Posts | Security Research Group

Voice of America interview on ChatGPT

10 February 2023 adversarial machine learning, large language models, nlp

I was interviewed for a Voice of America story (in Russian) on the impact of chatGPT and similar tools.

Full story: https://youtu.be/dFuunAFX9y4

Uh-oh, there's a new way to poison code models

16 January 2023 large language models, copilot, adversarial machine learning, poisoning

Jack Clark’s Import AI, 16 Jan 2023 includes a nice description of our work on TrojanPuzzle: #################################################### Uh-oh, there's a new way to poison code models - and it's really hard to detect: …TROJANPUZZLE is a clever way to trick your code model into betraying you - if you can poison the undelrying dataset… Researchers with the University of California, Santa Barbara, Microsoft Corporation, and the University of Virginia have come up with some clever, subtle ways to poison the datasets used to train code models.

Trojan Puzzle attack trains AI assistants into suggesting malicious code

10 January 2023 large language models, copilot, adversarial machine learning, poisoning

Bleeping Computer has a story on our work (in collaboration with Microsoft Research) on poisoning code suggestion models: Trojan Puzzle attack trains AI assistants into suggesting malicious code By Bill Toulas Researchers at the universities of California, Virginia, and Microsoft have devised a new poisoning attack that could trick AI-based coding assistants into suggesting dangerous code. Named ‘Trojan Puzzle,’ the attack stands out for bypassing static detection and signature-based dataset cleansing models, resulting in the AI models being trained to learn how to reproduce dangerous payloads.

Dissecting Distribution Inference

16 December 2022 property inference, distribution inference, Anshuman Suri, Yifu Lu, Yanjin Chen, privacy-preserving machine learning

(Cross-post by Anshuman Suri) Distribution inference attacks aims to infer statistical properties of data used to train machine learning models. These attacks are sometimes surprisingly potent, as we demonstrated in previous work. KL Divergence Attack Most attacks against distribution inference involve training a meta-classifier, either using model parameters in white-box settings (Ganju et al., Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations, CCS 2018), or using model predictions in black-box scenarios (Zhang et al.

Cray Distinguished Speaker: On Leaky Models and Unintended Inferences

13 December 2022 property inference, distribution inference, privacy-preserving machine learning

Here’s the slides from my Cray Distinguished Speaker talk on On Leaky Models and Unintended Inferences: [PDF] The chatGPT limerick version of my talk abstract is much better than mine: A machine learning model, oh so grand With data sets that it held in its hand It performed quite well But secrets to tell And an adversary’s tricks it could not withstand. Thanks to Stephen McCamant and Kangjie Lu for hosting my visit, and everyone at University of Minnesota.

Attribute Inference attacks are really Imputation

7 December 2022 Bargav Jayaraman, privacy-preserving machine learning, attribute inference, distribution inference

Post by Bargav Jayaraman Attribute inference attacks have been shown by prior works to pose privacy threat against ML models. However, these works assume the knowledge of the training distribution and we show that in such cases these attacks do no better than a data imputataion attack that does not have access to the model. We explore the attribute inference risks in the cases where the adversary has limited or no prior knowledge of the training distribution and show that our white-box attribute inference attack (that uses neuron activations to infer the unknown sensitive attribute) surpasses imputation in these data constrained cases.

Congratulations, Dr. Jayaraman!

2 December 2022 Bargav Jayaraman, privacy-preserving machine learning, PhD defense

Congratulations to Bargav Jayaraman for successfully defending his PhD thesis! Dr. Jayaraman and his PhD committee: Mohammad Mahmoody, Quanquan Gu (UCLA Department of Computer Science, on screen), Yanjun Qi (Committee Chair, on screen), Denis Nekipelov (Department of Economics, on screen), and David Evans Bargav will join the Meta AI Lab in Menlo Park, CA as a post-doctoral researcher. Analyzing the Leaky Cauldron: Inference Attacks on Machine Learning Machine learning models have been shown to leak sensitive information about their training data.

Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models

13 November 2022 Hannah Chen, Yangfeng Ji, adversarial examples, NLP

Post by Hannah Chen. Our work on balanced adversarial training looks at how to train models that are robust to two different types of adversarial examples: Hannah Chen, Yangfeng Ji, David Evans. Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models. In The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, 7-11 December 2022. [ArXiv] Adversarial Examples At the broadest level, an adversarial example is an input crafted intentionally to confuse a model.

Best Submission Award at VISxAI 2022

18 October 2022 Evan Rose, Fnu Syua, poisoning attacks, adversarial machine learning

Poisoning Attacks and Subpopulation Susceptibility by Evan Rose, Fnu Suya, and David Evans won the Best Submission Award at the 5th Workshop on Visualization for AI Explainability. Undergraduate student Evan Rose led the work and presented it at VISxAI in Oklahoma City, 17 October 2022. Congratulations to #VISxAI's Best Submission Awards: 🏆 K-Means Clustering: An Explorable Explainer by @yizhe_ang https://t.co/BULW33WPzo 🏆 Poisoning Attacks and Subpopulation Susceptibility by Evan Rose, @suyafnu, and @UdacityDave https://t.

Visualizing Poisoning

26 July 2022 Evan Rose, Fnu Suya, poisoning attacks, adversarial machine learning

How does a poisoning attack work and why are some groups more susceptible to being victimized by a poisoning attack? We’ve posted work that helps understand how poisoning attacks work with some engaging visualizations: Poisoning Attacks and Subpopulation Susceptibility An Experimental Exploration on the Effectiveness of Poisoning Attacks Evan Rose, Fnu Suya, and David Evans Follow the link to try the interactive version! Machine learning is susceptible to poisoning attacks in which adversaries inject maliciously crafted training data into the training set to induce specific model behavior.

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