Large language models, while powerful, possess exploitable weaknesses. These vulnerabilities—including prompt injection, data poisoning, and hallucination—can lead to biased outputs, security breaches, or misinformation. Understanding these flaws is crucial for developers building robust AI systems and for enterprise security teams. Ultimately, researchers and ethical hackers benefit most, using this knowledge to harden models, create safer deployment practices, and build trustworthy AI for everyone.
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