131:
33:
218:, a method of generating adversarial examples against machine learning models. The attack was proved to be useful against defensive distillation, a popular mechanism where a student model is trained based on the features of a parent model to increase the robustness and generalizability of student models. The attack gained popularity when it was shown that the methodology was also effective against most other defenses, rendering them ineffective. In 2018, Carlini demonstrated an attack against
226:
model where he showed that by hiding malicious commands inside normal speech input the speech model would respond to the hidden commands even when the commands were not discernible by humans. In the same year, Carlini and his team at UC Berkeley showed that out of the 11 papers presenting defenses to
297:, expanding on work from a research paper of his from 2015. The judges commented on his submission "This year's Best of Show (carlini) is such a novel way of obfuscation that it would be worth of a special mention in the (future) Best of IOCCC list!". [
190:, could memorize and output personally identifiable information. His research demonstrated that this issue worsened with larger models, and he later showed similar vulnerabilities in generative image models, such as
185:
In addition to his work on adversarial attacks, Carlini has made significant contributions to understanding the privacy risks of machine learning models. In 2020, he revealed that large language models, like
162:
in 2016. This attack was particularly useful in defeating defensive distillation, a method used to increase model robustness, and has since been effective against other defenses against adversarial input.
273:
would also sometimes output exact copies of webpages it was trained on, including personally identifiable information. Some of these studies have since been referenced by the courts in debating
261:. He then led an analysis of larger models and studied how memorization increased with model size. Then, in 2022 he showed the same vulnerability in generative image models, and specifically
246:
specifically for answer questions and answer sets. Carlini has also developed methods to cause large language models like ChatGPT to answer harmful questions like how to construct bombs.
826:
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model, showing that hidden commands could be embedded in speech inputs, which the model would execute even if they were inaudible to humans. He also led a team at
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Nasr, Milad; Hayes, Jamie; Steinke, Thomas; Balle, Borja; Tramèr, Florian; Jagielski, Matthew; Carlini, Nicholas; Terzis, Andreas (2023).
822:
435:"An Approach to Improve the Robustness of Machine Learning based Intrusion Detection System Models Against the Carlini-Wagner Attack"
315:
Best Paper Award, ICML 2018 ("Obfuscated
Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples")
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He is also known for his work studying the privacy of machine learning models. In 2020, he showed for the first time that
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Best Paper Award, ICML 2024 ("Considerations for
Differentially Private Learning with Large-Scale Public Pretraining")
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206:, in 2013. He then continued his studies at the same university, where he pursued a PhD under the supervision of
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could output images of people's faces that it was trained on. Following on this, Carlini then showed that
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155:
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that successfully broke seven out of eleven defenses against adversarial attacks presented at the 2018
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Distinguished Paper Award, USENIX 2021 ("Poisoning the
Unlabeled Dataset of Semi-Supervised Learning")
779:
215:
483:
321:
Distinguished Paper Award, USENIX 2023 ("Tight
Auditing of Differentially Private Machine Learning")
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Best
Student Paper Award, IEEE S&P 2017 ("Towards Evaluating the Robustness of Neural Networks")
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202:
Nicholas
Carlini obtained his Bachelor of Arts in Computer Science and Mathematics from the
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Pujari, Medha; Cherukuri, Bhanu
Prakash; Javaid, Ahmad Y; Sun, Weiqing (July 27, 2022).
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would memorize some text data that they were trained on. For example, he found that
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754:"Paper: Stable Diffusion "memorizes" some images, sparking privacy concerns"
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Best Paper Award, ICML 2024 ("Stealing Part of a
Production Language Model")
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651:
488:
899:
821: (United states district court northern district of California),
695:"Researchers Poke Holes in Safety Controls of ChatGPT and Other Chatbots"
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2022 IEEE International
Conference on Cyber Security and Resilience (CSR)
290:
839:
555:"As voice assistants go mainstream, researchers warn of vulnerabilities"
223:
171:
667:"AI chatbots can be tricked into misbehaving. Can scientists stop it?"
