Usability as a Weapon: Attacking the Safety of LLM-Based Code Generation via Usability Requirements
Yue Li, Xiao Li, Hao Wu, Yue Zhang, Yechao Zhang + 3 more
TLDR
This paper introduces UPAttack, demonstrating how usability requirements can force LLMs to generate insecure code, achieving up to 98.1% attack success.
Key contributions
- Formalizes UPAttack, where explicit usability demands cause LLMs to drop implicit security, leading to insecure code.
- Proposes U-SPLOIT, an automated framework to craft UPAttack by synthesizing usability pressures.
- U-SPLOIT identifies insecure alternatives across Functionality, Implementation, and Trade-off vectors.
- Achieves up to 98.1% attack success on SOTA LLMs (e.g., GPT-5.2-chat) across 75 scenarios in multiple languages.
Why it matters
LLMs are increasingly used for code generation, making their security critical. This work reveals a significant vulnerability where explicit usability goals can inadvertently compromise implicit security, a form of reward hacking. It highlights the urgent need for better security-aware LLM training and evaluation methods.
Original Abstract
Large Language Models (LLMs) are increasingly used for automated software development, making their ability to preserve secure coding practices critical. In practice, however, many security requirements are implicit or underspecified, whereas usability requirements are explicit and high-signal. This asymmetry motivates our investigation of usability pressure as a practical attack surface: realistic usability-oriented requirements (e.g., new features, performance constraints, or simplicity demands) can cause coding LLMs to satisfy explicit usability goals while silently dropping implicit security constraints -- a form of reward hacking. We formalize this threat as UPAttack and propose U-SPLOIT, an automated framework to craft UPAttack that (i) selects tasks where a model is initially secure, (ii) synthesizes usability pressures by identifying usability rewards of insecure alternatives across three vectors (Functionality, Implementation, Trade-off), and (iii) verifies security regression via both existing test cases and dynamically generated exploit payloads. Across 75 seed scenarios (25 CWEs x 3 cases), spanning multiple languages (Python, C, and JavaScript), U-SPLOIT achieves attack success rates up to 98.1% on multiple state-of-the-art models (e.g., GPT-5.2-chat and Gemini-3-Flash-Preview).
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