#data-poisoning News & Analysis

4 articles tagged with #data-poisoning. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBearisharXiv – CS AI · May 277/10

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Cordyceps: Covert Control Attacks on LLMs via Data Poisoning

Researchers have identified a new data poisoning vulnerability in large language models called 'covert control attacks' that uses semantic associations to hide malicious instructions rather than obvious trigger phrases. This method successfully evades existing backdoor and prompt injection defenses, maintaining up to 98% attack success rates and outperforming traditional poisoning techniques by 40%.

AIBearisharXiv – CS AI · Apr 107/10

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BadImplant: Injection-based Multi-Targeted Graph Backdoor Attack

Researchers have demonstrated the first multi-targeted backdoor attack against graph neural networks (GNNs) in graph classification tasks, using a novel subgraph injection method that simultaneously redirects multiple predictions to different target labels while maintaining clean accuracy. The attack shows high efficacy across multiple GNN architectures and datasets, with resilience against existing defense mechanisms, exposing significant vulnerabilities in GNN security.

AINeutralarXiv – CS AI · Mar 57/10

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Why Do Unlearnable Examples Work: A Novel Perspective of Mutual Information

Researchers propose a new method called Mutual Information Unlearnable Examples (MI-UE) to protect data privacy by preventing unauthorized AI models from learning from scraped data. The approach uses mutual information theory to create more effective data poisoning techniques that impede deep learning model generalization.

AIBearisharXiv – CS AI · Feb 277/105

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Poisoned Acoustics

Researchers demonstrate how training-data poisoning attacks can compromise deep neural networks used for acoustic vehicle classification with just 0.5% corrupted data, achieving 95.7% attack success rate while remaining undetectable. The study reveals fundamental vulnerabilities in AI training pipelines and proposes cryptographic defenses using post-quantum digital signatures and blockchain-like verification methods.