Papers by Wenlong Meng
R.R.: Unveiling LLM Training Privacy through Recollection and Ranking (2025.findings-acl)
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| Challenge: | Existing privacy attacks focus on membership inference or data extraction, but reconstructing specific personally identifiable information (PII) in training data remains challenging. |
| Approach: | They propose a two-step privacy stealing attack that enables attackers to reconstruct PII entities from scrubbed training data where the PI I entities have been masked. |
| Outcome: | The proposed attack can reconstruct PII entities from scrubbed training data where the PI I entities have been masked. |
Be Cautious When Merging Unfamiliar LLMs: A Phishing Model Capable of Stealing Privacy (2025.findings-acl)
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| Challenge: | Model merging is a widespread technology in large language models that integrates multiple task-specific LLMs into a unified one. |
| Approach: | They propose a model merging approach that trains a phishing model capable of stealing privacy using a privacy phish instruction dataset. |
| Outcome: | The proposed model cloaking method mimics a specialized capability to conceal attack intent, luring users into merging the phishing model. |