Closed RikkaLzw closed 9 months ago
VegeBun-csj
commented
1 year ago 理解PDF的时候问了没几个问题就出现：Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.) 即使释放了部分缓存，依然无法完整回答之前的问题，只能重新上传阅读论文，重新问，这样很浪费token，有什么好的解决办法吗？
对的，一段时间就没有上下文了，这个比较蛋疼，不知道程序里面有没有相关的配置？
binary-husky
commented
1 year ago 请提供测试样本？
RikkaLzw
commented
1 year ago 请提供测试样本？
就基本阅读英文文献多问几个问题就会出现这个。只要一出现上述问题，就完全没办法继续询问了，下面是log里面记录的。
INFO:root:[raw_input] 为什么能f大于0.5时仍然能安全？能模拟一下这个过程吗？
INFO:root:[response] 当f大于0.5时，传统的拜占庭容错机制就无法保证系统的安全性和正确性。但是BCUBE使用了新的拜占庭广播协议OVERLAYBB和PoS机制来保证系统的安全性和正确性。具体来说，在BCUBE中，OVERLAYBB协议使用多个分段的Hash链来减少节点之间的通信复杂度，并使用了预留和分批确认
INFO:root:[raw_input] 为什么能f大于0.5时仍然能安全？能模拟一下这个过程吗？
INFO:root:[response] 当f大于0.5时，一般的拜占庭容错机制会失效，因此传统的拜占庭广播无法保证系统的正确性和安全性。但是，本文提出的拜占庭广播协议OVERLAYBB通过使用高斯消元法和基于群论的方法来减少通信复杂度，并具有一定的容错能力，可以在f大于0.5时依请提供测试样本？
可以持续问>10个文本较长的大文本，就会出现
RikkaLzw
commented
1 year ago 请提供测试样本？
也就问了他几个问题。
To create a public link, setshare=Trueinlaunch()`.
begin analysis on: private_upload/2023-05-18-10-06-39\Qin 等 - 2022 - Quantifying Blockchain Extractable Value How dark.pdf
gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
This section discusses the emergence of BEV (blockchain extractable value) which refers to the monetary value being extracted from decentralized finance (DeFi) smart contracts by opportunistic traders. These traders use various market manipulation techniques, including high frequency attacks, pump and dump schemes, and wash trading, to extract value from transactions that have not yet been added to the blockchain. The section presents the first algorithm for generalized transaction replay, which could result in a significant increase in BEV and endangers the blockchain's security. The authors also examine the emerging BEV relay systems, which allow miners to accept BEV transactions from centralized relay servers instead of the peer-to-peer network, leading to the aggravation of consensus layer attacks and endangering the blockchain's security. The authors estimate that over 32 months, BEV yielded 540.54M USD in profit, divided among 11,289 addresses, capturing 49,691 cryptocurrencies and 60,830 on-chain markets. Additionally, the section offers insights into permissionless blockchains, smart contracts, and DeFi, as well as how AMM (Automated Market Maker) exchanges operate on the blockchain. Overall, the section highlights the importance of understanding the risks of BEV and its impact on blockchain security. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words: Lending Syst .......... This section discusses DeFi lending systems that require over-collateralization, with borrowers locking 150% of their desired loan value as collateral. Liquidators can purchase this collateral at a discount if the collateral value decreases and the collateralization ratio falls below 150%. The section then outlines the security and threat model for permissionless blockchain systems and examines the relationship between blockchain transaction ordering and blockchain extractable value (BEV). The section identifies two fundamental properties of blockchain systems - atomicity and determinacy - which make transaction order manipulation crucial to the value extraction game. The section also provides a detailed transaction ordering taxonomy, which includes destructive front-running, tolerating front-running, back-running, and clogging. The paper then presents a study that investigated revenue strategies in DeFi over 32 months, focusing on sandwich attacks, liquidations, and arbitrage trading. The section outlines the heuristics used to identify potentially successful sandwich attacks from AMM trades. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words: • Heuristic .......... This section provides an analysis of three common revenue-generating strategies used by adversaries in DeFi: sandwich attacks, fixed spread liquidations, and arbitrage trading. The authors identify heuristics for detecting profitable sandwich attacks and estimate that 750,529 sandwich attacks have been made with a total profit of 174.34M USD. The authors also analyze fixed spread liquidations on Aave, Compound, and dYdX, finding 31,057 liquidations yielding a collective profit of 89.18M USD. They identify front-running as the dominating strategy, but also note that internal back-running transactions tend to pay higher gas prices. Finally, the authors describe arbitrage trading and identify two strategies: block state arbitrage and network state arbitrage. They highlight that arbitrage trading is typically seen as benign and helps promote market efficiency. