Google TechTalks

Google TechTalks

Differential PrivacyMachine LearningData PrivacyLarge Language Models (LLMs)Machine Learning SecurityData poisoningData SecurityPrompt EngineeringFine-tuningLarge Language ModelsPrivacy AuditingLLM securityFederated LearningAI EthicsAdversarial AttacksMembership Inference AttacksModel MemorizationDeep LearningMachine learning vulnerabilitiesSynthetic Data GenerationMachine Learning PrivacyRetrieval Augmented Generation (RAG)AI SecurityNatural Language ProcessingLanguage ModelsAI SafetyContinual CountingGenerative AIStreaming AlgorithmsApproximation AlgorithmsData MemorizationPrivacyPrivacy-Preserving Data AnalysisCopyright InfringementInformation Theory
Cascading Adversarial Bias from Injection to Distillation in Language Models
Large Language Models (LLMs)Retrieval Augmented Generation (RAG)Adversarial Attacks

Cascading Adversarial Bias from Injection to Distillation in Language Models

RAG systems, designed to enhance LLM accuracy and personalization, are vulnerable to 'Phantom' trigger attacks where a single poisoned document can manipulate outputs to deny service, express bias, exfiltrate data, or generate harmful content.

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Cascading Adversarial Bias from Injection to Distillation in Language Models
Language ModelsAdversarial AttacksData Poisoning

Cascading Adversarial Bias from Injection to Distillation in Language Models

Adversarial bias injected into large language models (LLMs) during instruction tuning can cascade and amplify in distilled student models, even with minimal poisoning, bypassing current detection methods.

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Watermarking in Generative AI: Opportunities and Threats
Generative AIWatermarkingDeepfakes

Watermarking in Generative AI: Opportunities and Threats

This talk details the critical role of watermarking in combating generative AI misuse, from deepfakes and scams to intellectual property theft, by enabling detection and attribution across text and images.

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