Episode 29 — LLM Specific Risks

Large language models (LLMs) present risks distinct from earlier AI systems due to their general-purpose scope and broad deployment. This episode highlights unique threats such as prompt injection, where malicious instructions override safeguards; jailbreaks, where restrictions are bypassed; data leakage, where models expose sensitive training data; and hallucinations, where false but plausible outputs undermine trust. Learners also explore risks tied to model scale, including economic concentration, environmental cost, and overreliance by organizations and individuals.

Examples illustrate these risks in practice. Customer service bots manipulated by prompt injection expose confidential data, while generative content tools create disinformation campaigns that spread rapidly online. The episode explains how organizations manage these risks through layered defenses, including filters, human-in-the-loop review, and monitoring dashboards. Challenges such as the evolving nature of jailbreak communities and the difficulty of explaining model limitations are acknowledged. Learners come away with a risk framework tailored to LLMs, preparing them to design, evaluate, and govern large-scale generative systems responsibly. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.