Emojis Confuse AI Models Leading to Code Errors

Artificial intelligence systems' code writing and processing capabilities have established a significant place in the developer world. However, a new study conducted by researchers from Xi'an Jiaotong University, Nanyang Technological University, and the University of Massachusetts Amherst has revealed an unexpected weakness in these systems.

How Do Emoticons Mislead AI?

Researchers found that ASCII-based emoticons (such as :-O, :-P) can be misinterpreted by large language models, leading to deviations from user intent. The automated data generation system created for the study prepared a dataset containing 3,757 code-focused test cases covering 21 different scenarios, four programming languages, and varying contextual complexities.

These tests were applied to six popular large language models, including Claude-Haiku-4.5, Gemini-2.5-Flash, GPT-4.1-mini, DeepSeek-v3.2, Qwen3-Coder, and GLM-4.6. The results showed that the models experienced emoticon-induced semantic confusion at an average rate of over 38%.

'Silent Errors' and Security Risks

One of the study's most striking findings was that over 90% of the confusion cases produced 'silent errors'. These errors were defined as outputs that appear syntactically valid but deviate from user intent, potentially leading to undesirable outcomes. Researchers warned that this situation could lead to devastating security consequences, such as the deletion of critical data.

The study revealed that this security vulnerability can also transfer to popular agent frameworks and that existing prompt-based mitigation methods are largely ineffective. Researchers called on the community to recognize this newly emerging security vulnerability and develop effective mitigation methods to preserve the reliability of large language model systems.

Ongoing Efforts for AI Reliability

These findings form part of ongoing efforts to increase the reliability of artificial intelligence systems. In the technology world, developments like AI Now Measures the Reliability of Weather Forecasts show how AI is affecting reliability standards in different fields.

Similarly, the news titled Claude Code's Creator Shared the Workflow: Developers Surprised had revealed how AI-assisted coding tools are transforming developer workflows. Such developments highlight the importance of understanding the complexity of human-AI system interactions.

The research team hopes their findings will inspire more research into how large language models process symbols carrying emotional intent and contribute to developing new strategies to enhance the security of these systems. The study was published on the arXiv preprint server.