Related papers: WACANA: A Concolic Analyzer for Detecting On-chain Data Vulnerabilities in WASM Smart Contracts

Related papers

IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation [49.796717294455796]
We present IMMACULATE, a practical auditing framework that detects economically motivated deviations.<n>IMMACULATE selectively audits a small fraction of requests using verifiable computation, achieving strong detection guarantees while amortizing cryptographic overhead.
arXiv Detail & Related papers (2026-02-26T07:21:02Z)
CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks [54.04030169323115]
We introduce CREDIT, a certified ownership verification against Model Extraction Attacks (MEAs)<n>We quantify the similarity between DNN models, propose a practical verification threshold, and provide rigorous theoretical guarantees for ownership verification based on this threshold.<n>We extensively evaluate our approach on several mainstream datasets across different domains and tasks, achieving state-of-the-art performance.
arXiv Detail & Related papers (2026-02-23T23:36:25Z)
Examining the Effectiveness of Transformer-Based Smart Contract Vulnerability Scan [0.0]
We evaluate deep learning-based approaches for vulnerability scanning of smart contracts.<n>We propose VASCOT, a Vulnerability Analyzer for Smart COntracts using Transformers.<n>VASCOT's performance is compared against a state-of-the-art LSTM-based vulnerability detection model.
arXiv Detail & Related papers (2026-01-12T09:00:42Z)
SSR: Safeguarding Staking Rewards by Defining and Detecting Logical Defects in DeFi Staking [55.62033436283969]
Decentralized Finance (DeFi) staking is one of the most prominent applications within the DeFi ecosystem.<n> logical defects in DeFi staking could enable attackers to claim unwarranted rewards.<n>We developed SSR (Safeguarding Staking Reward), a static analysis tool designed to detect logical defects in DeFi staking contracts.
arXiv Detail & Related papers (2026-01-09T15:01:41Z)
Prompt Engineering vs. Fine-Tuning for LLM-Based Vulnerability Detection in Solana and Algorand Smart Contracts [1.0255673932966183]
This paper investigates the capability of large language models (LLMs) to detect vulnerabilities in smart contracts.<n>We focus on the smart contract ecosystem of Solana and Algorand.<n>Our findings suggest that LLM-based approaches are viable for static vulnerability detection in smart contracts.
arXiv Detail & Related papers (2025-11-14T12:50:36Z)
One Signature, Multiple Payments: Demystifying and Detecting Signature Replay Vulnerabilities in Smart Contracts [56.94148977064169]
lacking checks on signature usage conditions can lead to repeated verifications, increasing the risk of permission abuse and threatening contract assets.<n>We define this issue as the Signature Replay Vulnerability (SRV)<n>From 1,419 audit reports across 37 blockchain security companies, we identified 108 with detailed SRV descriptions and classified five types of SRVs.
arXiv Detail & Related papers (2025-11-12T09:17:13Z)
Trace: Securing Smart Contract Repository Against Access Control Vulnerability [58.02691083789239]
GitHub hosts numerous smart contract repositories containing source code, documentation, and configuration files.<n>Third-party developers often reference, reuse, or fork code from these repositories during custom development.<n>Existing tools for detecting smart contract vulnerabilities are limited in their ability to handle complex repositories.
arXiv Detail & Related papers (2025-10-22T05:18:28Z)
ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models [46.71493672772134]
We introduce ZKPROV, a novel cryptographic framework that enables zero-knowledge proofs of LLM provenance.<n>It allows users to verify that a model is trained on a reliable dataset without revealing sensitive information about it or its parameters.<n>Our method cryptographically binds a trained model to its authorized training dataset(s) through zero-knowledge proofs while avoiding proof of every training step.
arXiv Detail & Related papers (2025-06-26T00:49:02Z)
Decompiling Smart Contracts with a Large Language Model [51.49197239479266]
Despite Etherscan's 78,047,845 smart contracts deployed on (as of May 26, 2025), a mere 767,520 ( 1%) are open source.<n>This opacity necessitates the automated semantic analysis of on-chain smart contract bytecode.<n>We introduce a pioneering decompilation pipeline that transforms bytecode into human-readable and semantically faithful Solidity code.
arXiv Detail & Related papers (2025-06-24T13:42:59Z)
CyberGym: Evaluating AI Agents' Cybersecurity Capabilities with Real-World Vulnerabilities at Scale [46.76144797837242]
Large language model (LLM) agents are becoming increasingly skilled at handling cybersecurity tasks autonomously.<n>Existing benchmarks fall short, often failing to capture real-world scenarios or being limited in scope.<n>We introduce CyberGym, a large-scale and high-quality cybersecurity evaluation framework featuring 1,507 real-world vulnerabilities.
arXiv Detail & Related papers (2025-06-03T07:35:14Z)
SmartBugBert: BERT-Enhanced Vulnerability Detection for Smart Contract Bytecode [0.7018579932647147]
This paper introduces SmartBugBert, a novel approach that combines BERT-based deep learning with control flow graph (CFG) analysis to detect vulnerabilities directly from bytecode. Our method first decompiles smart contract bytecode into optimized opcode sequences, extracts semantic features using TF-IDF, constructs control flow graphs to capture execution logic, and isolates vulnerable CFG fragments for targeted analysis.
