Vulnerability and Transaction behavior based detection of Malicious Smart Contracts

• URL: http://arxiv.org/abs/2106.13422v1
• Date: Fri, 25 Jun 2021 04:25:23 GMT
• Title: Vulnerability and Transaction behavior based detection of Malicious Smart Contracts
• Abstract summary: We study the correlation between malicious activities and the vulnerabilities present in Smart Contracts (SCs) We develop and study the feasibility of a scoring mechanism that corresponds to the severity of the vulnerabilities present in SCs. We analyze the utility of severity score towards detection of suspicious SCs using unsupervised machine learning (ML) algorithms.
• Score: 3.646526715728388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Smart Contracts (SCs) in Ethereum can automate tasks and provide different functionalities to a user. Such automation is enabled by the `Turing-complete' nature of the programming language (Solidity) in which SCs are written. This also opens up different vulnerabilities and bugs in SCs that malicious actors exploit to carry out malicious or illegal activities on the cryptocurrency platform. In this work, we study the correlation between malicious activities and the vulnerabilities present in SCs and find that some malicious activities are correlated with certain types of vulnerabilities. We then develop and study the feasibility of a scoring mechanism that corresponds to the severity of the vulnerabilities present in SCs to determine if it is a relevant feature to identify suspicious SCs. We analyze the utility of severity score towards detection of suspicious SCs using unsupervised machine learning (ML) algorithms across different temporal granularities and identify behavioral changes. In our experiments with on-chain SCs, we were able to find a total of 1094 benign SCs across different granularities which behave similar to malicious SCs, with the inclusion of the smart contract vulnerability scores in the feature set.

