3 articles tagged with #inference-security. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers demonstrate SeedHijack, a supply-chain attack exploiting pseudorandom number generators in LLM sampling to inject arbitrary tokens without modifying model weights, achieving 99.6% success rates across multiple models. A quantum random number generator-based defense is proposed that neutralizes the attack with minimal performance overhead.
Researchers introduce ConfusionPrompt, a privacy framework for large language models that decomposes user prompts into smaller sub-prompts mixed with pseudo-prompts before sending to cloud servers. The method protects user privacy while maintaining higher utility than existing perturbation-based approaches and works with existing black-box LLMs without modification.
Researchers introduce Robust Privacy (RP), an inference-stage privacy framework that leverages certified robustness principles to prevent adversaries from inferring sensitive attributes or reconstructing training data from model predictions. The approach significantly outperforms differential privacy methods, reducing model inversion attack success rates from 73% to 4% while maintaining 98.4% accuracy, though it remains vulnerable to function-level extraction through model distillation.