arXiv Detail & Related papers (2025-04-07T12:30:12Z)
Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs [60.881609323604685]
Large Language Models (LLMs) accessed via black-box APIs introduce a trust challenge. Users pay for services based on advertised model capabilities. providers may covertly substitute the specified model with a cheaper, lower-quality alternative to reduce operational costs. This lack of transparency undermines fairness, erodes trust, and complicates reliable benchmarking.
arXiv Detail & Related papers (2025-04-07T03:57:41Z)
Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions [51.43521977132062]
Money laundering is a financial crime that obscures the origin of illicit funds. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated anti-money laundering investigations. This paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML.
arXiv Detail & Related papers (2025-03-13T05:19:44Z)
SmartLLM: Smart Contract Auditing using Custom Generative AI [0.0]
This paper introduces SmartLLM, a novel approach leveraging fine-tuned LLaMA 3.1 models with Retrieval-Augmented Generation (RAG) By integrating domain-specific knowledge from ERC standards, SmartLLM achieves superior performance compared to static analysis tools like Mythril and Slither. Experimental results demonstrate a perfect recall of 100% and an accuracy score of 70%, highlighting the model's robustness in identifying vulnerabilities.
arXiv Detail & Related papers (2025-02-17T06:22:05Z)
ContractTrace: Retracing Smart Contract Versions for Security Analyses [4.126275271359132]
We introduce ContractTrace, an automated infrastructure that accurately identifies and links versions of smart contracts into coherent lineages.<n>This capability is essential for understanding vulnerability propagation patterns and evaluating the effectiveness of security patches in blockchain environments.
arXiv Detail & Related papers (2024-12-30T11:10:22Z)
CryptoFormalEval: Integrating LLMs and Formal Verification for Automated Cryptographic Protocol Vulnerability Detection [41.94295877935867]
We introduce a benchmark to assess the ability of Large Language Models to autonomously identify vulnerabilities in new cryptographic protocols. We created a dataset of novel, flawed, communication protocols and designed a method to automatically verify the vulnerabilities found by the AI agents.
arXiv Detail & Related papers (2024-11-20T14:16:55Z)
Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis [0.0]
We analyze the state of the art in machine-learning vulnerability detection for smart contracts. We discuss best practices to enhance the accuracy, scope, and efficiency of vulnerability detection in smart contracts.
arXiv Detail & Related papers (2024-07-26T10:09:44Z)
Vulnerability Detection with Code Language Models: How Far Are We? [40.455600722638906]
PrimeVul is a new dataset for training and evaluating code LMs for vulnerability detection. It incorporates a novel set of data labeling techniques that achieve comparable label accuracy to human-verified benchmarks. It also implements a rigorous data de-duplication and chronological data splitting strategy to mitigate data leakage issues.
arXiv Detail & Related papers (2024-03-27T14:34:29Z)
Efficiently Detecting Reentrancy Vulnerabilities in Complex Smart Contracts [35.26195628798847]
Existing vulnerability detection tools perform poorly in terms of efficiency and successful detection rates for vulnerabilities in complex contracts. SliSE provides a robust and efficient method for detection of Reentrancy vulnerabilities for complex contracts.
arXiv Detail & Related papers (2024-03-17T16:08:30Z)
A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure. This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus. We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
arXiv Detail & Related papers (2024-01-19T14:52:04Z)
LookAhead: Preventing DeFi Attacks via Unveiling Adversarial Contracts [15.071155232677643]
Decentralized Finance (DeFi) incidents have resulted in financial damages exceeding 3 billion US dollars. Current detection tools face significant challenges in identifying attack activities effectively. We propose a new direction for detecting DeFi attacks that focuses on identifying adversarial contracts.
arXiv Detail & Related papers (2024-01-14T11:39:33Z)
Vulnerability Scanners for Ethereum Smart Contracts: A Large-Scale Study [44.25093111430751]
In 2023 alone, such vulnerabilities led to substantial financial losses exceeding a billion of US dollars. Various tools have been developed to detect and mitigate vulnerabilities in smart contracts. This study investigates the gap between the effectiveness of existing security scanners and the vulnerabilities that still persist in practice.
arXiv Detail & Related papers (2023-12-27T11:26:26Z)
Enhancing Smart Contract Security Analysis with Execution Property Graphs [48.31617821205042]
We introduce Clue, a dynamic analysis framework specifically designed for a runtime virtual machine. Clue captures critical information during contract executions, employing a novel graph-based representation, the Execution Property Graph. evaluation results reveal Clue's superior performance with high true positive rates and low false positive rates, outperforming state-of-the-art tools.
arXiv Detail & Related papers (2023-05-23T13:16:42Z)
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning [80.85273827468063]
Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable. We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts. We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
arXiv Detail & Related papers (2021-03-23T15:04:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.