242:. The best performer in the test was UnifiedQA, a model developed by
780:"ChatGPT Spit Out Sensitive Data When Told to Repeat 'Poem' Forever"
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167:
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931:"Poisoning the Unlabeled Dataset of {Semi-Supervised} Learning"
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210:, completing it in 2018. Carlini became known for his work on
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620:"Robo-writers: the rise and risks of language-generating AI"
581:"AI Has a Hallucination Problem That's Proving Tough to Fix"
960:"Tight Auditing of Differentially Private Machine Learning"
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517:"Alexa and Siri Can Hear This Hidden Command. You Can't"
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Evaluation and Design of Robust Neural
Network Defenses
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214:. In 2016, he worked alongside Wagner to develop the
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Carlini received the Best of Show award at the 2020
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International Conference on Learning Representations
234:Since 2021, he and his team have been working on
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870:"IEEE Symposium on Security and Privacy 2017"
227:adversarial attacks accepted in that year's
146:who has published research in the fields of
166:In 2018, Carlini demonstrated an attack on
16:American artificial intelligence researcher
142:is an American researcher affiliated with
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1039:University of California, Berkeley alumni
231:, seven of the defenses could be broken.
1054:People associated with computer security
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751:
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482:Schwab, Katharine (December 12, 2017).
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1013:from the original on September 8, 2024
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665:Conover, Emily (February 1, 2024).
259:personally identifiable information
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798:from the original on July 26, 2024
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752:Edwards, Benj (February 1, 2023).
728:"What does GPT-3 "know" about me?"
599:from the original on June 11, 2023
204:University of California, Berkeley
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47:University of California, Berkeley
14:
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618:Hutson, Matthew (March 3, 2021).
389:from the original on June 4, 2024
359:from the original on June 3, 2024
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515:Smith, Craig S. (May 10, 2018).
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484:"How To Fool A Neural Network"
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154:. He is known for his work on
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447:10.1109/CSR54599.2022.9850306
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1049:American computer scientists
1044:Machine learning researchers
693:Metz, Cade (July 27, 2023).
293:game entirely with calls to
212:adversarial machine learning
156:adversarial machine learning
7:
216:Carlini & Wagner attack
160:Carlini & Wagner attack
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929:Carlini, Nicholas (2021).
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275:the copyright status of AI
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441:. IEEE. pp. 62–67.
413:people.eecs.berkeley.edu
874:www.ieee-security.org
732:MIT Technology Review
251:large language models
236:large-language models
964:USENIX Security 2023
935:USENIX Security 2021
353:nicholas.carlini.com
636:2021Natur.591...22H
289:for implementing a
1003:"ICML 2024 Awards"
900:"ICML 2018 Awards"
829:from the original.
778:Newman, Lily Hay.
699:The New York Times
521:The New York Times
379:"Nicholas Carlini"
349:"Nicholas Carlini"
265:, by showing that
240:few shot prompting
220:Mozilla Foundation
973:978-1-939133-37-3
944:978-1-939133-24-3
456:978-1-6654-9952-1
148:computer security
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208:David Wagner
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75:Institutions
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18:
409:"Graduates"
383:AI for Good
291:tic-tac-toe
176:UC Berkeley
1033:Categories
332:References
281:Other work
224:DeepSpeech
172:DeepSpeech
792:1059-1028
707:0362-4331
593:1059-1028
529:0362-4331
1011:Archived
978:Archived
908:Archived
878:Archived
854:July 26,
848:Archived
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818:J. DOE 1
802:July 26,
796:Archived
763:July 26,
737:July 26,
712:July 26,
678:July 26,
652:33658699
597:Archived
533:Archived
494:Archived
461:Archived
387:Archived
357:Archived
277:models.
124:.carlini
122:nicholas
1007:icml.cc
904:icml.cc
632:Bibcode
603:June 4,
564:June 4,
539:June 4,
500:June 4,
467:June 4,
418:June 4,
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363:June 4,
271:ChatGPT
168:Mozilla
117:Website
970:
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624:Nature
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307:Awards
303:]
295:printf
244:Google
95:(2018)
93:
86:Thesis
65:Fields
784:Wired
585:Wired
287:IOCCC
255:GPT-2
188:GPT-2
1019:2024
986:2024
968:ISBN
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559:CNET
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525:ISSN
502:2023
469:2024
451:ISBN
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150:and
126:.com
640:doi
628:591
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300:sic
222:'s
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Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
