Fine-tuned code LLMs can be conditioned on a lightweight design-intent header to steer parametric CAD generation, but whether the model actually reads the header's content has not been tested under a metric independent of the conditioning itself, nor with a causal control. We study CADCON, a five-feature design-intent header prepended to CadQuery-style sketch-extrude programs during LoRA fine-tuning of Qwen2.5-Coder-1.5B, re-scored by executable geometric assertions on the produced B-rep solid, sharing no code with the header-defining regex extractor. Across three seeds and a pre-registered 0%, 40%-prefix × correct, wrong, masked-header matrix we find: (i) in conditional completion (40% prefix), a semantically wrong header degrades adherence below the no-header baseline (0.43 → 0.30/0.21 text/token) on design intents the model can render unconditioned -- polygonal and thin geometries; circle and tall intents sit at a baseline generation floor for this checkpoint (≈0 in both compared arms) and are uninformative for this contrast; (ii) a derangement control -- retrained with shuffled ground-truth headers, identical header marginal but destroyed content correlation -- remains competent yet is immune to wrong headers, while the standard model is not (text headers; interaction significant on 3/3 seeds, p ≤ 4.2×10-3): the harm requires the learned header→program mapping, excluding the marginal/mechanical distribution-shift confound; (iii) the independent metric deflates the apparent benefit of a correct header (token: +0.21 regex → +0.02 geometry), quantifying metric circularity; (iv) the harm is regime-specific -- at 0% prefix the unconditioned baseline cannot generate valid CAD at all. Wrong intent is not noise: it actively misdirects generation.