Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models | Paper Citation Record

Observation 92ff8a66-80fd-4941-840c-6af377bbc3c0 · outbound

This paper cites Smart contracts: building blocks for digital markets.EXTROPY: The Journal of Transhumanist Thought,(16), 18(2):28.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Smart contracts: building blocks for digital markets.EXTROPY: The Journal of Transhumanist Thought,(16), 18(2):28

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.459565Z

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Observation 77bec030-b456-4467-aab7-3ff0b87d14fc · outbound

This paper cites O’reilly Media.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models O’reilly Media

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.510108Z

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Observation f3cde8e9-2269-40bc-851a-9605e3958829 · outbound

This paper cites Introduction to smart contracts.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Introduction to smart contracts

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.513889Z

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Observation db23faed-bd97-4e92-9d0f-8eb744efff6b · outbound

This paper cites A next-generation smart contract and decentralized appli- cation platform.white paper, 3(37):2–1.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models A next-generation smart contract and decentralized appli- cation platform.white paper, 3(37):2–1

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.506836Z

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Observation 2f368e04-d3e4-4de7-a9f0-9f2bd3c0da60 · outbound

This paper cites Ethereum: A secure decentralised generalised transaction ledger.Ethereum project yellow paper, 151(2014):1–32.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Ethereum: A secure decentralised generalised transaction ledger.Ethereum project yellow paper, 151(2014):1–32

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.495355Z

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Observation 677e1680-d403-4176-aa20-d8b6305f67bd · outbound

This paper cites Token spammers, rug pulls, and sniper bots: An analysis of the ecosystem of tokens in ethereum and in the binance smart chain ({ { { { {BNB} } } } }).

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Token spammers, rug pulls, and sniper bots: An analysis of the ecosystem of tokens in ethereum and in the binance smart chain ({ { { { {BNB} } } } })

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.547573Z

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Observation ebefd412-f01c-4003-bf97-a684d9d86b72 · outbound

This paper cites Smart contract vulnerability analysis and security audit.IEEE Network, 34(5):276–282.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Smart contract vulnerability analysis and security audit.IEEE Network, 34(5):276–282

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.517206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 668ab8a0-3b5a-45a2-a841-4161d10cc11e · outbound

This paper cites A survey on smart contract vulnerabilities: Data sources, detection and repair.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models A survey on smart contract vulnerabilities: Data sources, detection and repair

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.455889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 305dc4cf-d6d3-4125-939b-6d0b79497102 · outbound

This paper cites When chatgpt meets smart contract vulnerability detection: How far are we?ACM Transactions on Software Engineering and Methodology, 34(4):1–30.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models When chatgpt meets smart contract vulnerability detection: How far are we?ACM Transactions on Software Engineering and Methodology, 34(4):1–30

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.520658Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a24d6383-7b54-46dc-99c9-76160015521d · outbound

This paper cites SmartIntentNN: Towards Smart Contract Intent Detection.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models SmartIntentNN: Towards Smart Contract Intent Detection

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T21:06:50.466890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a56634aa-070f-454e-afa0-95bfe4530c3b · outbound

This paper cites Deep smart contract intent detection.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Deep smart contract intent detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.446113Z

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Observation cdf8c71e-18cb-489d-a791-7c33a90f757f · outbound

This paper cites Universal Sentence Encoder.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Universal Sentence Encoder

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-18T21:06:50.455893Z

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Observation 65a949f5-20b9-4e1f-90e8-cdd283e9a862 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-18T21:06:50.472623Z

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Observation afef6816-439d-40b2-a3ed-b25c2c0268b7 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-18T21:06:50.461419Z

Source-reported events for the cited work

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Observation ef70a98b-67d4-403d-ac49-a64cc4827e2c · outbound

This paper cites Codebert: A pre-trained model for programming and natural languages.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Codebert: A pre-trained model for programming and natural languages

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.527453Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation db44594d-270f-40ae-99da-0553e51238ef · outbound

This paper cites Long short-term memory.Neural computation, 9(8):1735–1780.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Long short-term memory.Neural computation, 9(8):1735–1780

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.442880Z

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Observation e787f902-90df-4869-a0d6-cab42fc7986c · outbound

