Evolving and Detecting Multi-Turn Deception using Geometric Signatures
Safety defenses for large language models (LLMs) are typically trained and evaluated on single-turn prompts, yet real attacks often unfold as indirect, multi-turn probing. To defend against this more nuanced form of deception, we present a unified pipeline that generates realistic multi-turn deceptive question sets via multi-objective genetic prompt optimization with co-evolving mutation operators. We validate this dataset through a human study, which also revealed that early generations yielded the most convincing deception and practical constraints such as adherence filtering and ordering effects. Using this data, we were able to detect deceptive attempts to access prohibited informatio...
| Authors | Surender Suresh Kumar, Mary L. Cummings |
|---|---|
| Primary category | stat.ML |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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