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words: 1) Heuristic .......... The section discusses a methodology that uses heuristics to detect profitable arbitrage trades on Uniswap V1/V2/V3, Sushiswap, Curve, Swerve, 1inch, and Bancor between December 2018 and August 2021. The heuristics aim to identify arbitrage trades involving more than one platform that create a loop, and thus a cyclical route between those platforms resulting in a profitable outcome. The results show that over 1 million arbitrage trades were performed, with a total profit of 277.02M USD. The authors also identify that 9.6% of these transactions are privately relayed to miners, representing 82.75M USD of extracted value, indicating the exploitation and manipulation of the system. They also find that most traders prefer simpler strategies, involving only two or three markets, rather than executing strategies with more than four markets. The section also discusses the concept of clogging, where adversaries issue multiple transactions to increase the cost of writing to the blockchain. Finally, the section presents an application-agnostic method for adversaries to extract value by copying and replaying the execution logic of an unconfirmed victim transaction, resulting in a profit transfer to the adversary-controlled account, thereby highlighting the need for continued development of security measures for decentralized finance. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words: T = {sender, ..........
Traceback (most recent call last):
File ".\crazy_functions\crazy_utils.py", line 78, in _req_gpt
result = predict_no_ui_long_connection(
File ".\request_llm\bridge_all.py", line 230, in predict_no_ui_long_connection
return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
File ".\request_llm\bridge_chatgpt.py", line 87, in predict_no_ui_long_connection
raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
RuntimeError: OpenAI拒绝了请求：{ "error": { "message": "Rate limit reached for default-gpt-3.5-turbo in organization org-OpD1wE3r5RSxw7spuECBSzXy on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.", "type": "requests", "param": null, "code": null }}gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words: T = {sender, .......... The section describes a replay attack on Ethereum transactions where an adversary duplicates the fields of a potential victim transaction, but replaces the sender address with an adversarial address, executing the replay transaction and front-running the victim. The authors apply this algorithm to 883,023,232 Ethereum transactions from December 2018 to August 2021, finding 188,365 profitable transactions that could have been replayed, resulting in an estimated profit of 57,037.32 ETH (35.37M USD) in total. 1,213 ERC20 tokens contribute revenue of 179,843.52 ETH in 128,200 transactions. The authors observe a general uptrend in the number of replayable transactions since January 2020 and find 19,825 replayable transactions in June 2021 alone. The authors detect 83.2% of replay transactions do not require upfront ETH except for transaction fees, and 6,685 replayable transactions have zero gas price. The authors perform a real-time replay attack investigation from July to August 2021 and find 166 replayable transactions via P2P network connectivity, compared to the 576 replayable transactions in the on-chain data within the same time-frame. The authors recommend future work to use this metric as a success indicator of adversarial node connectivity in the P2P network. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words: The on-chain .......... This section focuses on understanding replayable transactions in Ethereum and proposing various methods to protect against replay attacks. The authors detect 188,365 replayable transactions, including 89 with zero gas price. They cross-compare these transactions with liquidations and arbitrage, finding that replay transactions capture a different set of profit-generating transactions. The authors propose two simple methods of protection: authentication and beneficiary provision. However, these methods are not sufficient against more sophisticated replay algorithms that can extract emitted events and reconstruct the application layer logic. The authors recommend more robust protection