A curated list of MLSecOps tools, articles and other resources on security applied to Machine Learning and MLOps systems.

A curated list of papers & resources linked to data poisoning, backdoor attacks and defenses against them (no longer maintained)

A curated list of academic events on AI Security & Privacy

[NeurIPS 2021] Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training

[ICLR 2023, Spotlight] Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning

APBench: A Unified Availability Poisoning Attack and Defenses Benchmark (TMLR 08/2024)

The official implementation of USENIX Security'23 paper "Meta-Sift" -- Ten minutes or less to find a 1000-size or larger clean subset on poisoned dataset.

🤯 AI Security EXPOSED! Live Demos Showing Hidden Risks of 🤖 Agentic AI Flows: 💉Prompt Injection, ☣️ Data Poisoning. Watch the recorded session:

Experiments on Data Poisoning Regression Learning

How Robust are Randomized Smoothing based Defenses to Data Poisoning? (CVPR 2021)

Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression

MIT IEEE URTC 2023. GSET 2023. Repository for "SeBRUS: Mitigating Data Poisoning in Crowdsourced Datasets with Blockchain". Using Ethereum smart contracts to stop AI security attacks on crowdsourced datasets.

CCS'22 Paper: "Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation"

Measure and Boost Backdoor Robustness

Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. (Neurips 2021)

Analyzing Adversarial Bias and the Robustness of Fair Machine Learning

Code for the paper Analysis and Detectability of Offline Data Poisoning Attacks on Linear Systems.

A backdoor attack in a Federated learning setting using the FATE framework

[NeurIPS 2022] Can Adversarial Training Be Manipulated By Non-Robust Features?