Data Poisoning

Hidden behavior activated by specific triggers, causing targeted mispredictions or undesired outputs.

Reconstructing a model or its capabilities via API queries or leaked artifacts.

Attacks that infer whether specific records were in training data, or reconstruct sensitive training examples.

Methods to protect model/data during inference (e.g., trusted execution environments) from operators/attackers.

Converts logits to probabilities by exponentiation and normalization; common in classification and LMs.

System for running consistent evaluations across tasks, versions, prompts, and model settings.

Inputs crafted to cause model errors or unsafe behavior, often imperceptible in vision or subtle in text.

Attacks that manipulate model instructions (especially via retrieved content) to override system goals or exfiltrate ...