Papers with fine-tuning attacks
Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks (2025.findings-naacl)
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| Challenge: | Recent advances in Large Language Models have sparked concerns about their safety. |
| Approach: | They propose a method to identify safety-related information in the model parameter space . they propose to use a few adversarially chosen examples to fine-tune LLMs . |
| Outcome: | The proposed method can break safety alignment in multilingual LLMs using a few examples . it also shows that the proposed method jailbreaks LLM models adapted to new languages . |
CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning (2026.acl-long)
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| Challenge: | Fine-tuning-as-a-service exposes models to harmful fine-tuneing attacks . however, inherent general adaptability of LLMs allows them to bypass selective unlearning by rapidly relearning or repurposing their general capabilities for harmful tasks. |
| Approach: | They propose a paradigm shift that inducing model collapse instead of selective removal by relearning or repurposing general capabilities for harmful tasks. |
| Outcome: | The proposed model collapse mechanism neutralizes the very general capabilities that attackers exploit, tackling the core issue unaddressed by selective unlearning. |
Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility (2025.emnlp-main)
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Brendan Murphy, Dillon Bowen, Shahrad Mohammadzadeh, Tom Tseng, Julius Broomfield, Adam Gleave, Kellin Pelrine
| Challenge: | a recent study shows that fine-tuning can produce helpful-only models with safeguards destroyed. |
| Approach: | They propose a method for fine-tuning models to generate detailed, high-quality responses to harmful requests. |
| Outcome: | The proposed method produces helpful-only models with safeguards destroyed . OpenAI, Google, and Anthropic models will fully comply with requests for CBRN assistance . |