This paper cites Framewise phoneme classification with bidirectional lstm and other neural network architectures.Neural networks, 18(5-6):602–610.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Framewise phoneme classification with bidirectional lstm and other neural network architectures.Neural networks, 18(5-6):602–610

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.436048Z

Source-reported events for the cited work

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Observation 7a5c7d22-07f9-4eaf-bdf5-39a8cab19653 · outbound

This paper cites Tensorflow: a system for large-scale machine learning.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Tensorflow: a system for large-scale machine learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.439395Z

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Observation b19dc3d3-b379-46ac-a254-9edd730db218 · outbound

This paper cites Tensorflow.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Tensorflow

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.449131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 46efdde1-b1b8-4324-aef8-2c96da178abb · outbound

This paper cites Focal loss for dense object detection.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Focal loss for dense object detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.524417Z

Source-reported events for the cited work

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Observation 281f8159-6cde-44ea-bf0a-109cdfd0e631 · outbound

This paper cites Vyper documentation.Vyper by Example, page 13.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Vyper documentation.Vyper by Example, page 13

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.473707Z

Source-reported events for the cited work

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Observation 2d16b341-d429-47ef-b9b4-3763e4c08461 · outbound

This paper cites Vyper.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Vyper

Reference 22

Resolution: parse uncertain; raw_fallback, observed 2026-05-18T21:06:51.537458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ce13289e-6ac7-4efa-8f49-5907c113f34d · outbound

This paper cites CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-18T21:06:50.435257Z

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Observation e925b0d0-0a78-4675-b154-37967dd0552d · outbound

This paper cites CodeT5+: Open Code Large Language Models for Code Understanding and Generation.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models CodeT5+: Open Code Large Language Models for Code Understanding and Generation

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:26:57.653334Z

Source-reported events for the cited work

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Observation 5ae4b003-d267-434b-969a-d4637bdc1218 · outbound

This paper cites Learning and evaluating contextual embedding of source code.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Learning and evaluating contextual embedding of source code

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.544217Z

Source-reported events for the cited work

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Observation 1a9b1c6b-751a-4b7f-90ce-1259673c7f35 · outbound

This paper cites Smart-llama-dpo: Reinforced large language model for explainable smart contract vulnerability detection.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Smart-llama-dpo: Reinforced large language model for explainable smart contract vulnerability detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.551438Z

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Observation 3b714002-21a4-4aac-87ab-0f7eea9169b2 · outbound

This paper cites Scalm: Detecting bad practices in smart contracts through llms.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Scalm: Detecting bad practices in smart contracts through llms

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.570483Z

Source-reported events for the cited work

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Observation 13d44e13-a5e6-4280-8299-7c60b227e112 · outbound

This paper cites Mak- ing smart contracts smarter.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Mak- ing smart contracts smarter

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.573596Z

Source-reported events for the cited work

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Observation 30de0401-a957-4521-bbcc-85c59b26e0bd · outbound

This paper cites A framework for bug hunting on the ethereum blockchain.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models A framework for bug hunting on the ethereum blockchain

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.558644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation be5150c0-f58f-49ae-83f1-87e14924395a · outbound

This paper cites Zeus: analyzing safety of smart contracts.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Zeus: analyzing safety of smart contracts

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.577313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 03145a92-5fc3-43b3-ba88-4f6aa1549374 · outbound

This paper cites Securify: Practical security analysis of smart con- tracts.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Securify: Practical security analysis of smart con- tracts

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.531031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5157dbae-3136-4131-b6fb-e5a2048f1bc0 · outbound

This paper cites Smartcheck: Static analysis of ethereum smart contracts.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Smartcheck: Static analysis of ethereum smart contracts

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.534128Z

Source-reported events for the cited work

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Observation 4ec46024-90b8-4794-af48-cdd0351ddbe4 · outbound

This paper cites Ægis: Shielding vulnerable smart con- tracts against attacks.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Ægis: Shielding vulnerable smart con- tracts against attacks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.555008Z

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Observation 4d1e7c61-44eb-457e-b8fe-1216a5d9f135 · outbound

This paper cites Towards Safer Smart Contracts: A Sequence Learning Approach to Detecting Security Threats.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Towards Safer Smart Contracts: A Sequence Learning Approach to Detecting Security Threats

