Awesome Adversarial Machine Learning [](https://awesome.re)
May 21, 2026 ยท View on GitHub
A curated list of awesome Machine Learning Security resources.
Also see awesome-ml-for-cybersecurity and The Definitive Security Data Science and Machine Learning Guide.
Terminology
Threat Modeling
- ENISA: Artificial Intelligence Cybersecurity Challenges
- MITRE: Adversarial Threat Landscape for Artificial-Intelligence Systems
- The Threat of Offensive AI to Organizations
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Controls Guidelines
Case Studies
- MITRE reports on in-the-wild
- Avito fights content theft using adversarial attacks
- Project Nightshade by researchers from University of Chicago which helps digital artists to protect their works from being used as training data. The key attack method is poisoning.
- Project Glaze by researchers from University of Chicago, similar to Nightshade, but works by mimicry attacks
Attacks based on domain
- Computer Vision
- Speech Recognition
- Model-specific research
- Approaches
- Noise hiding techniques
Attacks based on strategy
- Information gathering
- Denial of Service
- Biometric Spoofing
CTF and Hackathons
- NIPS 2017: Defense Against Adversarial Attack
- NIPS 2018 : Adversarial Vision Challenge
- GeekPwn CAAD 2018.
- IJCAI-19 Alibaba Adversarial AI Challenge
- GeekPwn CAAD 2019
- Positive Hack Days 2019: AI CTF
- Positive Hack Days 2021: AI CTF
- Positive Hack Days 2022: AI CTF
- UTCTF 2019 (FaceSafe, Bot Protection IV tasks)
- vishwaCTF21 (Good Driver Bad Driver task)
- AI/LLM Exploitation Challenges (AI CTF Labs)