Attack-Centric by Design: A Program-Structure Taxonomy of Smart Contract Vulnerabilities

• URL: http://arxiv.org/abs/2511.09051v1
• Date: Thu, 13 Nov 2025 01:28:40 GMT
• Title: Attack-Centric by Design: A Program-Structure Taxonomy of Smart Contract Vulnerabilities
• Abstract summary: This paper introduces an attack-centric, program-structure taxonomy that unifies Solidity vulnerabilities into eight root-cause families.<n>Each family is illustrated through concise Solidity examples, exploit mechanics, and mitigations, and linked to the detection signals observable by static, dynamic, and learning-based tools.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Smart contracts concentrate high value assets and complex logic in small, immutable programs, where even minor bugs can cause major losses. Existing taxonomies and tools remain fragmented, organized around symptoms such as reentrancy rather than structural causes. This paper introduces an attack-centric, program-structure taxonomy that unifies Solidity vulnerabilities into eight root-cause families covering control flow, external calls, state integrity, arithmetic safety, environmental dependencies, access control, input validation, and cross-domain protocol assumptions. Each family is illustrated through concise Solidity examples, exploit mechanics, and mitigations, and linked to the detection signals observable by static, dynamic, and learning-based tools. We further cross-map legacy datasets (SmartBugs, SolidiFI) to this taxonomy to reveal label drift and coverage gaps. The taxonomy provides a consistent vocabulary and practical checklist that enable more interpretable detection, reproducible audits, and structured security education for both researchers and practitioners.

Related papers

• ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step [74.83535434786145]
  We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
  arXiv  Detail & Related papers  (2026-08-03T15:07:46Z)
• 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)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• SmartGraphical: A Human-in-the-Loop Framework for Detecting Smart Contract Logical Vulnerabilities via Pattern-Driven Static Analysis and Visual Abstraction [0.30586855806896046]
  This paper introduces SmartGraphical, a novel security framework specifically engineered to identify logical attack surfaces.<n>By synthesizing the context-dependent nature of logical bugs, SmartGraphical enables developers to interpret warnings within a visualized structural context.<n>Our findings indicate that this hybrid methodology significantly enhances the interpretability and detection rate of non-trivial logical security threats in smart contracts.
  arXiv  Detail & Related papers  (2026-03-09T16:35:17Z)
• CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing [51.56484100374058]
  CLAIRE is a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems.<n>It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features.<n>The proposed framework highlights the potential of integrating explainable AI with feature-aware regularization for robust fault detection.
  arXiv  Detail & Related papers  (2026-03-06T15:11:58Z)
• Towards Worst-Case Guarantees with Scale-Aware Interpretability [58.519943565092724]
  Neural networks organize information according to the hierarchical, multi-scale structure of natural data.<n>We propose a unifying research agenda -- emphscale-aware interpretability -- to develop formal machinery and interpretability tools.
  arXiv  Detail & Related papers  (2026-02-05T01:22:31Z)
• Hierarchical Evaluation of Software Design Capabilities of Large Language Models of Code [7.897548449569687]
  Large language models (LLMs) are increasingly adopted in software engineering domain, yet robustness of their grasp on core design concepts remains unclear.<n>We generate poorly designed software fragments under various levels of guidance.<n> Reasoning about coupling proves brittle; performance collapses in noisy, open-ended scenarios.<n> Reasoning-trace analysis confirms these failure modes, revealing textitcognitive shortcutting for coupling versus a more exhaustive (yet still failing) analysis for cohesion.
  arXiv  Detail & Related papers  (2025-11-25T23:50:00Z)
• 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)
• EvoSyn: Generalizable Evolutionary Data Synthesis for Verifiable Learning [63.03672166010434]
  We introduce an evolutionary, task-agnostic, strategy-guided, executably-checkable data synthesis framework.<n>It jointly synthesizes problems, diverse candidate solutions, and verification artifacts.<n>It iteratively discovers strategies via a consistency-based evaluator that enforces agreement between human-annotated and strategy-induced checks.
  arXiv  Detail & Related papers  (2025-10-20T11:56:35Z)
• From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning [71.41062111470414]
  Current object detectors excel at entity localization and classification, yet exhibit inherent limitations in event recognition capabilities.<n>We present a novel framework that expands the capability of standard object detectors beyond mere object recognition to complex event understanding.<n>Our key innovation lies in bridging the semantic gap between object detection and event understanding without requiring expensive task-specific training.
  arXiv  Detail & Related papers  (2025-02-09T10:30:54Z)
• Assessing Vulnerability in Smart Contracts: The Role of Code Complexity Metrics in Security Analysis [0.6906005491572401]
  This study investigates the use of code complexity metrics as indicators of vulnerable code in Solidity smart contracts.<n>By analyzing 21 complexity metrics, we explored their interrelation, association with vulnerability, discriminative power, and mean values in vulnerable versus neutral codes.
  arXiv  Detail & Related papers  (2024-11-26T11:36:32Z)
• Vulnerability-Hunter: An Adaptive Feature Perception Attention Network for Smart Contract Vulnerabilities [4.487191851300675]
  We propose AFPNet, a novel vulnerability detection model equipped with a feature perception module that has dynamic weights for comprehensive scanning of the entire smart contract code. We conduct an evaluation of AFPNet in the several large-scale datasets with vulnerability labels.
  arXiv  Detail & Related papers  (2024-07-07T10:13:41Z)
• Pre-deployment Analysis of Smart Contracts -- A Survey [0.27195102129095]
  We present a systematic review of the literature on smart contract vulnerabilities and methods. Specifically, we enumerate and classify smart contract vulnerabilities and methods by the properties they address. Several patterns about the strengths of different methods emerge through this classification process.
  arXiv  Detail & Related papers  (2023-01-15T12:36:56Z)
• Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation [57.92972327649165]
  This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program.
  arXiv  Detail & Related papers  (2021-09-07T21:24:36Z)
• Predicting Themes within Complex Unstructured Texts: A Case Study on Safeguarding Reports [66.39150945184683]
  We focus on the problem of automatically identifying the main themes in a safeguarding report using supervised classification approaches. Our results show the potential of deep learning models to simulate subject-expert behaviour even for complex tasks with limited labelled data.
  arXiv  Detail & Related papers  (2020-10-27T19:48:23Z)
• Structured Prediction with Partial Labelling through the Infimum Loss [85.4940853372503]
  The goal of weak supervision is to enable models to learn using only forms of labelling which are cheaper to collect. This is a type of incomplete annotation where, for each datapoint, supervision is cast as a set of labels containing the real one. This paper provides a unified framework based on structured prediction and on the concept of infimum loss to deal with partial labelling.
  arXiv  Detail & Related papers  (2020-03-02T13:59:41Z)

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.