ENS 实验室此前推出基于 Web3 的互联网域名系统，其使用的后缀就是 ETH 也就是以太坊，这类域名可以在 […]

本篇作为系列结尾，让我们补充一下其他 web3/加密货币圈的知识点。零知识证明，Rollup，layer2，侧链，跨链桥，DeFi，闪电贷

Introduction Web3 security has been a popular research field of information security in recent years. In this report, we collect, categorize, and briefly summarize 16 Web3-security-related papers from ACM CCS, NDSS, IEEE S&P, and USENIX Security conferences in 2023 and 2024, to identify common patterns and trends in this field. Note that I omit theoretical studies, which are out of my research scope. The papers are categorized into four topics: vulnerability detection, novel attack & defense, optimization and privacy issues as shown in the following table. These topics provide a systemic view of Web3 security. Topic Research Vulnerability Detection Crush [1], VetEOS [3], SmartInv [4],Nyx [5], FuzzDelSol [6], Phoenix [8],ESCORT [10], LOKI [11], BlockScope [12],Panda [16] Novel Attacks & Defenses Ma et al. [2], TxPhishScope [7], Sting [14],Ape [15] Optimization POSE [9] Privacy Issues Torres et al. [13] Topic 1: Vulnerability Detection 10 out of 16 collected studies aim to detect bugs or vulnerabilities in Web3 ecosystem. VetEOS [3] is a static analysis tool for the Groundhog Day vulnerabilities in EOSIO contracts. In a Groundhog Day attack, attackers leverage the rollback issue in EOSIO contracts to persistently execute identical contract code with varying inputs. By using the information exposed in prior executions, attackers can accumulate insights about the target contract and figure out a reliable method to generate unauthorized profits. The authors formally define this vulnerability as a control and data dependency problem. The insight is that the vulnerability lies in intrinsic data and control dependencies among key contract constructs, such as user inputs, global states, database tables, API return values and inlined action calls. VetEOS performs context-sensitive flow-sensitive interprocedural dataflow analysis on EOSIO WebAssembly bytecode. Ultimately, VetEOS detected 735 new vulnerabilities in the wild. Crush [1] is an analysis system to detect and investigate a type of cross-contract vulnerability known as a storage collision at scale, which is resulted from the different understandings of the types/semantics of two contracts’ shared storage. Storage collision vulnerabilities may cause unexpected behavior such as denial of service (frozen funds), privilege escalation, and theft of financial assets. The approach is divided into three stages: component discovery, collision discovery, and vulnerability discovery, leveraging program analysis techniques. Crush performs a large-scale analysis of 14,237,696 smart contracts deployed on the Ethereum blockchain, identifies 14,891 potentially vulnerable contracts, and automatically synthesizes an end-to-end exploit for 956 of them. SmartInv [4] is an accurate and fast smart contract invariant inference framework, which is used to detect machine un-auditable bugs. Such bugs cannot be reliably detected using existing automated tools that rely on pre-defined bug patterns. The insight is that the expected behavior of smart contracts, as specified by invariants, relies on understanding and reasoning across multimodal information, such as source code and natural language. The authors propose a new finetuning and prompting strategy to foundation models, Tier of Thought (ToT), to reason across multiple modalities of smart contracts and to generate invariants. FuzzDelSol [6] is the first binary-only coverage-guided fuzzing architecture for Solana smart contracts. The authors extract low-level program and state information to develop a diverse set of bug oracles covering all major bug classes in Solana. LOKI [11] is a blockchain consensus protocol fuzzing framework, which detects consensus memory-related and logic bugs. LOKI fetches consensus states in realtime by masquerading as a node. First, LOKI dynamically builds a state model that records the state transition of each node. After that, LOKI adaptively generates the input targets, types, and contents according to the state model. With a bug analyzer, LOKI detects the consensus protocol implementation bugs with well-defined oracles. Nyx [5] is a static analyzer for detection of exploitable front-running vulnerabilities in smart contracts, which features a Datalog-based preprocessing procedure that efficiently and soundly prunes a large part of the search space, followed by a symbolic validation engine that precisely locates vulnerabilities with an SMT solver. Phoenix [8] is a system for detecting and locating resilience issues in blockchain systems by context-sensitive chaos. The authors firstly identify two typical types of resilience issues in blockchain systems: node unrecoverable and data unrecoverable. Then, they design three context-sensitive chaos strategies tailored to the blockchain feature. They also create a coordinator to effectively trigger resilience issues by scheduling these strategies. ESCORT [10] is a deep learning-based vulnerability detection method that uses a common feature extractor to learn generic bytecode semantics of smart contracts and separate branches to learn the features of each vulnerability type. As a multi-label classifier, ESCORT can detect multiple vulnerabilities of the contract at once. ESCORT is also the first deep learning-based framework utilizing transfer learning on new vunerability types with minimal model modification and retraining overhead. Panda [16] is the first extensible static analysis (symbolic execution and constraint solving) framework that automatically nine types of generic vulnerabilities