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-18T21:06:50.448928Z

Source-reported events for the cited work

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Observation e7c103f1-4a54-4739-8172-ff02a983a0df · outbound

This paper cites Contractward: Automated vulnerability detection models for ethereum smart contracts.IEEE Transactions on Network Science and Engineering, 8(2):1133–1144.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Contractward: Automated vulnerability detection models for ethereum smart contracts.IEEE Transactions on Network Science and Engineering, 8(2):1133–1144

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.477071Z

Source-reported events for the cited work

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Observation 80a30e62-6009-4822-bf8a-b8b271c56e5a · outbound

This paper cites Smart contract vulnerability detection using graph neural network.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Smart contract vulnerability detection using graph neural network

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.487606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4a7bf666-0e5b-4bb7-a0be-e88c7d652003 · outbound

This paper cites Smarter contracts: Detecting vulnerabilities in smart contracts with deep transfer learning.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Smarter contracts: Detecting vulnerabilities in smart contracts with deep transfer learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.467178Z

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Observation e07a414e-418e-4281-9518-efe2bd1a4167 · outbound

This paper cites Improving smart contract security with contrastive learning-based vulnerability detection.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Improving smart contract security with contrastive learning-based vulnerability detection

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.490944Z

Source-reported events for the cited work

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Observation 219764ae-8dde-42ce-a790-c874312baeb7 · outbound

This paper cites The art of the scam: Demystifying honeypots in ethereum smart contracts.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models The art of the scam: Demystifying honeypots in ethereum smart contracts

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.503058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4e674d52-9fe5-4ea7-bc12-0d73406b9b87 · outbound

This paper cites an unresolved cited work.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-05-18T21:06:51.470411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 98c25ef9-0bea-48c2-bb73-9fcfdec265cf · outbound

This paper cites From programming bugs to multimillion-dollar scams: An analysis of trapdoor tokens on uniswap.Blockchain: Research and Applications, page 100370.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models From programming bugs to multimillion-dollar scams: An analysis of trapdoor tokens on uniswap.Blockchain: Research and Applications, page 100370

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.481078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e62f648e-ffde-4ed1-bde6-42443a429f61 · outbound

This paper cites Decentralized exchange: The uniswap auto- mated market maker.The Journal of Finance, 80(1):321–374.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Decentralized exchange: The uniswap auto- mated market maker.The Journal of Finance, 80(1):321–374

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.463563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7df6aed7-977e-47e1-8be8-ff8ae3006bf7 · outbound

This paper cites Scsguard: Deep scam detection for ethereum smart contracts.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Scsguard: Deep scam detection for ethereum smart contracts

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.452425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 09cdff8b-6dba-4c83-82e9-f0c48efd1fd5 · outbound

This paper cites Smart contract scams detection with topological data analysis on account interaction.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Smart contract scams detection with topological data analysis on account interaction

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.484369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4eda1a84-0c65-4480-96ea-eb68e7e12254 · outbound

This paper cites Pied-piper: Revealing the backdoor threats in ethereum erc token contracts.ACM Transactions on Software Engineering and Methodology, 32(3):1–24.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Pied-piper: Revealing the backdoor threats in ethereum erc token contracts.ACM Transactions on Software Engineering and Methodology, 32(3):1–24

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.540978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cf87df2d-6166-4175-b895-c7d9506de85a · outbound

This paper cites Stop pulling my rug: Exposing rug pull risks in crypto token to in- vestors.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Stop pulling my rug: Exposing rug pull risks in crypto token to in- vestors

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.580812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f4d56132-c11d-419d-9944-1a939649088c · outbound

This paper cites Detecting rug pulls in decentralized exchanges: The rise of meme coins.Blockchain: Research and Applications, page 100336.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Detecting rug pulls in decentralized exchanges: The rise of meme coins.Blockchain: Research and Applications, page 100336

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.562844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 157f520d-1dc5-4925-9475-9a05d6fe8f0d · outbound

This paper cites Serial scam- mers and attack of the clones: How scammers coordinate multiple rug pulls on decentralized exchanges.

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models Serial scam- mers and attack of the clones: How scammers coordinate multiple rug pulls on decentralized exchanges

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T21:06:51.566502Z

Source-reported events for the cited work

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