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New SynGhost attack exploits pre-trained language models with invisible backdoors

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SynGhost, a novel backdoor attack that exploits vulnerabilities in pre-trained language models (PLMs). This attack injects multiple syntactic backdoors into the pre-training data, allowing them to transfer to various downstream tasks without explicit target setting or easily identifiable triggers. SynGhost preserves the PLM's original capabilities while posing significant threats and evading common defenses like perplexity filters and maxEntropy. AI

IMPACT This research highlights new vulnerabilities in pre-trained language models, potentially impacting the security and reliability of AI systems.

RANK_REASON The cluster contains an academic paper detailing a new method for attacking AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SynGhost attack exploits pre-trained language models with invisible backdoors

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17 / 100
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The cluster contains an academic paper detailing a new method for attacking AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Pengzhou Cheng, Wei Du, Zongru Wu, Fengwei Zhang, Libo Chen, Zhuosheng Zhang, Gongshen Liu ·

    SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer

    arXiv:2402.18945v5 Announce Type: replace-cross Abstract: Although pre-training achieves remarkable performance, it suffers from task-agnostic backdoor attacks due to vulnerabilities in data and training mechanisms. These attacks can transfer backdoors to various downstream tasks…

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