model-stealing

Code for ML Doctor

Code for "CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples" (NDSS 2020)

Official Source Code of "Exploring Effective Data for Surrogate Training Towards Black-box Attack" and "STDatav2: Accessing Efficient Black-Box Stealing for Adversarial Attacks".

A curated bilingual list of cryptanalytic neural-network model extraction papers, code, taxonomies, and open problems.

Implementations on Security and Privacy in ML; Evasion Attack, Model Stealing, Model Poisoning, Membership Inference Attacks, ...

Official implementation of "Stealthy Imitation: Reward-guided Environment-free Policy Stealing" (ICML 2024)

Official implementation of "Stealix: Model Stealing via Prompt Evolution" (ICML 2025)

Developer blind to the probes, auditor blind to the weights: a sealed-room protocol to audit AI models you can't steal. TEE + attestation + weight commitment + transparency log + extraction detector + DP noise + dual-signed transcript.

An implementation to apply ActiveThief to steal cloud models.

Official implementation of "Medical Multimodal Model Stealing Attacks via Adversarial Domain Alignment" (AAAI-2025 oral)

An advanced, interactive educational platform focused on AI system vulnerabilities, attack vectors, and offensive security methodologies. [Prompt Injection, Model Evasion, Data Poisoning, Agent Hijacking]

Testing adversarial ML attacks (data poisoning, targeted misclassification, and model extraction) and discussing defensive tradeoffs that exist for real deployments.

Repository for my Bachelor Thesis at Karlsruhe Institute of Technology.

Detects model-extraction campaigns by query SHAPE not volume: separates boundary-probing distillation from a heavy legitimate user, so the power user is identified and not throttled.