mechanisms that require no entity besides the issuer to inspect the transaction and the miner to validate but not view the transaction. They propose using trusted hardware modules such as Intel SGX or fair ordering techniques to grant the original transaction issuer priority access to the blockchain. The authors then introduce the concept of BEV relayers, centralized entities that mediate between traders seeking to extract value and miners. They formalize an abstract BEV auction game capturing both the P2P and the centralized BEV relayer model and analyze how the introduction of BEV relayers impacts the P2P network and the consensus layer. They propose a relay auction as a first-price sealed-bid auction, and claim that it always encourages players to participate, leading to more intense competition than in the P2P auction. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words: Increasing b .......... This section discusses the impact of first-price relay auctions on revenue concentration and incentivizing miners to perform attacks on the consensus layer. The authors propose the concept of protogenetic opportunities, which are the transactions that a player would broadcast to the P2P network when there is no BEV relayer. The impact of BEV relayers on the P2P network is quantified by measuring how many protogenetic BEV transactions could have been prevented from propagating in the P2P network due to the introduction of these relayers. The results show that BEV relay mechanisms do not substantially reduce the P2P network overhead despite the intermediary introduced by the relayers. The authors also discuss privately relayed transactions and how BEV relayers aggravate consensus layer attacks. They also draw attention to the BEV forking threshold and the danger of drastic forking competition among BEV aware miners. The section concludes by highlighting the risks of BEV and how it can cause congestion on the P2P network layer, which negatively affects consensus security. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words: BEV relayer ..........
Traceback (most recent call last):
File ".\crazy_functions\crazy_utils.py", line 78, in _req_gpt
result = predict_no_ui_long_connection(
File ".\request_llm\bridge_all.py", line 230, in predict_no_ui_long_connection
return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
File ".\request_llm\bridge_chatgpt.py", line 87, in predict_no_ui_long_connection
raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
RuntimeError: OpenAI拒绝了请求：{ "error": { "message": "Rate limit reached for default-gpt-3.5-turbo in organization org-OpD1wE3r5RSxw7spuECBSzXy on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.", "type": "requests", "param": null, "code": null }}gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words: BEV relayer .......... This section highlights the potential threats posed by BEV relayers and the impact of DeFi on BEV. The authors suggest that BEV should never be considered a desirable feature and rather a design flaw since it triggers transaction overhead and weakens the incentive mechanisms of block rewards and transaction fees. They propose several avenues towards mitigating BEV, including fair ordering and application-specific BEV mitigation. The section discusses the sandwich attacks on AMM exchanges and clogging attacks using bidding bots. Through empirical data, the authors provide insights into the practices of obscure and predatory traders in blockchains and warn about the risks of BEV relayers endangering blockchain security. The section concludes by offering promising ideas for improving DeFi and blockchain security while minimizing the impact of BEV. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words: Case Studies .......... This section presents several case studies related to DeFi and blockchain security. The first case study discusses clogging events in the Ethereum network, where the authors found instances of incentivized clogging, attacks on gambling protocols, and mass USDT transfers without any apparent reason. The second case study explores replayable transactions that can be exploited for profit and presents a solution to protect against transaction replay attacks. The third case study focuses on identifying non-broadcast transactions in the Ethereum network and provides empirical data showing that 1.64% of transactions are privately relayed. The authors further investigate private transactions used by mining pools and identify instances of private value-extracting transactions and replayable transactions related to 1inch exchange trades. Overall, these case studies provide insights into the various types of attacks and behaviors in the DeFi and blockchain ecosystem and highlight the importance of effective security measures to mitigate these risks. gpt-3.5-turbo : 5 : 简要介绍这篇论文。 .......... gpt-3.5-turbo : 6 : 本文旨在研究区块链中的价值提取行为及其对区块链共识安全的影响。局势如何研究的？请分点作答 .......... gpt-3.5-turbo : 7 : 研究DeFi贷款系统，探讨抵押率达到150%的超额抵押的机制及其关联的账户清算系统。是如何进行BEV的？ .......... gpt-3.5-turbo : 8 : 介绍一下本文的安全和威胁模型 .......... gpt-3.5-turbo : 9 : 本文的安全和威胁模型是什么？ .......... gpt-3.5-turbo : 10 : 将全文内容总结生成至xmind的思维导图的形式 .......... Traceback (most recent call last): File "C:\Users\33519\Desktop\gpt_academic\request_llm\bridge_chatgpt.py", line 181, in predict if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0): File "C:\Users\33519\anaconda3\envs\ChatPaper-main\lib\json__init__.py", line 346, in loads return _default_decoder.decode(s) File "C:\Users\33519\anaconda3\envs\ChatPaper-main\lib\json\decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "C:\Users\33519\anaconda3\envs\ChatPaper-main\lib\json\decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) `
VegeBun-csj
commented
1 year ago 我感觉目前这个还不能做到类似gpt官网那个对话的模式，当前的上下文很容易被清除，不能持续很长时间进行对话，有什么优化吗
iblctw
commented
1 year ago 请提供测试样本？
可以持续问>10个文本较长的大文本，就会出现
终端记录： gpt-3.5-turbo : 5 : 介绍一下文中提到的HTCPN模型 .......... gpt-3.5-turbo : 6 : 相对与TCPN模型 HTCPN模型有哪些改进 .......... gpt-3.5-turbo : 7 : 介绍一下传统的TCPN模型 .......... gpt-3.5-turbo : 8 : 为什么要选择改进这个模型应用在地铁站火灾的紧急响应中 .......... gpt-3.5-turbo : 9 : 介绍一下petri网模型 .......... gpt-3.5-turbo : 10 : 可以举例来介绍一些这个模型么 .......... gpt-3.5-turbo : 11 : 传统的TCPN模型应该如何应用在火灾场景中呢 .......... Traceback (most recent call last): File "d:\chatgbt学术优化\gpt_academic-master\request_llm\bridge_chatgpt.py", line 189, in predict if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json__init.py", line 346, in loads return _default_decoder.decode(s) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) gpt-3.5-turbo : 12 : 传统的TCPN模型应该如何应用在火灾场景中呢 .......... gpt-3.5-turbo : 13 : 继续 .......... Traceback (most recent call last): File "d:\chatgbt学术优化\gpt_academic-master\request_llm\bridge_chatgpt.py", line 189, in predict if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json__init.py", line 346, in loads return _default_decoder.decode(s) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) gpt-3.5-turbo : 14 : 继续 .......... gpt-3.5-turbo : 15 : 文章中的HTCPN是怎么应用在地铁火灾场景中的 .......... gpt-3.5-turbo : 16 : 继续 .......... Traceback (most recent call last): File "d:\chatgbt学术优化\gpt_academic-master\request_llm\bridge_chatgpt.py", line 189, in predict if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\init.py", line 346, in loads return _default_decoder.decode(s) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) gpt-3.5-turbo : 17 : 继续 .......... gpt-3.5-turbo : 18 : 介绍一些他是怎么实现的 .......... gpt-3.5-turbo : 19 : 继续 .......... Traceback (most recent call last): File "d:\chatgbt学术优化\gpt_academic-master\request_llm\bridge_chatgpt.py", line 189, in predict if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\init__.py", line 346, in loads return _default_decoder.decode(s) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) gpt-3.5-turbo : 20 : 继续 .......... gpt-3.5-turbo : 21 : 介绍一下 Skyline operator算法 .......... if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\init__.py", line 346, in loads return _default_decoder.decode(s) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program1\Anaconda3\envs\chatgpt\Lib\json\decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
chatgpt学术优化页面上会出现： Json异常 “error”: { “message”: “This model’s maximum context length is 4097 tokens. However, your messages resulted in 4102 tokens. Please reduce the length of the messages.”, “type”: “invalid_request_error”, “param”: “messages”, “code”: “context_length_exceeded” }}
binary-husky
commented
1 year ago 我试试改进一下
frr717
commented
1 year ago 关键是, 如果说到一半 停了 我说"继续", 他就重新开始答, 但不是接着之前的回答了. 有时候也会出现上面所说的这个缓存问题 但是自己很想要上一条未回答完整的回答的完整回答
kane7878
commented
1 year ago 请提供测试样本？
也就问了他几个问题。
To create a public link, setshare=Trueinlaunch()`.begin analysis on: private_upload/2023-05-18-10-06-39\Qin 等 - 2022 - Quantifying Blockchain Extractable Value How dark.pdf
gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
Arthur Ger ..........
