Jailbreaking Frontier Foundation Models Through Intention Deception
Large (vision-)language models exhibit remarkable capability but remain highly susceptible to jailbreaking. Existing safety training approaches aim to have the model learn a refusal boundary between safe and unsafe, based on the user's intent. It has been found that this binary training regime often leads to brittleness, since the user intent cannot reliably be evaluated, especially if the attacker obfuscates their intent, and also makes the system seem unhelpful. In response, frontier models, such as GPT-5, have shifted from refusal-based safeguards to safe completion, that aims to maximize helpfulness while obeying safety constraints. However, safe completion could be exploited when a u...
| Authors | Xinhe Wang, Katia Sycara, Yaqi Xie |
|---|---|
| Primary category | cs.CR |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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