USCSA: Evolution-Aware Security Analysis for Proxy-Based Upgradeable Smart Contracts

• URL: http://arxiv.org/abs/2512.08372v1
• Date: Tue, 09 Dec 2025 08:55:11 GMT
• Title: USCSA: Evolution-Aware Security Analysis for Proxy-Based Upgradeable Smart Contracts
• Abstract summary: We propose an Upgradable Smart Contract Security Analyzer, USCSA, which evaluates the risks associated with the upgrade process.<n>We collected and analyzed 3,546 cases of vulnerabilities in upgradable contracts, covering common vulnerability categories such as reentrancy, access control flaws, and integer overflow.<n>USCSA achieves an accuracy of 92.3%, recall of 89.7%, and F1-score of 91.0% in detecting upgrade-induced vulnerabilities.
• Score: 13.147747002201848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the case of upgrading smart contracts on blockchain systems, it is essential to consider the continuity of upgrade and subsequent maintenance. In practice, upgrade operations often introduce new vulnerabilities. To address this, we propose an Upgradable Smart Contract Security Analyzer, USCSA, which evaluates the risks associated with the upgrade process using the Abstract Syntax Tree (AST) differential analysis. We collected and analyzed 3,546 cases of vulnerabilities in upgradable contracts,covering common vulnerability categories such as reentrancy, access control flaws, and integer overflow. Experimental results show that USCSA achieves an accuracy of 92.3%, recall of 89.7%, and F1-score of 91.0% in detecting upgrade-induced vulnerabilities. In addition, the efficiency of mapping high-risk changes has achieved a 30% improvement over the conventional approach. As a result, USCSA provides a significant advantage to improve the security and integrity of upgradable smart contracts, providing a novel and efficient solution to secure audits on blockchain applications.

Related papers

• Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw [87.97230960702274]
  We present the first real-world safety evaluation of OpenClaw.<n>We introduce the CIK taxonomy, which unifies an agent's persistent state into three dimensions.<n>Our evaluations cover 12 attack scenarios on a live OpenClaw instance.
  arXiv  Detail & Related papers  (2026-04-06T15:27:05Z)
• SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization [50.71047638695205]
  Reasoning language models (RLMs) are increasingly used in programming.<n>Yet, even state-of-the-art RLMs frequently introduce critical security vulnerabilities in generated code.<n>We present SecPI, a fine-tuning pipeline that teaches RLMs to internalize structured security reasoning.
  arXiv  Detail & Related papers  (2026-04-04T04:29:11Z)
• Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents [57.49020237126194]
  Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation.<n>We propose Co-RedTeam, a security-aware multi-agent framework designed to mirror real-world red-teaming.<n>Co-RedTeam decomposes vulnerability analysis into coordinated discovery and exploitation stages, enabling agents to plan, execute, validate, and refine actions.
  arXiv  Detail & Related papers  (2026-02-02T14:38:45Z)
• 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)
• ParaVul: A Parallel Large Language Model and Retrieval-Augmented Framework for Smart Contract Vulnerability Detection [43.41293570032631]
  ParaVul is a retrieval-augmented framework to improve the reliability and accuracy of smart contract vulnerability detection.<n>We develop Sparse Low-Rank Adaptation (SLoRA) for LLM fine-tuning.<n>We construct a vulnerability contract dataset and develop a hybrid Retrieval-Augmented Generation (RAG) system.
  arXiv  Detail & Related papers  (2025-10-20T03:23:41Z)
• VulnRepairEval: An Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities [41.85494398578654]
  VulnRepairEval is an evaluation framework anchored in functional Proof-of-Concept exploits.<n>Our framework delivers a comprehensive, containerized evaluation pipeline that enables reproducible differential assessment.
  arXiv  Detail & Related papers  (2025-09-03T14:06:10Z)
• The Dark Side of Upgrades: Uncovering Security Risks in Smart Contract Upgrades [12.414536548730421]
  We build a dataset containing 83,085 upgraded contracts and 20,902 upgrade chains.<n>We develop a taxonomy of insecurities based on 37 real-world security incidents.<n>We survey public awareness of these risks and existing mitigations.
  arXiv  Detail & Related papers  (2025-08-04T07:43:43Z)
• LLM-BSCVM: An LLM-Based Blockchain Smart Contract Vulnerability Management Framework [5.88442044992561]
  LLM-BSCVM is a Large Language Model-based smart contract vulnerability management framework.<n>It provides end-to-end vulnerability detection, analysis, repair, and evaluation capabilities for Web 3.0 ecosystem.<n>It achieves a vulnerability detection accuracy and F1 score exceeding 91% on benchmark datasets.
  arXiv  Detail & Related papers  (2025-05-23T03:05:09Z)
• 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)
• The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach [56.4040698609393]
  Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies. We propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings.
  arXiv  Detail & Related papers  (2024-09-10T10:12:37Z)
• 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)
• Unveiling Hidden Links Between Unseen Security Entities [3.7138962865789353]
  VulnScopper is an innovative approach that utilizes multi-modal representation learning, combining Knowledge Graphs (KG) and Natural Processing (NLP) We evaluate VulnScopper on two major security datasets, the National Vulnerability Database (NVD) and the Red Hat CVE database. Our results show that VulnScopper outperforms existing methods, achieving up to 78% Hits@10 accuracy in linking CVEs to Common Vulnerabilities and Exposures (CWEs), and Common Platform Languageions (CPEs)
  arXiv  Detail & Related papers  (2024-03-04T13:14:39Z)
• Certifiers Make Neural Networks Vulnerable to Availability Attacks [70.69104148250614]
  We show for the first time that fallback strategies can be deliberately triggered by an adversary. In addition to naturally occurring abstains for some inputs and perturbations, the adversary can use training-time attacks to deliberately trigger the fallback. We design two novel availability attacks, which show the practical relevance of these threats.
  arXiv  Detail & Related papers  (2021-08-25T15:49:10Z)
• 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.