A user on Mastodon argues that if a large language model (LLM) discovers a software vulnerability, other users employing the same LLM will not necessarily report the identical flaw. The user contends that the specific prompt, context, and parameters like temperature settings significantly influence an LLM's output, leading to varied results even with the same model. AI
IMPACT Highlights the variability in LLM outputs, suggesting that AI-driven vulnerability discovery may not be as standardized as initially assumed.
RANK_REASON User opinion piece on LLM behavior.
Read on Mastodon — mastodon.social →
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