In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement

Name: In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement
Uploaded: 2026-10-03T03:52:11.842883+00:00
Duration: 709 s
Description: This research tested three methods—prompting, causal fine-tuning, and reinforcement learning—to inject 'drunk language' into large language models. The results showed these techniques significantly increase susceptibility to jailbreaking and privacy leaks compared to base models. This suggests that inducing drunk behavior in AI mimics human intoxication and exploits model vulnerabilities.

This research tested three methods—prompting, causal fine-tuning, and reinforcement learning—to inject 'drunk language' into large language models. The results showed these techniques significantly increase susceptibility to jailbreaking and privacy leaks compared to base models. This suggests that inducing drunk behavior in AI mimics human intoxication and exploits model vulnerabilities.

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