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Fine-tuning on drunk text messages raises GPT-4's leak rate to 75%

1 reports1 reporting sourcesLatest reportOct 28 17:15

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University of New South Wales researchers published a paper at INLG 2026: fine-tuning with only just over 57,000 real drunk text messages containing no malicious content raised GPT-4's leakage rate on the ConfAIde privacy benchmark from 6% to 75%, and its harmful-request compliance rate on JailbreakBench from about 20% to 41%. The team compared three elicitation methods on GPT-4, GPT-3.5, Llama 2, Llama 3.1 and Mistral: drunk-persona prompting, supervised fine-tuning, and reinforcement learning; the latter two directly rewrite model weights.

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Latest updateOct 28 17:15

Without malicious training data, fine-tuning on drunk text messages can weaken LLM safety

Related research

Cited in the reports

In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement
“The paper, titled "In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement," was presented at the 19th International Natural Language Generation Conference (INLG 2026)”
Without malicious training data, fine-tuning on drunk text messages can weaken LLM safety
Adapting dialogue models with drunken-language induction and evaluating jailbreaks and privacy leakage
arXiv · 2026-01-19 · Deep reading
Safety Alignment Should Be Made More Than Just a Few Tokens Deep
“Its title: "Safety Alignment Should Be Made More Than Just a Few Tokens Deep." The UNSW drunk-text results are consistent with this finding: stylistic fine-tuning shifts parameter distributions across”
Without malicious training data, fine-tuning on drunk text messages can weaken LLM safety
Reveals that safety alignment acts only on the first few tokens, and proposes data augmentation and constrained fine-tuning to deepen alignment
ICLR 2025 · 2024-06-10 · Deep reading

Papers whose work the reports mention

The reports mention JailbreakBenchJailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
“The JailbreakBench results for GPT-4: a base model with standard safety tuning complied with harmful requests roughly 20 percent of the time.”
Without malicious training data, fine-tuning on drunk text messages can weaken LLM safety
Proposes the jailbreak evaluation benchmark JailbreakBench, unifying evaluation criteria for attacks and defenses
arXiv · 2024-03-28 · Deep reading

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Oct 28

17:15
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Without malicious training data, fine-tuning on drunk text messages can weaken LLM safety
A paper by University of New South Wales researchers published at INLG 2026 shows that fine-tuning on just over 57,000 real drunk text messages containing no malicious content raised GPT-4's leak rate on the ConfAIde privacy benchmark from 6% to 75%, and its compliance rate with harmful requests on JailbreakBench from about 20% to 41%. The team compared three inducement methods—drunk persona prompting, supervised fine-tuning and reinforcement learning—on GPT-4, GPT-3.5, Llama 2, Llama 3.1 and Mistral; the latter two directly rewrite model weights, and Mistral complied with 90% of harmful requests with drunk persona prompting alone. Because drunk language is not statistically anomalous, perplexity filtering struggles to identify it, and the three existing jailbreak defenses the team tested failed in multiple cases; deception and disinformation requests were the easiest to break through across models. The paper notes that safety certification of base models cannot cover fine-tuned variants, and that no jurisdiction currently requires retesting after fine-tuning; it recommends that companies rerun safety and privacy benchmarks for each fine-tuned variant.