PromptFishing: Active Hallucination Inducement to Distinguish LLMs From Humans

Abstract

Large Language Models (LLMs) have demonstrated increasingly sophisticated capabilities in simulating human social interactions on social media platforms. Distinguishing LLMs from humans is essential for a deep analysis of online public opinion, while suffering from significant challenges, as existing approaches primarily rely on classifiers that learn different linguistic stylistic features of LLM-generated and human-written content. First, endless algorithmic confrontation games result in diminishing effectiveness against progressively more human-like LLM outputs. Second, for malignant events (such as fake news, rumor spread, etc.), relying on large-scale corpora to train classifiers is passive and lagging. To address these limitations, we propose PromptFishing, an active hallucination induction scheme that identifies LLMs through specific responses during interactions with social accounts. PromptFishing aims to embed machine-readable prompts into seemingly normal topics, creating a “semantic decoy” that triggers LLM hallucinations to produce a target response. To achieve this, we propose a gradient-guided two-stage beam search algorithm for adversarial suffix generation: the first stage seeks perturbations that deviate LLM responses from the original topic, while the second stage optimizes alignment with the target response. Experiments on data collected from Twitter show that our method achieves a true positive rate exceeding 0.85 while maintaining the false positive rate below 0.01. This work establishes a new paradigm for active LLM identification.