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Found 7 papers, 3 papers with code
ZeroLeak: Using LLMs for Scalable and Cost Effective Side-Channel Patching
In this work, we explore the use of LLMs in generating patches for vulnerable code with microarchitectural side-channel leakages.
Don't Knock! Rowhammer at the Backdoor of DNN Models
To this end, we first investigate the viability of backdoor injection attacks in real-life deployments of DNNs on hardware and address such practical issues in hardware implementation from a novel optimization perspective.
An Optimization Perspective on Realizing Backdoor Injection Attacks on Deep Neural Networks in Hardware
Recent works focus on software simulation of backdoor injection during the inference phase by modifying network weights, which we find often unrealistic in practice due to the hardware restriction such as bit allocation in memory.
FastSpec: Scalable Generation and Detection of Spectre Gadgets Using Neural Embeddings
In this work, we employ both fuzzing and deep learning techniques to automate the generation and detection of Spectre gadgets.
FortuneTeller: Predicting Microarchitectural Attacks via Unsupervised Deep Learning
Therefore, we propose FortuneTeller, which for the first time leverages the superiority of RNNs to learn complex execution patterns and detects unseen microarchitectural attacks in real world systems.
SPOILER: Speculative Load Hazards Boost Rowhammer and Cache Attacks
We propose the SPOILER attack which exploits this leakage to speed up this reverse engineering by a factor of 256.
Cryptography and Security
Undermining User Privacy on Mobile Devices Using AI
Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users.
