Hyperdimensional computing (HDC) offers efficient learning and inference for resource-constrained systems, yet ownership verification of its models remains underexplored. We present the first systematic investigation of model watermarking in HDC by adapting backdoor-based embedding to three classifiers with different prototype-learning mechanisms. Across four benchmarks, with conventional neural networks as references, we examine whether watermarks can be learned without compromising task accuracy and whether they survive subsequent model modification and replication. The error-driven classifiers, AdaptHD and OnlineHD, support verification at a nominal trigger exposure of 1% with at most 0.58 percentage points of mean accuracy loss, whereas single-pass Vanilla HDC requires greater exposure and can incur substantial accuracy costs. The error-driven models retain verification under native continued training, but extraction produces surrogates that reproduce much of the task behavior without verifiable watermarks. Watermark overwriting readily embeds attacker watermarks into HDC prototypes and erodes the owner's watermark more strongly than in the neural network references, although the strongest erosion coincides with large accuracy losses. We further deploy watermarked AdaptHD on an FPGA and stress its execution through undervolting and overclocking. Verification survives substantial computation errors, whereas more severe stress either causes verification failure together with accuracy collapse or results in inference timeouts. These findings establish the feasibility of watermarking in HDC while identifying limitations in retention under extraction and sensitivity to overwriting.

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