This section discusses the emergence of BEV (blockchain extractable value) which refers to the monetary value being extracted from decentralized finance (DeFi) smart contracts by opportunistic traders. These traders use various market manipulation techniques, including high frequency attacks, pump and dump schemes, and wash trading, to extract value from transactions that have not yet been added to the blockchain. The section presents the first algorithm for generalized transaction replay, which could result in a significant increase in BEV and endangers the blockchain's security. The authors also examine the emerging BEV relay systems, which allow miners to accept BEV transactions from centralized relay servers instead of the peer-to-peer network, leading to the aggravation of consensus layer attacks and endangering the blockchain's security. The authors estimate that over 32 months, BEV yielded 540.54M USD in profit, divided among 11,289 addresses, capturing 49,691 cryptocurrencies and 60,830 on-chain markets. Additionally, the section offers insights into permissionless blockchains, smart contracts, and DeFi, as well as how AMM (Automated Market Maker) exchanges operate on the blockchain. Overall, the section highlights the importance of understanding the risks of BEV and its impact on blockchain security. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
Lending Syst ..........
This section discusses DeFi lending systems that require over-collateralization, with borrowers locking 150% of their desired loan value as collateral. Liquidators can purchase this collateral at a discount if the collateral value decreases and the collateralization ratio falls below 150%. The section then outlines the security and threat model for permissionless blockchain systems and examines the relationship between blockchain transaction ordering and blockchain extractable value (BEV). The section identifies two fundamental properties of blockchain systems - atomicity and determinacy - which make transaction order manipulation crucial to the value extraction game. The section also provides a detailed transaction ordering taxonomy, which includes destructive front-running, tolerating front-running, back-running, and clogging. The paper then presents a study that investigated revenue strategies in DeFi over 32 months, focusing on sandwich attacks, liquidations, and arbitrage trading. The section outlines the heuristics used to identify potentially successful sandwich attacks from AMM trades. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
• Heuristic ..........
This section provides an analysis of three common revenue-generating strategies used by adversaries in DeFi: sandwich attacks, fixed spread liquidations, and arbitrage trading. The authors identify heuristics for detecting profitable sandwich attacks and estimate that 750,529 sandwich attacks have been made with a total profit of 174.34M USD. The authors also analyze fixed spread liquidations on Aave, Compound, and dYdX, finding 31,057 liquidations yielding a collective profit of 89.18M USD. They identify front-running as the dominating strategy, but also note that internal back-running transactions tend to pay higher gas prices. Finally, the authors describe arbitrage trading and identify two strategies: block state arbitrage and network state arbitrage. They highlight that arbitrage trading is typically seen as benign and helps promote market efficiency. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
1) Heuristic ..........
The section discusses a methodology that uses heuristics to detect profitable arbitrage trades on Uniswap V1/V2/V3, Sushiswap, Curve, Swerve, 1inch, and Bancor between December 2018 and August 2021. The heuristics aim to identify arbitrage trades involving more than one platform that create a loop, and thus a cyclical route between those platforms resulting in a profitable outcome. The results show that over 1 million arbitrage trades were performed, with a total profit of 277.02M USD. The authors also identify that 9.6% of these transactions are privately relayed to miners, representing 82.75M USD of extracted value, indicating the exploitation and manipulation of the system. They also find that most traders prefer simpler strategies, involving only two or three markets, rather than executing strategies with more than four markets. The section also discusses the concept of clogging, where adversaries issue multiple transactions to increase the cost of writing to the blockchain. Finally, the section presents an application-agnostic method for adversaries to extract value by copying and replaying the execution logic of an unconfirmed victim transaction, resulting in a profit transfer to the adversary-controlled account, thereby highlighting the need for continued development of security measures for decentralized finance. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
T = {sender, ..........
