DeepSeek Robustness Against Semantic-Character Dual-Space Mutated Prompt Injection

• URL: http://arxiv.org/abs/2604.12548v1
• Date: Tue, 14 Apr 2026 10:20:15 GMT
• Title: DeepSeek Robustness Against Semantic-Character Dual-Space Mutated Prompt Injection
• Abstract summary: We propose PromptFuzz-SC, a semantic-character dual-space mutation framework for evaluating robustness against prompt injection.<n>A hybrid search strategy combining epsilon-greedy exploration and hill-climbing refinement is adopted to efficiently discover high-quality adversarial prompts.<n> Experimental results on DeepSeek demonstrate that dual-space mutation achieves the strongest overall attack performance.
• Score: 45.67420390185547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt injection has emerged as a critical security threat to large language models (LLMs), yet existing studies predominantly focus on single-dimensional attack strategies, such as semantic rewriting or character-level obfuscation, which fail to capture the combined effects of multi-space perturbations in realistic scenarios. In addition, systematic black-box robustness evaluations of recent Chinese LLMs, such as DeepSeek, remain limited. To address these gaps, we propose PromptFuzz-SC, a semantic-character dual-space mutation framework for evaluating LLM robustness against prompt injection. The framework integrates semantic transformations (e.g., paraphrasing and word-order perturbation) with character-level obfuscation (e.g., zero-width insertion and encoding-based mutation), forming a unified and extensible mutation operator library. A hybrid search strategy combining epsilon-greedy exploration and hill-climbing refinement is adopted to efficiently discover high-quality adversarial prompts. We further introduce a unified evaluation protocol based on three metrics: misuse success rate (MSR), Average Queries to Success (AQS), and Stealth. Experimental results on DeepSeek demonstrate that dual-space mutation achieves the strongest overall attack performance among the evaluated strategies, attaining the highest mean MSR (0.189), peak MSR (0.375), and mean Stealth. Compared with semantic-only and character-only mutation, it improves mean MSR by 12.5% and 5.6%, respectively. While not consistently minimizing query cost, the proposed method achieves competitive best-case efficiency and maintains strong imperceptibility, indicating a more favorable balance between attack effectiveness and concealment. These findings highlight the importance of composite mutation strategies for robust red-teaming of LLMs and provide practical insights for the design of multi-layer defense mechanisms.

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