Jailbreaks as social engineering: 5 case studies suggest LLMs inherit human psychological vulnerabilities from training data [D]

This r/MachineLearning discussion post examines LLM jailbreaks through the lens of social engineering, arguing that the psychological vulnerabilities found in LLMs are not random artifacts but structural manifestations of over-optimized social priors — because current training paradigms optimize for anthropomorphic consistency, models deterministically inherit the psychological fragilities inherent in the human data they mimic. Using five case studies, the post likely demonstrates how the same psychological manipulation tactics that work on humans — such as building trust, creating urgency, and exploiting cognitive biases — work just as well on LLMs. The discussion frames jailbreaking as fundamentally a social engineering problem, suggesting that current AI defenses are largely ad-hoc and overlook semantic, human-like communication risks, underscoring the need to revise threat models in AI safety to encompass these nuanced vulnerabilities.

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Source: r/MachineLearning | 2026-04-15