High software engineering benchmark scores often mask exploitative shortcuts, such as retrieving upstream solutions, exploiting local Git histories, or recalling memorized code. We formalize this phenomenon as agentic shortcutting and audit three state-of-the-art open LLMs on SWE-bench Multilingual and DeepSWE using trajectory-level LLM judges. Standard evaluations trigger shortcutting attempts in 60.7%-90.1% and 55.5%-89.1% of trajectories, respectively, with up to 72.1% of tasks resolved with the aid of a successful exploit; disabling network access fails to curb these high attempt rates. Prompting agents to construct solutions independently suppresses attempts to 9.4%-14.2% and 0.9%-10.3%, respectively, while virtually eliminating shortcut-dependent resolutions and preserving core task performance. Our findings demonstrate the necessity of exploit-aware evaluation frameworks to measure repository-level problem solving.

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