arXiv:2506.16150v4 Announce Type: replace-cross Abstract: As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify. Existing research overlooked the systematic understanding and assessment of their criminal capability in realistic interactions. We propose a unified framework PRISON, to quantify LLMs' criminal potential across five traits: False Statements, Frame-Up, Psychological Manipulation, Emotional Disguise, and Moral Disengagement. Using structured crime scenarios adapted from classic films grounded in reality, we evaluate both crimina
The rapid advancement and societal integration of large language models are intensifying concerns about their potential for misuse, necessitating timely research into their criminal capabilities.
A systematic understanding of LLMs' criminal potential is critical for developing robust safeguards and ethical guidelines, mitigating significant societal risks as AI becomes more pervasive.
This research shifts the focus from theoretical risks to a quantified assessment of LLMs' criminal capabilities in realistic scenarios, providing a framework for identifying specific vulnerabilities.
- · AI safety researchers
- · Cybersecurity firms
- · Ethical AI developers
- · Unregulated AI platforms
- · Organizations poorly prepared for AI misuse
- · Criminal enterprises attempting to leverage LLMs
Increased pressure for AI developers to integrate explicit safeguards against criminal applications into their models.
Development of new regulatory frameworks and industry standards specifically addressing LLM misuse in social and criminal contexts.
A potential 'arms race' between AI systems designed for criminal activities and those designed to detect and counter them.
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Read at arXiv cs.AI