability and high efficiency over traditional methods. A performance evaluation highlighted the framework’s efficiency by demonstrating that the core ZKP verification for a 500-device group can be completed in just 190 ms. The framework drastically reduces on-chain costs, with critical access control policy transactions consuming only 82,748 gas—a reduction of over 90% compared to benchmarked on-chain systems. The complete end-to-end workflow, from user request to secure session establishment, has a latency bound of approximately 3s. Formal security verification with the BAN and AVISPA tools validates resilience against common attacks, including man-in-the-middle, replay, and impersonation, while static analysis using the Slither framework confirms the absence of critical vulnerabilities in the smart contract code. By combining an immutable on-chain foundation with intelligent, dynamic off-chain enforcement, our proposed framework provides a uniquely resilient, scalable, and adaptive security solution for modern smart home systems.
Smart contracts rely on blockchain oracles to access off-chain data, yet existing oracle designs often face challenges such as untrustworthy data sources, weak temporal guarantees, and limited verifiability. This work presents Ivy Oracle, a robust and time-trustworthy data feed framework that enhances the reliability and auditability of off-chain information for smart contracts. Ivy Oracle integrates trusted execution environments (TEEs) for secure data acquisition, an external time server for authenticated timestamps, and a PageRank-based trust model to evaluate source credibility. We implement and evaluate Ivy Oracle on the Ethereum Sepolia testnet, demonstrating that it achieves up to 63.6% lower on-chain gas consumption than Chainlink for signature verification while maintaining only a slight increase in communication overhead due to its dual-attestation mechanism. These results confirm that Ivy Oracle provides strong time trustworthiness and data reliability with minimal performance cost, making it suitable for latency-sensitive blockchain applications.
As vehicular ad hoc networks (VANETs) increase in size and complexity, ensuring secure, flexible, and privacy-preserving vehicle-to-infrastructure (V2I) authentication remains a major challenge. Existing protocols often focus solely on identity verification, overlooking the need for access control based on vehicle attributes. Furthermore, vehicles must obtain authentication credentials from various trusted entities, including automakers, regulators, and government agencies. However, the absence of a unified credential issuance mechanism introduces fragmentation and inconsistencies during the registration process. To address these issues, we propose a V2I authentication protocol, called PriV2I, that integrates distributed credential issuance, attribute-based access control, and strong anonymity guarantees. During vehicle registration, our approach uses Shamir’s Secret Sharing with a threshold t of n across multiple certification authorities (CAs) to consolidate credentials. A vehicle credential can only be issued by a predefined threshold number of CAs, enhancing security and flexibility. Within the authentication protocol, Pointcheval-Sanders (PS) signatures enable fine-grained access control based on vehicle attributes such as type and role. Meanwhile, noninteractive zero-knowledge proofs protect identity privacy by allowing vehicles to prove credential possession and policy compliance without revealing sensitive information. The proposed scheme also supports batch authentication at Roadside Units (RSUs) to efficiently handle high-density environments and includes a comprehensive revocation mechanism to trace and revoke malicious vehicles promptly and securely. In our implementation, the computation cost during the authentication phase is 75.58 ms. The communication overhead per authentication exchange is 992 bytes across two messages. Overall, the protocol provides a secure, scalable, and privacy-preserving solution tailored to modern VANET environments.
Vikash Singh, Little, Barrett, Phil Hayes, Fang, Max · 7 authors
Verifying the private liquidity state of Lightning Network (LN) channels is desirable for auditors, service providers, and network participants who need assurance of financial capacity. Current methods often lack robustness against a malicious or compromised node operator. This paper introduces a methodology for the verification of LN channel balances. The core contribution is a framework that combines Trusted Execution Environments (TEEs) with Zero-Knowledge Transport Layer Security (zkTLS) to provide strong, hardware-backed guarantees. In our proposed method, the node's balance-reporting software runs within a TEE, which generates a remote attestation quote proving the software's integrity. This attestation is then served via an Application Programming Interface (API), and zkTLS is used to prove the authenticity of its delivery. We also analyze an alternative variant where the TEE signs the report directly without zkTLS, discussing the trade-offs between transport-layer verification and direct enclave signing. We further refine this by distinguishing between "Hot Proofs" (verifiable claims via TEEs) and "Cold Proofs" (on-chain settlement), and discuss critical security considerations including hardware vulnerabilities, privacy leakage to third-party APIs, and the performance overhead of enclaved operations.
Love Allen Chijioke Ahakonye, Hamza Ibrahim, Jae-Min Lee, Dong‐Seong Kim
Smart contract environments are increasingly targeted by stealthy, adaptive attacks that evade conventional rule-based or static anomaly detection systems. Inspired by the anglerfish’s bioluminescent filament, which perceives and lures activity in dark, dynamic environments, this research introduces a Bioluminescent Filament-Inspired Artificial Intelligence Perception framework for smart contract intrusion detection. The proposed model emulates biological sensory adaptation through multi-modal attention layers that dynamically illuminate anomalous behaviors in contract execution flows. By integrating self-supervised temporal perception with context-driven feedback, the framework continuously refines its detection sensitivity while maintaining low computational overhead. We evaluate the framework using fuzz-tested smart contract vulnerability datasets that simulate diverse malicious execution behaviors observed in Ethereum environments, demonstrating over 98% detection accuracy with a 40% reduction in latency compared to traditional deep learning-based IDS models. This biologically inspired perception paradigm offers a scalable, energy-efficient solution for securing blockchain-based decentralized systems against evolving threat vectors.