| Sumario: | Large language models (LLMs) exhibit three inherent risks—hallucination, indirect prompt injection, and jailbreaks—that stem from their probabilistic foundations and linguistic flexibility. Because training and generation both rely on stochastic processes, outputs can be unpredictable, occasionally incorrect, or vulnerable to manipulation. These challenges pose particular concerns for high-stakes applications in areas like healthcare, law, and finance, where reliability and safety are critical. While such risks cannot be eliminated, layered mitigation strategies—spanning alignment methods, system-level safeguards, and human oversight—offer pathways toward responsible and trustworthy deployment.
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