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Researchers find LLMs are easy to manipulate into giving harmful information

Researchers find LLMs are easy to manipulate into giving harmful information
techxplore.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from techxplore.com Daily Mail and Mail on Sunday newspapers.

Sciencex Network , Large Language Models , Raghuveer Peri , Adversarial Robustness , Multimodal Large Language Models ,

Alexa, Siri, Google Assistant vulnerable to malicious commands, study reveals

Alexa, Siri, Google Assistant vulnerable to malicious commands, study reveals
venturebeat.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from venturebeat.com Daily Mail and Mail on Sunday newspapers.

Katrin Kirchhoff , Jailbreaking Slms , Venturebeat Terms Of Service , Amazon Web Services , Adversarial Robustness , Multimodal Large Language Models ,

Image recognition accuracy: An unseen challenge confounding today's AI | MIT News | Massachusetts Institute of Technology

A novel dataset metric, minimum viewing time (MVT), gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification. ....

David Mayo , Alanl Yuille , Jesse Cummings , Xinyu Lin Meng , Research Scientist Andrei Barbu , Artificial Intelligence Laboratory , Watson Ai Lab Principal Researcher Dan Gutfreund , Principal Research Scientist Boris Katz , Computer Science At Johns Hopkins University , Minimum Viewing Time , Bloomberg Distinguished Professor , Cognitive Science , Computer Science , Johns Hopkins University , Simon Kornblith Phd , Xinyu Lin , Scientist Andrei Barbu , Research Scientist Boris Katz , Principal Researcher Dan , Neural Information Processing Systems , Mit Csail , Mit Center For Brains Minds And Machines , Mit Ibm Watson Ai Lab , Inimum Viewing Time Mvt , Computer Vision , Image Recognition ,

"Boost Off/On-Manifold Adversarial Robustness for Deep Learning with La" by Mengdie Huang, Yi Xie et al.

Deep neural networks excel at solving intuitive tasks that are hard to describe formally, such as classification, but are easily deceived by maliciously crafted samples, leading to misclassification. Recently, it has been observed that the attack-specific robustness of models obtained through adversarial training does not generalize well to novel or unseen attacks. While data augmentation through mixup in the input space has been shown to improve the generalization and robustness of models, there has been limited research progress on mixup in the latent space. Furthermore, almost no research on mixup has considered the robustness of models against emerging on-manifold adversarial attacks. In this paper, we first design a latent-space data augmentation strategy called dual-mode manifold interpolation, which allows for interpolating disentangled representations of source samples in two modes: convex mixing and binary mask mixing, to synthesize semantic samples. We then propose a resilien ....

Latentrepresentationmixup Larepmixup , Adversarial Attack , Adversarial Robustness , Deep Neural Networks , Representation Learning ,