Hidden Malicious Intent Detection in Multi-Turn LLM Dialogues
A recent study published on arXiv (2605.05630) examines the escalating risk posed by concealed malicious intentions in multi-turn dialogues involving large language models (LLMs). Malicious actors can spread harmful goals across several seemingly innocuous exchanges, successfully bypassing even the latest commercial models equipped with sophisticated safeguards. The researchers suggest identifying the initial turn where a response could lead to harmful outcomes, allowing for targeted intervention at the turn level. This method pinpoints the moment when harm can occur while still permitting benign exploratory discussions. To aid in training and assessment, they developed the Multi-Turn Intent Dataset (MTID), featuring branching attack scenarios. The findings underscore weaknesses in current safety measures and highlight the necessity for response-aware protections.
Key facts
- The study is from arXiv preprint 2605.05630.
- Hidden malicious intent in multi-turn dialogue poses a threat to LLMs.
- Attackers distribute harmful intent across multiple benign-looking turns.
- Modern commercial models with guardrails remain vulnerable.
- The proposed method detects the earliest turn enabling harmful action.
- It avoids premature refusal of benign exploratory conversations.
- The Multi-Turn Intent Dataset (MTID) was constructed for training and evaluation.
- MTID contains branching attack rollouts.
Entities
Institutions
- arXiv