in Algorand smart contracts. The authors also construct the first benchmark dataset to evaluate Panda. BlockScope [12] is a patch-based code clone detection tool that can detect multiple types of cloned vulnerabilities given an input of existing Bitcoin/Ethereum security patches, leveraging similarity-based code match and proposing a new way of calculating code similarity to cover all the syntax-wide variant clones. To make BlockScope language-agnostic, there is no program analysis-alike preprocessing. Topic 2: Novel Attacks & Defenses In this section, we select four studies introducing novel attacks or defense mechanisms in Web3 security. Ape [15] is a generalized imitation attack methodology. Imitation attack is one type of front-running attacks, where the attackers only copy-paste and front-run a pending profitable transaction without understanding its logic. Leveraging dynaic program analysis techniques (dynamic taint analysis, program synthesis, and advanced instrumentations), Ape supports the automatic synthesis of adversarial smart contracts. Besides attack, blockchain imitation can also act as an intrusion prevention system by mimicking an attack, appropriating the vulnerable funds, and returning them to its victim. Ma et al. [2] presents the first comprehensive security analysis of smart contract verification services in the wild. By diving into the detailed workflow of existing verifiers, the authors summarized the key security properties that should be met, and observed eight types of vulnerabilities that can break the verification. Sting [14] is a novel runtime defense mechanism against smart contract exploits. The key idea is to instantly synthesize counterattack smart contracts from attacking transactions and leverage the power of Maximal Extractable Value (MEV) to front run attackers. TxPhishScope [7] is the largest TxPhish (transaction-based phishing scam) website detection system, which detects TxPhish websites and extracts phishing accounts automatically by dynamically visiting the suspicious websites, triggering transactions, and simulating results. Topic 3: Optimization POSE [9] is a practical off-chain protocol for smart contracts that addresses shortcomings of existing off-chain solutions, such as costly blockchain interactions, lack of data privacy, huge capital costs from locked collateral, and supporting only a restricted set of applications. POSE leverages a pool of Trusted Execution Environments (TEEs) to execute the computation efficiently and to swiftly recover from accidental or malicious failures. Topic 4: Privacy Issues Torres et al. [13] analyzes the privacy implications that Web3 technologies have on users. The authors build a framework that measures exposure of wallet information. The evaluation results show that 1,325 from the top 100K websites run scripts to probe whether users have wallets installed in their browser, and there are more than 2000 leaks across 211 applications and over 300 leaks across 13 wallets, which leak the user’s unique wallet address to third-parties. The approach is tracking, intercepting and analyzing the network traffic. References NDSS'24 [Crush] Ruaro, Nicola, et al. “Not your Type! Detecting Storage Collision Vulnerabilities in Ethereum Smart Contracts.” Ma, Pengxiang, et al. “Abusing the Ethereum Smart Contract Verification Services for Fun and Profit.” arXiv preprint arXiv:2307.00549 (2023). [vetEOS] Li, Levi Taiji, et al. “VETEOS: Statically Vetting EOSIO Contracts for the “Groundhog Day” Vulnerabilities.” SP'24 [SmartInv] Wang, Sally Junsong, Kexin Pei, and Junfeng Yang. “SMARTINV: Multimodal Learning for Smart Contract Invariant Inference.” 2024 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 2024. [Nyx] Zhang, Wuqi, et al. “Nyx: Detecting Exploitable Front-Running Vulnerabilities in Smart Contracts.” 2024 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 2024. CCS'23 [FuzzDelSol] Smolka, Sven, et al. “Fuzz on the Beach: Fuzzing Solana Smart Contracts.” Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. 2023. [TxPhishScope] He, Bowen, et al. “TxPhishScope: Towards Detecting and Understanding Transaction-based Phishing on Ethereum.” Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. 2023. [Phoenix] Ma, Fuchen, et al. “Phoenix: Detect and locate resilience issues in blockchain via context-sensitive chaos.” Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. 2023. NDSS'23 [POSE] Frassetto, Tommaso, et al. “POSE: Practical off-chain smart contract execution.” arXiv preprint arXiv:2210.07110 (2022). [ESCORT] Sendner, Christoph, et al. “Smarter Contracts: Detecting Vulnerabilities in Smart Contracts with Deep Transfer Learning.” NDSS. 2023. [LOKI] Ma, Fuchen, et al. “LOKI: State-Aware Fuzzing Framework for the Implementation of Blockchain Consensus Protocols.” NDSS. 2023. [BlockScope] Yi, Xiao, et al. “BlockScope: Detecting and Investigating Propagated Vulnerabilities in Forked Blockchain Projects.” arXiv preprint arXiv:2208.00205 (2022). Security'23 Torres, Christof Ferreira, Fiona Willi, and Shweta Shinde. “Is your wallet snitching on you? an analysis on the privacy implications of web3.” 32nd USENIX Security Symposium (USENIX Security 23). 2023. [Sting] Zhang, Zhuo, et al. “Your exploit is mine: Instantly synthesizing counterattack smart contract.” 32nd USENIX Security Symposium (USENIX Security 23). 2023. [Ape] Qin, Kaihua, et al. “The blockchain imitation game.” 32nd USENIX Security Symposium (USENIX Security 23). 2023. [Panda] Sun, Zhiyuan, Xiapu Luo, and Yinqian Zhang. “Panda: Security analysis of algorand smart contracts.” 32nd USENIX Security Symposium (USENIX Security 23). 2023.