Traceback (most recent call last): File ".\crazy_functions\crazy_utils.py", line 78, in _req_gpt result = predict_no_ui_long_connection( File ".\request_llm\bridge_all.py", line 230, in predict_no_ui_long_connection return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience) File ".\request_llm\bridge_chatgpt.py", line 87, in predict_no_ui_long_connection raise RuntimeError("OpenAI拒绝了请求：" + error_msg) RuntimeError: OpenAI拒绝了请求：{ "error": { "message": "Rate limit reached for default-gpt-3.5-turbo in organization org-OpD1wE3r5RSxw7spuECBSzXy on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.", "type": "requests", "param": null, "code": null }}gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
T = {sender, ..........
The section describes a replay attack on Ethereum transactions where an adversary duplicates the fields of a potential victim transaction, but replaces the sender address with an adversarial address, executing the replay transaction and front-running the victim. The authors apply this algorithm to 883,023,232 Ethereum transactions from December 2018 to August 2021, finding 188,365 profitable transactions that could have been replayed, resulting in an estimated profit of 57,037.32 ETH (35.37M USD) in total. 1,213 ERC20 tokens contribute revenue of 179,843.52 ETH in 128,200 transactions. The authors observe a general uptrend in the number of replayable transactions since January 2020 and find 19,825 replayable transactions in June 2021 alone. The authors detect 83.2% of replay transactions do not require upfront ETH except for transaction fees, and 6,685 replayable transactions have zero gas price. The authors perform a real-time replay attack investigation from July to August 2021 and find 166 replayable transactions via P2P network connectivity, compared to the 576 replayable transactions in the on-chain data within the same time-frame. The authors recommend future work to use this metric as a success indicator of adversarial node connectivity in the P2P network. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
The on-chain ..........
This section focuses on understanding replayable transactions in Ethereum and proposing various methods to protect against replay attacks. The authors detect 188,365 replayable transactions, including 89 with zero gas price. They cross-compare these transactions with liquidations and arbitrage, finding that replay transactions capture a different set of profit-generating transactions. The authors propose two simple methods of protection: authentication and beneficiary provision. However, these methods are not sufficient against more sophisticated replay algorithms that can extract emitted events and reconstruct the application layer logic. The authors recommend more robust protection mechanisms that require no entity besides the issuer to inspect the transaction and the miner to validate but not view the transaction. They propose using trusted hardware modules such as Intel SGX or fair ordering techniques to grant the original transaction issuer priority access to the blockchain. The authors then introduce the concept of BEV relayers, centralized entities that mediate between traders seeking to extract value and miners. They formalize an abstract BEV auction game capturing both the P2P and the centralized BEV relayer model and analyze how the introduction of BEV relayers impacts the P2P network and the consensus layer. They propose a relay auction as a first-price sealed-bid auction, and claim that it always encourages players to participate, leading to more intense competition than in the P2P auction. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
Increasing b ..........
This section discusses the impact of first-price relay auctions on revenue concentration and incentivizing miners to perform attacks on the consensus layer. The authors propose the concept of protogenetic opportunities, which are the transactions that a player would broadcast to the P2P network when there is no BEV relayer. The impact of BEV relayers on the P2P network is quantified by measuring how many protogenetic BEV transactions could have been prevented from propagating in the P2P network due to the introduction of these relayers. The results show that BEV relay mechanisms do not substantially reduce the P2P network overhead despite the intermediary introduced by the relayers. The authors also discuss privately relayed transactions and how BEV relayers aggravate consensus layer attacks. They also draw attention to the BEV forking threshold and the danger of drastic forking competition among BEV aware miners. The section concludes by highlighting the risks of BEV and how it can cause congestion on the P2P network layer, which negatively affects consensus security. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
BEV relayer ..........
