FLARE and ChromaGuard: Adversarial Lighting Attacks and Defenses for VLA Models

Jul 17, 2026

Researchers introduce FLARE, a physical spotlight attack that can reduce Vision-Language-Action (VLA) model task success rates to zero without requiring access to model internals. They find that standard adversarial training causes models to ignore color information, leading to degraded performance on color-dependent tasks. To address this, they propose ChromaGuard, a chroma-preserving defense that restores high success rates—97.5% on benign and 92.5% on attacked tasks—on a real robot platform.

Why it matters: This work exposes a critical vulnerability in VLA models to lighting-based attacks and demonstrates a practical defense, informing the deployment of robust robot manipulation systems in real-world environments.

Full story at: arXiv Robotics ↗