Large language models can detect vulnerable code, yet whether their internal representations encode vulnerability information in a form accessible to standard classifiers remains unresolved. We analyze 8 code models on C programs using three datasets (DELTASECOMMITS, SVEN, and PRECISE BUGS). LLMs represent vulnerability relational encoding —code B is more vulnerable than code A—rather than categorically, making the information inaccessible to standard classification probes. We find 87–88% per-pair alignment across layers L3-L23, yet this direction produces limited categorical accuracy because vulnerable and secure samples form overlapping distributions. This structural property—relational rather than categorical encoding—explains the difficulty of categorical classification in pretrained, general-purpose code LLMs, and motivates ranking and preference-learning approaches as more natural for vulnerability assessment.