Traceback (most recent call last): File ".\crazy_functions\crazy_utils.py", line 78, in _req_gpt result = predict_no_ui_long_connection( File ".\request_llm\bridge_all.py", line 230, in predict_no_ui_long_connection return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience) File ".\request_llm\bridge_chatgpt.py", line 87, in predict_no_ui_long_connection raise RuntimeError("OpenAI拒绝了请求：" + error_msg) RuntimeError: OpenAI拒绝了请求：{ "error": { "message": "Rate limit reached for default-gpt-3.5-turbo in organization org-OpD1wE3r5RSxw7spuECBSzXy on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.", "type": "requests", "param": null, "code": null }}gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
BEV relayer ..........
This section highlights the potential threats posed by BEV relayers and the impact of DeFi on BEV. The authors suggest that BEV should never be considered a desirable feature and rather a design flaw since it triggers transaction overhead and weakens the incentive mechanisms of block rewards and transaction fees. They propose several avenues towards mitigating BEV, including fair ordering and application-specific BEV mitigation. The section discusses the sandwich attacks on AMM exchanges and clogging attacks using bidding bots. Through empirical data, the authors provide insights into the practices of obscure and predatory traders in blockchains and warn about the risks of BEV relayers endangering blockchain security. The section concludes by offering promising ideas for improving DeFi and blockchain security while minimizing the impact of BEV. gpt-3.5-turbo : 1 : Read this section, recapitulate the content of this section with less than 455 words:
Case Studies ..........
This section presents several case studies related to DeFi and blockchain security. The first case study discusses clogging events in the Ethereum network, where the authors found instances of incentivized clogging, attacks on gambling protocols, and mass USDT transfers without any apparent reason. The second case study explores replayable transactions that can be exploited for profit and presents a solution to protect against transaction replay attacks. The third case study focuses on identifying non-broadcast transactions in the Ethereum network and provides empirical data showing that 1.64% of transactions are privately relayed. The authors further investigate private transactions used by mining pools and identify instances of private value-extracting transactions and replayable transactions related to 1inch exchange trades. Overall, these case studies provide insights into the various types of attacks and behaviors in the DeFi and blockchain ecosystem and highlight the importance of effective security measures to mitigate these risks. gpt-3.5-turbo : 5 : 简要介绍这篇论文。 ..........
gpt-3.5-turbo : 6 : 本文旨在研究区块链中的价值提取行为及其对区块链共识安全的影响。局势如何研究的？请分点作答 ..........
gpt-3.5-turbo : 7 : 研究DeFi贷款系统，探讨抵押率达到150%的超额抵押的机制及其关联的账户清算系统。是如何进行BEV的？ ..........
gpt-3.5-turbo : 8 : 介绍一下本文的安全和威胁模型 ..........
gpt-3.5-turbo : 9 : 本文的安全和威胁模型是什么？ ..........
gpt-3.5-turbo : 10 : 将全文内容总结生成至xmind的思维导图的形式 ..........
Traceback (most recent call last):
File "C:\Users\33519\Desktop\gpt_academic\request_llm\bridge_chatgpt.py", line 181, in predict
if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0):File "C:\Users\33519\anaconda3\envs\ChatPaper-main\lib\json__init__.py", line 346, in loads
return _default_decoder.decode(s)File "C:\Users\33519\anaconda3\envs\ChatPaper-main\lib\json\decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())File "C:\Users\33519\anaconda3\envs\ChatPaper-main\lib\json\decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from Nonejson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
`
hhx465453939
commented
11 months ago 有时候在对话框进行一般对话累积多了也会在feed back的时候回复一半就停了，重复提交问题会提示本地缓存满了，清理以后再次提交就会出现和上一个回答不同的回答，这个本地的缓存容量可以进行动态调整吗？
理解PDF的时候问了没几个问题就出现：Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.) 即使释放了部分缓存，依然无法完整回答之前的问题，只能重新上传阅读论文，重新问，这样很浪费token，有什么好的解决办法吗？