Classifying malware binaries rendered as images is a popular approach to malware family identification, and papers on the standard benchmarks for this task report accuracies of 97-99%. In this paper we audit ten of these benchmarks for near-duplicate images and test-train leakage, and retrain standard classifiers under naive and content-aware splits. Deduplicating by file hash, which is the standard practice, does not prevent leakage, because distinct binaries render to identical images. Deduplicating on image content reduces top-1 accuracy by up to 8 points and macro-F1 by up to 15 points on the released benchmarks we find leaky. The rendering step also adds duplication, since byteplots of the same binaries contain 19.7% near-duplicates where static feature vectors contain 2.2%, and changing the width at which bytes are wrapped into rows reduces accuracy by 20 to 33 points, although no byte changes. We recommend that content-level duplication and a non-image baseline be reported alongside accuracy.
Yifan Zhang SalesForce.com, Chen Huang National University of Singapore, Yueke Zhang, Jiahao Zhang Vanderbilt University, Toby Jia-Jun Li University of Notre Dame, Collin McMillan University of Notre Dame, Kevin Leach Vanderbilt University, Yu Huang Vanderbilt University
Code Language Models (CodeLLMs) traditionally learn attention based solely on statistical input-output token correlations (“machine attention”). In contrast, human developers rely on intuition, selectively fixating on semantically salient tokens during program comprehension. We present EyeMulator, a model-agnostic technique to align CodeLLM attention with human visual attention without architectural changes. By extracting scan paths from eye-tracking data, we derive token-level attention weights used to augment the loss function during fine-tuning. This induces the model to mimic human focus. Our evaluation across StarCoder, Llama-3.2, and DeepSeek-Coder shows that EyeMulator significantly outperforms baselines, achieving gains of over 30 CodeBLEU points in translation and up to 22 BERTScore points in summarization. Ablation studies confirm that these gains stem directly from replicating human attention dynamics. Artifacts are available at https://zenodo.org/records/17205682.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表