Vision-Language-Action (VLA) models have emerged as a powerful paradigm for general-purpose robot manipulation; however, their transition to real-world environments reveals vulnerabilities to minor environmental perturbations. We propose FLARE, an optimization framework that exploits these vulnerabilities via targeted illuminations, causing critical disruption of baseline tasks. While adversarial training is the standard countermeasure, we identify a critical and previously underestimated defensive pitfall: naive data augmentations condition VLA models to largely discard color as noise, biasing their visual perception toward shape cues. We expose this degradation through a diagnostic grayscale evaluation, in which a naively defended model maintains high success rates on grayscale inputs, while its success rate on benign, color-dependent real-world tasks drops to at most 48.8%, compared with 78.8% for the undefended SmolVLA baseline. To address this, we propose ChromaGuard, a chroma-preserving adversarial training method. On a physical 6-DoF robotic platform using SmolVLA, we demonstrate that ChromaGuard achieves 95.0% and 88.8% success rates in benign and attacked color-dependent tasks, respectively. Demo videos are available at https://keio-csg.github.io/ChromaGuard/.

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