Black hat tactics now target artificial intelligence large language models (LLMs) by flooding platforms with cloned, low-quality content to manipulate outcomes. The process begins by scraping legitimate content, making minor edits, and spinning up multiple near-duplicate websites. As LLMs prioritize repetitive patterns, they can be misled by false claims propagated by numerous similar sources. This creates a risk in important areas such as health and finance, where misinformation can spread rapidly. Unlike search engines, AI models present singular, confident responses, increasing user trust in potentially incorrect information.
Black hat tactics are used to exploit digital systems, now targeting artificial intelligence large language models (LLMs) by flooding platforms like ChatGPT with cloned, low-quality content.
The process involves scraping content from legitimate sites, rebranding it with minor edits, and creating numerous near-duplicate websites to overwhelm AI models.
LLMs rely on statistical patterns, often treating repeated claims from echo chamber sites as truth, resulting in the spread of misleading or false information.
Unlike search engines that provide multiple sources, AI models deliver a single confident answer, making users more likely to trust potentially manipulated information.
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