Related papers

• CLEAR: Causal Context-Based Agentic Reasoning for Vulnerability Detection [3.2238600872149275]
  Causal Context-based Agentic Reasoning (CLEAR) is a novel multi-agent vulnerability detection framework integrated with a causal knowledge graph.<n>Four specialized agents, including the Collector, Claim, Critic, and Judge, collaboratively verify vulnerability hypotheses through retrieved causal contexts.<n>CLEAR improves Pair-Correct (P-C) performance by 130.7% and 71.56% over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-08-04T05:03:11Z)
• Exploiting LLM Agent Supply Chains via Payload-less Skills [10.141577783380281]
  We introduce Semantic Compliance Hijacking (SCH), a payload-less supply chain attack targeting autonomous coding environments.<n>The SCH approach translates malicious goals into unstructured natural language instructions formatted as necessary compliance rules, leading the agent to generate and execute unauthorized code.<n>The findings demonstrate the pervasive nature of this threat, with SCH achieving peak success rates of up to 77.67% for confidentiality breaches.
  arXiv  Detail & Related papers  (2026-05-14T06:55:47Z)
• GoAT-X: A Graph of Auditing Thoughts for Securing Token Transactions in Cross-Chain Contracts [52.51342355102833]
  Cross-chain bridges, the critical infrastructure of the multi-chain ecosystem, have become a primary target for attackers.<n>Existing defenses, such as bytecode-level static analysis, are ill-equipped to handle the semantic complexity of cross-chain interactions.<n>We propose GoAT-X, a framework that shifts automated cross-chain smart contracts auditing from pattern matching toward systematic first-principles verification.
  arXiv  Detail & Related papers  (2026-04-27T11:34:21Z)
• ShieldNet: Network-Level Guardrails against Emerging Supply-Chain Injections in Agentic Systems [56.613157564882925]
  Malicious behaviors are embedded in seemingly benign tools, silently hijacking agent execution, leaking sensitive data, or triggering unauthorized actions.<n>Despite their growing impact, there is currently no comprehensive benchmark for evaluating such threats.<n>We propose ShieldNet, a network-level guardrail framework that detects supply-chain poisoning by observing real network interactions.
  arXiv  Detail & Related papers  (2026-04-06T05:15:00Z)
• On the Adversarial Robustness of Discrete Image Tokenizers [56.377796750281796]
  We first formulate attacks that aim to perturb the features extracted by discrete tokenizers, and thus change the extracted tokens.<n>We fine-tune popular tokenizers with unsupervised adversarial training, keeping all other components frozen.<n>Our approach significantly improves robustness to both unsupervised and end-to-end supervised attacks and generalizes well to unseen tasks and data.
  arXiv  Detail & Related papers  (2026-02-20T14:39:17Z)
• SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking [58.475471437150674]
  We propose sequential watermarking for soft prompts (SWAP)<n>SWAP encodes watermarks through a specific order of defender-specified out-of-distribution classes.<n>Experiments on 11 datasets demonstrate SWAP's effectiveness, harmlessness, and robustness against potential adaptive attacks.
  arXiv  Detail & Related papers  (2025-11-05T13:48:48Z)
• Are Sparse Autoencoders Useful for Java Function Bug Detection? [5.119371135458389]
  Software vulnerabilities are a major source of security breaches.<n>Traditional methods for vulnerability detection are limited by high false positive rates, scalability issues, and reliance on manual effort.<n>Sparse Autoencoder offer a promising solution to this problem.
  arXiv  Detail & Related papers  (2025-05-15T14:59:17Z)
• CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
  We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
  arXiv  Detail & Related papers  (2025-05-14T13:37:07Z)
• Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositories [8.583591493627276]
  We introduce JitVul, a vulnerability detection benchmark linking each function to its vulnerability-introducing and fixing commits. We show that ReAct Agents, leveraging thought-action-observation and interprocedural context, perform better than LLMs in distinguishing vulnerable from benign code.
  arXiv  Detail & Related papers  (2025-03-05T15:22:24Z)
• A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition [71.61103962200666]
  Zero-shot named entity recognition (NER) aims to develop entity recognition systems from unannotated text corpora. Recent work has adapted large language models (LLMs) for zero-shot NER by crafting specialized prompt templates. We introduce the cooperative multi-agent system (CMAS), a novel framework for zero-shot NER.
  arXiv  Detail & Related papers  (2025-02-25T23:30:43Z)
• Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
  We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem. These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem. We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
  arXiv  Detail & Related papers  (2025-01-25T04:53:36Z)
• Combining GPT and Code-Based Similarity Checking for Effective Smart Contract Vulnerability Detection [0.0]
  We present SimilarGPT, a vulnerability identification tool for smart contract. The main concept of SimilarGPT is to measure the similarity between the code under inspection and the secure code from third-party libraries. We propose optimizing the detection sequence using topological ordering to enhance logical coherence and reduce false positives during detection.
  arXiv  Detail & Related papers  (2024-12-24T07:15:48Z)
• MASKDROID: Robust Android Malware Detection with Masked Graph Representations [56.09270390096083]
  We propose MASKDROID, a powerful detector with a strong discriminative ability to identify malware. We introduce a masking mechanism into the Graph Neural Network based framework, forcing MASKDROID to recover the whole input graph. This strategy enables the model to understand the malicious semantics and learn more stable representations, enhancing its robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2024-09-29T07:22:47Z)
• From Generalist to Specialist: Exploring CWE-Specific Vulnerability Detection [1.9249287163937974]
  Common Weaknession (CWE) represents a unique category of vulnerabilities with distinct characteristics, code semantics, and patterns. Treating all vulnerabilities as a single label with a binary classification approach may oversimplify the problem.
  arXiv  Detail & Related papers  (2024-08-05T09:12:39Z)
• AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models [95.09157454599605]
  Large Language Models (LLMs) are becoming increasingly powerful, but they still exhibit significant but subtle weaknesses. Traditional benchmarking approaches cannot thoroughly pinpoint specific model deficiencies. We introduce a unified framework, AutoDetect, to automatically expose weaknesses in LLMs across various tasks.
  arXiv  Detail & Related papers  (2024-06-24T15:16:45Z)
• Soley: Identification and Automated Detection of Logic Vulnerabilities in Ethereum Smart Contracts Using Large Language Models [1.081463830315253]
  We empirically investigate logic vulnerabilities in real-world smart contracts extracted from code changes on GitHub. We introduce Soley, an automated method for detecting logic vulnerabilities in smart contracts. We examine mitigation strategies employed by smart contract developers to address these vulnerabilities in real-world scenarios.
  arXiv  Detail & Related papers  (2024-06-24T00:15:18Z)
• Improving Smart Contract Security with Contrastive Learning-based Vulnerability Detection [8.121484960948303]
  We propose Contrastive Learning Enhanced Automated Recognition Approach for Smart Contract Vulnerabilities, named Clear. In particular, Clear employs a contrastive learning (CL) model to capture the fine-grained correlation information among contracts. We show that Clear achieves optimal performance over all baseline methods; (2) 9.73%-39.99% higher F1-score than existing deep learning methods.
  arXiv  Detail & Related papers  (2024-04-27T09:13:25Z)
• 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)
• Triplet Contrastive Learning for Unsupervised Vehicle Re-identification [55.445358749042384]
  Part feature learning is a critical technology for fine semantic understanding in vehicle re-identification. We propose a novel Triplet Contrastive Learning framework (TCL) which leverages cluster features to bridge the part features and global features.
  arXiv  Detail & Related papers  (2023-01-23T15:52:12Z)
• Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism [120.1998866178014]
  We present a flexible framework for continual object detection via pRotOtypical taSk corrElaTion guided gaTingAnism (ROSETTA) Concretely, a unified framework is shared by all tasks while task-aware gates are introduced to automatically select sub-models for specific tasks. Experiments on COCO-VOC, KITTI-Kitchen, class-incremental detection on VOC and sequential learning of four tasks show that ROSETTA yields state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-05-06T07:31:28Z)
• ADC: Adversarial attacks against object Detection that evade Context consistency checks [55.8459119462263]
  We show that even context consistency checks can be brittle to properly crafted adversarial examples. We propose an adaptive framework to generate examples that subvert such defenses. Our results suggest that how to robustly model context and check its consistency, is still an open problem.
  arXiv  Detail & Related papers  (2021-10-24T00:25:09Z)
• Multi-context Attention Fusion Neural Network for Software Vulnerability Identification [4.05739885420409]
  We propose a deep learning model that learns to detect some of the common categories of security vulnerabilities in source code efficiently. The model builds an accurate understanding of code semantics with a lot less learnable parameters. The proposed AI achieves 98.40% F1-score on specific CWEs from the benchmarked NIST SARD dataset.
  arXiv  Detail & Related papers  (2021-04-19T11:50:36Z)
• 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.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.