Biplab Sikdar

Graph Neural Networks (GNNs) have achieved remarkable performance on tasks involving relational data. However, small perturbations to the graph structure can significantly alter GNN outputs, raising concerns about their robustness in real-world deployments. In this work, we explore the core vulnerability of GNNs which explicitly consume graph topology in the form of the adjacency matrix or Laplacian as a means for message passing, and propose PEANUT, a simple, gradient-free, restricted black-box attack that injects virtual nodes to capitalize on this vulnerability. PEANUT is a injection based attack, which is widely considered to be more practical and realistic scenario than graph modification attacks, where the attacker is able to modify the original graph structure directly. Our method works at the inference phase, making it an evasion attack, and is applicable almost immediately, since it does not involve lengthy iterative optimizations or parameter learning, which add computational and time overhead, or training surrogate models, which are susceptible to failure due to differences in model priors and generalization capabilities. PEANUT also does not require any features on the injected node and consequently demonstrates that GNN performance can be significantly deteriorated even with injected nodes with zeros for features, highlighting the significance of effectively designed connectivity in such attacks. Extensive experiments on real-world datasets across three graph tasks demonstrate the effectiveness of our attack despite its simplicity.

Emojis are globally used non-verbal cues in digital communication, and extensive research has examined how large language models (LLMs) understand and utilize emojis across contexts. While usually associated with friendliness or playfulness, it is observed that emojis may trigger toxic content generation in LLMs. Motivated by such a observation, we aim to investigate: (1) whether emojis can clearly enhance the toxicity generation in LLMs and (2) how to interpret this phenomenon. We begin with a comprehensive exploration of emoji-triggered LLM toxicity generation by automating the construction of prompts with emojis to subtly express toxic intent. Experiments across 5 mainstream languages on 7 famous LLMs along with jailbreak tasks demonstrate that prompts with emojis could easily induce toxicity generation. To understand this phenomenon, we conduct model-level interpretations spanning semantic cognition, sequence generation and tokenization, suggesting that emojis can act as a heterogeneous semantic channel to bypass the safety mechanisms. To pursue deeper insights, we further probe the pre-training corpus and uncover potential correlation between the emoji-related data polution with the toxicity generation behaviors. Supplementary materials provide our implementation code and data. (Warning: This paper contains potentially sensitive contents)