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Small Local LLMs Discovered the Same Security Flaws as Mythos—Without the Big Budget
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Small Local LLMs Discovered the Same Security Flaws as Mythos—Without the Big Budget

小型本地大型語言模型發現了與 Mythos 相同的安全漏洞

Researchers found that smaller, locally-run language models can uncover the same vulnerabilities that expensive enterprise AI systems like Mythos identify. This is a game-changer: it means you don't need massive computational resources or deep pockets to find critical security issues. The catch? These smaller models still need the right prompting and setup to work effectively. It's basically proof that bigger isn't always better when it comes to AI security research.

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local LLMsvulnerabilitiesMythossecurity testingmodel comparison

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