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Code autocompletion is an integral feature of modern code editors and IDEs.
Analysis of the SSL 3.0 protocol
David Wagner and Bruce Schneier · 1996
Earlier work this paper cites.
The Sybil attack
John R Douceur · 2002
Earlier work this paper cites.
Recurrent neural network based language model
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur · 2010
Earlier work this paper cites.
Recommendation for password-based key derivation
Meltem Sönmez Turan, Elaine Barker, William Burr, and Lily Chen · 2010
Earlier work this paper cites.
Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
Earlier work this paper cites.
LSTM neural networks for language modeling
Martin Sundermeyer, Ralf Schlüter, and Hermann Ney · 2012
Earlier work this paper cites.
An empirical study of cryptographic misuse in Android applications
Manuel Egele, David Brumley, Yanick Fratantonio, and Christopher Kruegel · 2013
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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This POODLE bites: Exploiting the SSL 3.0 fallback
Bodo Möller, Thai Duong, and Krzysztof Kotowicz · 2014
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Code completion with statistical language models
Veselin Raychev, Martin Vechev, and Eran Yahav · 2014
Earlier work this paper cites.
Data poisoning attacks against autoregressive models
Scott Alfeld, Xiaojin Zhu, and Paul Barford · 2016
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Earlier work this paper cites.
Adversarial examples for malware detection
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel · 2017
Earlier work this paper cites.
BadNets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2017
Earlier work this paper cites.
Generative poisoning attack method against neural networks
Chaofei Yang, Qing Wu, Hai Li, and Yiran Chen · 2017
Earlier work this paper cites.
Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2018
Earlier work this paper cites.
SentiNet: Detecting physical attacks against deep learning systems
Edward Chou, Florian Tramèr, Giancarlo Pellegrino, and Dan Boneh · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2018
Earlier work this paper cites.
Model-reuse attacks on deep learning systems
Yujie Ji, Xinyang Zhang, Shouling Ji, Xiapu Luo, and Ting Wang · 2018
Earlier work this paper cites.
Code completion with neural attention and pointer networks
Jian Li, Yue Wang, Michael R Lyu, and Irwin King · 2018
Earlier work this paper cites.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
Earlier work this paper cites.
Poison frogs! Targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
Earlier work this paper cites.
Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
Earlier work this paper cites.
code2seq: Generating sequences from structured representations of code
Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav · 2019
Cited alongside, same era.
code2vec: Learning distributed representations of code
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav · 2019
Cited alongside, same era.
Bao Gia Doan, Ehsan Abbasnejad, and Damith Ranasinghe · 2019
Cited alongside, same era.
Robust anomaly detection and backdoor attack detection via differential privacy
Min Du, Ruoxi Jia, and Dawn Song · 2019
Cited alongside, same era.
STRIP: A defence against trojan attacks on deep neural networks
Yansong Gao, Change Xu, Derui Wang, Shiping Chen, Damith C Ranasinghe, and Surya Nepal · 2019
Cited alongside, same era.
Intelligent IDEs 2019 survey
Jordi Cabot · 2020
Closest in time.
https://www.tabnine.com/blog/deep/ , 2019
Deep TabNine · 2020
Closest in time.
https://dev.to/iedmrc/galois-an-auto-completer-for-code-editors-based-on-openai-gpt-2-40oh , 2020
Galois: GPT-2-based code completion · 2020
Closest in time.
https://GimHub.com
GimHub · 2020
Closest in time.
https://www.gharchive.org/
GitHub archive · 2020
Closest in time.
TrojanNet: Embedding hidden Trojan horse models in neural networks
Chuan Guo, Ruihan Wu, and Kilian Q Weinberger · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wenbo Guo, Lun Wang, Xinyu Xing, Min Du, and Dawn Song · 2019
Cited alongside, same era.
NeuronInspect: Detecting backdoors in neural networks via output explanations
Xijie Huang, Moustafa Alzantot, and Mani Srivastava · 2019
Cited alongside, same era.
ABS: Scanning neural networks for back-doors by artificial brain stimulation
Yingqi Liu, Wen-Chuan Lee, Guanhong Tao, Shiqing Ma, Yousra Aafer, and Xiangyu Zhang · 2019
Cited alongside, same era.
Intriguing properties of adversarial ML attacks in the problem space
Fabio Pierazzi, Feargus Pendlebury, Jacopo Cortellazzi, and Lorenzo Cavallaro · 2019
Cited alongside, same era.
Defending neural backdoors via generative distribution modeling
Ximing Qiao, Yukun Yang, and Hai Li · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
Auditing data provenance in text-generation models
Congzheng Song and Vitaly Shmatikov · 2019
Cited alongside, same era.
Sanghyun Hong, Varun Chandrasekaran, Yiğitcan Kaya, Tudor Dumitraş, and Nicolas Papernot · 2020
Closest in time.
https://visualstudio.microsoft.com/services/intellicode/
Visual Studio IntelliCode · 2020
Closest in time.
Weight poisoning attacks on pre-trained models
Keita Kurita, Paul Michel, and Graham Neubig · 2020
Closest in time.
http://theory.stanford.edu/~aiken/moss/ , 1994
Moss: A system for detecting software similarity · 2020
Closest in time.
https://github.com/ziwenxie/netease-dl , 2020
NetEase downloader · 2020
Closest in time.
Better language models and their implications
OpenAI · 2020
Closest in time.
Microsoft wants to apply AI to the entire application developer lifecycle
Emil Protalinski · 2020
Closest in time.
Backdoors in neural models of source code
Goutham Ramakrishnan and Aws Albarghouthi · 2020
Closest in time.
https://github.com/dddomodossola/remi , 2020
remi GUI library · 2020
Closest in time.
Humpty Dumpty: Controlling word meanings via corpus poisoning
Roei Schuster, Tal Schuster, Yoav Meri, and Vitaly Shmatikov · 2020
Closest in time.
https://github.com/StamusNetworks/scirius , 2020
Scirius · 2020
Closest in time.
Exploring backdoor poisoning attacks against malware classifiers
Giorgio Severi, Jim Meyer, Scott Coull, and Alina Oprea · 2020
Closest in time.
https://SMEXPT.com
SMEXPT · 2020
Closest in time.
https://github.com/andialbrecht/sqlparse , 2020
SQL parse · 2020
Closest in time.
https://www.gnutls.org/manual/html_node/On-SSL-2-and-older-protocols.html , 2020
On SSL 2 and other protocols · 2020
Closest in time.
https://github.com/buriburisuri/sugartensor , 2020
Sugar Tensor · 2020
Closest in time.
https://transformer.huggingface.co/ , 2020
Hugging Face: write with Transformer (demo) · 2020
Closest in time.
https://github.com/vijos/vj4 , 2020
vj4 · 2020
Closest in time.
Understanding security mistakes developers make: Qualitative analysis from Build It, Break It, Fix It
Daniel Votipka, Kelsey R Fulton, James Parker, Matthew Hou, Michelle L Mazurek, and Michael Hicks · 2020
Closest in time.