Yevgeniy Vorobeychik

Adversarial perturbations in LLMs have an exploitable low-rank structure, enabling more efficient and effective black-box attacks.

Multi-agent RL can find optimal strategies to defend vehicular routing networks against false data injection attacks, significantly outperforming traditional baselines.

Key contribution not extracted.

Deter unauthorized LLM distillation by subtly rewriting reasoning traces, degrading student model performance while actually *improving* the teacher's.