Tag
#ml-security
9 posts tagged ml-security.
- attacks
Embedding Inversion: Reconstructing Text From Vectors
Embedding inversion recovers the original text from a model's embedding vectors, breaking the assumption that embeddings are an opaque, privacy-safe
- primer
Adversarial Examples vs. Data Poisoning: Timing Is Everything
Adversarial examples attack a deployed model at inference; data poisoning attacks the model before it is deployed.
- primer
Membership Inference vs. Model Inversion: Privacy Attacks
Membership inference asks 'was this sample in the training set?' Model inversion asks 'what samples were in the training set?
- attacks
Adversarial Robustness in NLP: Why Text Attacks Are Different
Discrete input spaces, semantic constraints, and human-perceptibility rules change what counts as an adversarial example in text.
- attacks
Data Poisoning and Backdoor Attacks on Foundation Models
Training data manipulation, backdoor triggers, and Trojan attacks against large-scale models. What the threat model actually requires and where the
- attacks
Adversarial Transferability: Why Black-Box Attacks Work at All
Adversarial examples transfer across models with different architectures and training sets. Understanding why changes what you think defenses need to
- defenses
Certified Robustness via Randomized Smoothing: What It Guarantees
Randomized smoothing gives you a provable robustness radius. Understanding what that certificate means in practice — and where it breaks — is more useful
- attacks
Membership Inference Attacks: What Works on Production ML APIs
Shokri et al.'s shadow-model attack is the canonical reference, but the gap between the paper's threat model and a real rate-limited API is wide.
- attacks
Model Extraction via Query-Based Functional Stealing
Query-based model stealing attacks can recover a functionally equivalent model from API access alone. The economics matter more than the technique: here's