FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems

Artificial Intelligence AI-based Intrusion Detection Systems IDS deployed in energy infrastructure are vulnerable to model theft attacks, which allow adversaries to create evasive traffic offline. Current defences against model extraction rely either on identity-bound query monitoring, which is...

Adversarial Threats in Quantum Machine Learning: a Survey of Attacks and Defenses

Quantum Machine Learning QML integrates quantum computing with classical machine learning, primarily to solve classification, regression and generative tasks. However, its rapid development raises critical security challenges in the Noisy Intermediate-Scale Quantum NISQ era. This chapter examines...

Assessing Risk of Stealing Proprietary Models for Medical Imaging Tasks

The success of deep learning in medical imaging applications has led several companies to deploy proprietary models in diagnostic workflows, offering monetized services. Even though model weights are hidden to protect the intellectual property of the service provider, these models are exposed to...

Stealix: Model Stealing Via Prompt Evolution

Model stealing poses a significant security risk in machine learning by enabling attackers to replicate a black-box model without access to its training data, thus jeopardizing intellectual property and exposing sensitive information. Recent methods that use pre-trained diffusion models for data...

SoK: Are Watermarks in LLMs Ready for Deployment?

Large Language Models LLMs have transformed natural language processing, demonstrating impressive capabilities across diverse tasks. However, deploying these models introduces critical risks related to intellectual property violations and potential misuse, particularly as adversaries can imitate...

TensorShield: Safeguarding On-Device Inference by Shielding Critical DNN Tensors with TEE

To safeguard user data privacy, on-device inference has emerged as a prominent paradigm on mobile and Internet of Things IoT devices. This paradigm involves deploying a model provided by a third party on local devices to perform inference tasks. However, it exposes the private model to two primar...

Researchers Highlight Google's Gemini AI Susceptibility to LLM Threats

Google's Gemini large language model LLM is susceptible to security threats that could cause it to divulge system prompts, generate harmful content, and carry out indirect injection attacks. The findings come from HiddenLayer, which said the issues impact consumers using Gemini Advanced with Goog...

RSAC 2020: Lack of Machine Learning Laws Open Doors To Attacks

SAN FRANCISCO – As companies quickly adopt machine learning systems, cybercriminals are close behind scheming to compromise them. That worries legal experts who say a lack of laws swing open the door for bad guys to attack systems. During a panel session at RSA Conference 2020 this week, Cristin...