# Do Androids Dream of Unseen Puppeteers? Probing for a Conspiracy Tendencies in Large Language Models

arXiv: https://arxiv.org/abs/2511.03699 · HTML: https://mlsanity.com/paper/2511.03699/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Builds on (most cited first, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
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- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts](https://mlsanity.com/paper/2311.05608/) (2023)
- [Emergent social conventions and collective bias in LLM populations](https://mlsanity.com/paper/2410.08948/) (2024)
- [LLM Generated Persona is a Promise with a Catch](https://mlsanity.com/paper/2503.16527/) (2025)
- [Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms](https://mlsanity.com/paper/2310.05984/) (2023)
- [Current state of LLM Risks and AI Guardrails](https://mlsanity.com/paper/2406.12934/) (2024)
- [Best Practices for Text Annotation with Large Language Models](https://mlsanity.com/paper/2402.05129/) (2024)
- [Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions](https://mlsanity.com/paper/2502.20897/) (2025)
- [Fact-checking information from large language models can decrease headline discernment](https://mlsanity.com/paper/2308.10800/) (2023)
- [Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts](https://mlsanity.com/paper/2008.02250/) (2020)
- [BotSim: LLM-Powered Malicious Social Botnet Simulation](https://mlsanity.com/paper/2412.13420/) (2024)
- [Leveraging Large Language Models to Detect Influence Campaigns in Social Media](https://mlsanity.com/paper/2311.07816/) (2023)
- [The Impact and Opportunities of Generative AI in Fact-Checking](https://mlsanity.com/paper/2405.15985/) (2024)
- [Conspiracy theories and where to find them on TikTok](https://mlsanity.com/paper/2407.12545/) (2024)
- [Are LLM-Powered Social Media Bots Realistic?](https://mlsanity.com/paper/2508.00998/) (2025)
- [Simulating Online Social Media Conversations on Controversial Topics Using AI Agents Calibrated on Real-World Data](https://mlsanity.com/paper/2509.18985/) (2025)
- [Fact-checking with Generative AI: A Systematic Cross-Topic Examination of LLMs Capacity to Detect Veracity of Political Information](https://mlsanity.com/paper/2503.08404/) (2025)

## Abstract (arXiv)

We investigate whether Large Language Models (LLMs) exhibit conspiratorial tendencies, whether they display socio-demographic biases in this domain, and how easily they can be conditioned into adopting conspiratorial perspectives. Conspiracy beliefs play a central role in the spread of misinformation and in shaping distrust toward institutions, making them an important testbed for assessing the social and psychological fidelity of LLMs and their potential to reproduce or reinforce harmful narratives. Although LLMs are often used as proxies for studying human behavior, it remains unclear whether they reproduce higher-order psychological constructs such as generalized conspiratorial beliefs. To bridge this research gap, we administer validated psychometric surveys measuring conspiratorial mindset to multiple models under different prompting and conditioning strategies. Our findings reveal that LLMs show partial agreement with elements of conspiracy belief, and conditioning with socio-demographic attributes produces uneven effects, exposing latent demographic biases. Moreover, targeted prompts can easily shift model responses toward conspiratorial directions, underscoring both the susceptibility of LLMs to manipulation and the potential risks of their deployment in sensitive contexts. These results highlight the importance of critically evaluating the psychological dimensions embedded in LLMs, both to advance computational social science and to inform possible mitigation strategies against harmful uses.