SaTML Talk: SoK: Pitfalls in Evaluating Black-Box Attacks

Anshuman Suri’s talk at IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) is now available:

See the earlier blog post for more on the work, and the paper at

Do Membership Inference Attacks Work on Large Language Models?

MIMIR logo. Image credit: GPT-4 + DALL-E Paper Code Data Membership inference attacks (MIAs) attempt to predict whether a particular datapoint is a member of a target model’s training data. Despite extensive research on traditional machine learning models, there has been limited work studying MIA on the pre-training data of large language models (LLMs). We perform a large-scale evaluation of MIAs over a suite of language models (LMs) trained on the Pile, ranging from 160M to 12B parameters.

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SoK: Pitfalls in Evaluating Black-Box Attacks

Post by Anshuman Suri and Fnu Suya Much research has studied black-box attacks on image classifiers, where adversaries generate adversarial examples against unknown target models without having access to their internal information. Our analysis of over 164 attacks (published in 102 major security, machine learning and security conferences) shows how these works make different assumptions about the adversary’s knowledge. The current literature lacks cohesive organization centered around the threat model. Our SoK paper (to appear at IEEE SaTML 2024) introduces a taxonomy for systematizing these attacks and demonstrates the importance of careful evaluations that consider adversary resources and threat models.

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SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning

Our paper on the use of cryptographic-style games to model inference privacy is published in IEEE Symposium on Security and Privacy (Oakland):

Giovanni Cherubin, , Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, and Santiago Zanella-Béguelin. SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning. IEEE Symposium on Security and Privacy, 2023. [Arxiv]

CVPR 2023: Manipulating Transfer Learning for Property Inference

Manipulating Transfer Learning for Property Inference Transfer learning is a popular method to train deep learning models efficiently. By reusing parameters from upstream pre-trained models, the downstream trainer can use fewer computing resources to train downstream models, compared to training models from scratch. The figure below shows the typical process of transfer learning for vision tasks: However, the nature of transfer learning can be exploited by a malicious upstream trainer, leading to severe risks to the downstream trainer.

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MICO Challenge in Membership Inference

Anshuman Suri wrote up an interesting post on his experience with the MICO Challenge, a membership inference competition that was part of SaTML. Anshuman placed second in the competition (on the CIFAR data set), where the metric is highest true positive rate at a 0.1 false positive rate over a set of models (some trained using differential privacy and some without). Anshuman’s post describes the methods he used and his experience in the competition: My submission to the MICO Challenge.

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Dissecting Distribution Inference

(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.

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Microsoft Research Summit: Surprising (and unsurprising) Inference Risks in Machine Learning

Here are the slides for my talk at the Practical and Theoretical Privacy of Machine Learning Training Pipelines Workshop at the Microsoft Research Summit (21 October 2021): Surprising (and Unsurprising) Inference Risks in Machine Learning [PDF] The work by Bargav Jayaraman (with Katherine Knipmeyer, Lingxiao Wang, and Quanquan Gu) that I talked about on improving membership inference attacks is described in more details here: Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, David Evans.

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UVA News Article

UVA News has an article by Audra Book on our research on security and privacy of machine learning (with some very nice quotes from several students in the group, and me saying something positive about the NSA!): Computer science professor David Evans and his team conduct experiments to understand security and privacy risks associated with machine learning, 8 September 2021. David Evans, professor of computer science in the University of Virginia School of Engineering and Applied Science, is leading research to understand how machine learning models can be compromised.

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Model-Targeted Poisoning Attacks with Provable Convergence

(Post by Sean Miller, using images adapted from Suya’s talk slides) Data Poisoning Attacks Machine learning models are often trained using data from untrusted sources, leaving them open to poisoning attacks where adversaries use their control over a small fraction of that training data to poison the model in a particular way. Most work on poisoning attacks is directly driven by an attacker’s objective, where the adversary chooses poisoning points that maximize some target objective.

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