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该研究提出了一种全新的质量服务（Quality-of-Service）模型，利用深度强化学习方法来找到多用户社交中接近最优的信道资源分配。全面的实验证明采用该质量服务模型可以提升整体社交体验。

GPTs GPTs是什么 GPTs 是指"Generative Pre-Training"的缩写，意思是生成式的预训练。它是一种人工智能技术，由OpenAI开发，用于生成各种类型的文本，如小说、诗歌、新闻和对话等。 GPTs 是 ChatGPT 的定制版本，允许用户根据特定用途或业务场景构建定制化模型。 GPTs可以干什么 GPTs 可以用于许多不同的任务和应用场景。以下是一些示例： 写作助手：GPTs可以提供创意和灵感，帮助您写作文章、博客、社交媒体帖子等。 语言翻译：GPTs可以用于自动翻译文本，帮助人们在不同语言之间进行沟通和理解。 问题回答：GPTs可以回答用户提出的问题，提供相关的信息和解决方案。 个性化助手：GPTs可以根据用户的需求和喜好提供个性化的建议和推荐，如电影推荐、旅行规划等。 教育和培训：GPTs可以用于教育和培训领域，帮助学生学习和理解各种学科知识。 创意设计：GPTs可以用于生成创意设计，如艺术作品、音乐、图像等 GPTs Prompts 拥有了 Prompts，你就拥有了撬动世界的杠杆。 网站名称 网站介绍 CrackGPTs👍 可提取任意 GPTs 的 Prompts GPTs: leaked prompts of GPTs 收集 Prompts 的仓库 chatgpt_system_prompt 收集 Prompts 的仓库 BlackFriday-GPTs-Prompts 收集 Prompts 的仓库 GPTs应用 下面网站如果正常没法访问，那就只能富强使用。 网站名称 网站介绍 界面语言 GPTStore.AI GPTs 合集、GPT 插件，可以根据各种分类筛选，以及作者，还可以提交；已收录 21933 个 GPTs 英语 GPTs CAT 根据各种分类筛选，已收录 6658 个 GPTs 中文 GPT-Store.Pro 已收录 7000+ 个 GPTs 英语 Awesome-GPTs 致力于收集有趣和创新的GPT模型，包含中文、写作、教育、生产力、编程、生活方式等 英语、中文 GPTsHunter 已收录 30584 个 GPTs 英语 CUSTOM GPTs 已收录 5000+ 个 GPTs 英语 GPTHub 已收录 716 个 GPTs 英语 GPTs Works 已收录 21894 个 GPTs（数据来源：GPTsHunter） 英语 gpts store 已收录 2000+ 个 GPTs 英语 AllGPTs 已收录 100+ 个 GPTs 英语 GPT Store 已收录 15968 个 GPTs 英语

