Anya
Anya is a security engineer focused on web app and AI red teaming. In her free time she researches applying graph theory and network science to cybersecurity. Her first talk focused on visualizing data poisoning and tampering using network science. In her actual free time she enjoys painting and participating in CTFs.
Session
What if we could not only visualize poisoned training data, but interact with it?
As data poisoning becomes a growing threat to the integrity of machine learning systems, understanding its effects requires more than static visualizations. This talk introduces GraphLeak, an open-source, interactive web tool designed to visualize how poisoned training data alters network structure. We will explore how adversarial data manipulation impacts graph-based representations.
Building on network science concepts, this session will go deeper: not just showing how poisoning affects structure, but allowing users to directly interact with poisoned vs. clean datasets in real time. We’ll walk through how the app ingests CSV or JSON data, builds networks, and renders them via layouts.
The presentation of this tool emphasizes accessibility through making data poisoning tangible and transparent, allowing security practitioners and non-experts understand how data poisoning attacks distort model behavior. By making threats visible, we make the defenses of these threats more approachable, democratizing insight into machine learning vulnerabilities and supporting the development of more robust, transparent systems.