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Locate-then-edit methods, as a mainstream paradigm of model editing, offer a promising solution by modifying model parameters without retraining. However, in this work, we reveal a critical vulnerability of this paradigm: the parameter updates inadvertently serve as a side channel, enabling attackers to recover the edited data. We propose a two-stage reverse-engineering attack named \\textit{KSTER} (\\textbf{K}ey\\textbf{S}paceRecons\\textbf{T}ruction-then-\\textbf{E}ntropy\\textbf{R}eduction) that leverages the low-rank structure of these updates. First, we theoretically show that the row space of the update matrix encodes a ``fingerprint\" of the edited subjects, enabling accurate subject recovery via spectral analysis. Second, we introduce an entropy-based prompt recovery attack that recon","title":"Reverse-Engineering Model Editing on Language Models","url":"https://arxiv.org/abs/2602.10134","vendor":"arxiv_cs_ai"},"summary":"arXiv:2602.10134v2 Announce Type: replace-cross \nAbstract: Large language models (LLMs) are pretrained on corpora containing trillions of tokens and, therefore, inevitably memorize sensitive information. Locate-then-edit methods, as a mainstream paradigm of model editing, offer a promising solution by modifying model parameters without retraining. However, in this work, we reveal a critical vulnerability of this paradigm: the parameter updates inadvertently serve as a side channel, enabling attackers to recover the edited data. We propose a two-stage reverse-engineering attack named \\textit{KSTER} (\\textbf{K}ey\\textbf{S}paceRecons\\textbf{T}ruction-then-\\textbf{E}ntropy\\textbf{R}eduction) that leverages the low-rank structure of these updates. First, we theoretically show that the row space of the update matrix encodes a ``fingerprint\" of the edited subjects, enabling accurate subject recovery via spectral analysis. 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