GPTs GPTs是什么 GPTs 是指"Generative Pre-Training"的缩写，意思是生成式的预训练。它是一种人工智能技术，由OpenAI开发，用于生成各种类型的文本，如小说、诗歌、新闻和对话等。 GPTs 是 ChatGPT 的定制版本，允许用户根据特定用途或业务场景构建定制化模型。 GPTs可以干什么 GPTs 可以用于许多不同的任务和应用场景。以下是一些示例： 写作助手：GPTs可以提供创意和灵感，帮助您写作文章、博客、社交媒体帖子等。 语言翻译：GPTs可以用于自动翻译文本，帮助人们在不同语言之间进行沟通和理解。 问题回答：GPTs可以回答用户提出的问题，提供相关的信息和解决方案。 个性化助手：GPTs可以根据用户的需求和喜好提供个性化的建议和推荐，如电影推荐、旅行规划等。 教育和培训：GPTs可以用于教育和培训领域，帮助学生学习和理解各种学科知识。 创意设计：GPTs可以用于生成创意设计，如艺术作品、音乐、图像等 GPTs Prompts 拥有了 Prompts，你就拥有了撬动世界的杠杆。 网站名称 网站介绍 CrackGPTs👍 可提取任意 GPTs 的 Prompts GPTs: leaked prompts of GPTs 收集 Prompts 的仓库 chatgpt_system_prompt 收集 Prompts 的仓库 BlackFriday-GPTs-Prompts 收集 Prompts 的仓库 GPTs应用 下面网站如果正常没法访问，那就只能富强使用。 网站名称 网站介绍 界面语言 GPTStore.AI GPTs 合集、GPT 插件，可以根据各种分类筛选，以及作者，还可以提交；已收录 21933 个 GPTs 英语 GPTs CAT 根据各种分类筛选，已收录 6658 个 GPTs 中文 GPT-Store.Pro 已收录 7000+ 个 GPTs 英语 Awesome-GPTs 致力于收集有趣和创新的GPT模型，包含中文、写作、教育、生产力、编程、生活方式等 英语、中文 GPTsHunter 已收录 30584 个 GPTs 英语 CUSTOM GPTs 已收录 5000+ 个 GPTs 英语 GPTHub 已收录 716 个 GPTs 英语 GPTs Works 已收录 21894 个 GPTs（数据来源：GPTsHunter） 英语 gpts store 已收录 2000+ 个 GPTs 英语 AllGPTs 已收录 100+ 个 GPTs 英语 GPT Store 已收录 15968 个 GPTs 英语

GPTs是什么 GPTs 是指"Generative Pre-Training"的缩写，意思是生成式的预训练。它是一种人工智能技术，由OpenAI开发，用于生成各种类型的文本，如小说、诗歌、新闻和对话等。 GPTs 是 ChatGPT 的定制版本，允许用户根据特定用途或业务场景构建定制化模型。 GPTs可以干什么 GPTs 可以用于许多不同的任务和应用场景。以下是一些示例： 写作助手：GPTs可以提供创意和灵感，帮助您写作文章、博客、社交媒体帖子等。 语言翻译：GPTs可以用于自动翻译文本，帮助人们在不同语言之间进行沟通和理解。 问题回答：GPTs可以回答用户提出的问题，提供相关的信息和解决方案。 个性化助手：GPTs可以根据用户的需求和喜好提供个性化的建议和推荐，如电影推荐、旅行规划等。 教育和培训：GPTs可以用于教育和培训领域，帮助学生学习和理解各种学科知识。 创意设计：GPTs可以用于生成创意设计，如艺术作品、音乐、图像等 GPTs Prompts 拥有了 Prompts，你就拥有了撬动世界的杠杆。 网站名称 网站介绍 CrackGPTs👍 可提取任意 GPTs 的 Prompts GPTs: leaked prompts of GPTs 收集 Prompts 的仓库 chatgpt_system_prompt 收集 Prompts 的仓库 BlackFriday-GPTs-Prompts 收集 Prompts 的仓库 GPTs应用 下面网站如果正常没法访问，那就只能富强使用。 网站名称 网站介绍 界面语言 GPTStore.AI GPTs 合集、GPT 插件，可以根据各种分类筛选，以及作者，还可以提交；已收录 21933 个 GPTs 英语 GPTs CAT 根据各种分类筛选，已收录 6658 个 GPTs 中文 GPT-Store.Pro 已收录 7000+ 个 GPTs 英语 Awesome-GPTs 致力于收集有趣和创新的GPT模型，包含中文、写作、教育、生产力、编程、生活方式等 英语、中文 GPTsHunter 已收录 30584 个 GPTs 英语 CUSTOM GPTs 已收录 5000+ 个 GPTs 英语 GPTHub 已收录 716 个 GPTs 英语 GPTs Works 已收录 21894 个 GPTs（数据来源：GPTsHunter） 英语 gpts store 已收录 2000+ 个 GPTs 英语 AllGPTs 已收录 100+ 个 GPTs 英语 GPT Store 已收录 15968 个 GPTs 英语

前两年由币圈带起的 Web3 概念非常火，号称要替代当前传统的 Web 2.0，彼时知名内容分发网络和防护提供 […]