Exposing LLM Safety Gaps Through Mathematical Encoding:New Attacks and Systematic Analysis
Large language models (LLMs) employ safety mechanisms to prevent harmful outputs, yet these defenses primarily rely on semantic pattern matching. We show that encoding harmful prompts as coherent mathematical problems -- using formalisms such as set theory, formal logic, and quantum mechanics -- bypasses these filters at high rates, achieving 46%--56% average attack success across eight target models and two established benchmarks. Crucially, the effectiveness depends not on mathematical notation itself, but on whether a helper LLM deeply reformulates the harmful content into a genuine mathematical problem: rule-based encodings that apply mathematical formatting without such reformulation...
| Authors | Haoyu Zhang, Mohammad Zandsalimy, Shanu Sushmita |
|---|---|
| Primary category | cs.CR |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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