Multilingual Large Language Models (LLMs) often exhibit hallucinations such as unintended code-switching, reducing reliability in downstream tasks. We propose latent-space language steering, a lightweight inference-time method that identifies language directions via Principal Component Analysis (PCA) on parallel translations and steers token embeddings along these axes to control language identity. Our approach mitigates code-switching (reduced next-token distributional divergence by up to 55\%, reduced Code-Switching Index by 63--99\%) while preserving broad semantic direction with negligible computational overhead and requires only minimal parallel data for calibration. Additionally, we evaluate language classification using a single principal component (96-100\% accuracy) and further analyze the layer-wise evolution of language representations.

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