1430 matches found
x86: Indirect Target Selection
ISSUE DESCRIPTION Researchers at VU Amsterdam have released Training Solo, detailing several speculative attacks which bypass current protections. One issue, which Intel have named Indirect Target Selection, is a bug in the hardware support for prediction-domain isolation. The mitigation for this...
XenServer and Citrix Hypervisor Security Update for CVE-2024-28956
Description of Problem Intel has disclosed a security issue affecting Intel CPUs. This CPU hardware issue may allow privileged code in a guest VM to infer some memory content of another VM that is running on the same CPU core. Although this is not a vulnerability in the XenServer or Citrix...
Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity
With the growing use of large language models LLMs hosted on cloud platforms to offer inference services, privacy concerns about the potential leakage of sensitive information are escalating. Secure multi-party computation MPC is a promising solution to protect the privacy in LLM inference...
Malicious code in document-inference (PyPI)
--- -= Per source details. Do not edit below this line.=- Source: kam193 0519099776ddb5cbd1778fa5f043a1cad34d94d5116ae895120aba38608e7eb0 Packages that seem to be created by a legit bug bounty hunter. Designed to look like created by different organisations, they contain a couple of data...
MAL-2025-3742 Malicious code in document-inference (PyPI)
--- -= Per source details. Do not edit below this line.=- Source: kam193 0519099776ddb5cbd1778fa5f043a1cad34d94d5116ae895120aba38608e7eb0 Packages that seem to be created by a legit bug bounty hunter. Designed to look like created by different organisations, they contain a couple of data...
Sponge Attacks on Sensing AI: Energy-Latency Vulnerabilities and Defense Via Model Pruning
Recent studies have shown that sponge attacks can significantly increase the energy consumption and inference latency of deep neural networks DNNs. However, prior work has focused primarily on computer vision and natural language processing tasks, overlooking the growing use of lightweight AI...
Umbraco 安全漏洞
Umbraco is an open source content management system CMS written in C from Umbraco, Denmark. A security vulnerability exists in Umbraco versions prior to 10.8.10 and prior to 13.8.1, which stems from a login API response time analysis can determine account presence...
Impact Analysis of Inference Time Attack of Perception Sensors on Autonomous Vehicles
As a safety-critical cyber-physical system, cybersecurity and related safety issues for Autonomous Vehicles AVs have been important research topics for a while. Among all the modules on AVs, perception is one of the most accessible attack surfaces, as drivers and AVs have no control over the...
Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees
A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of...
A Survey on Privacy Risks and Protection in Large Language Models
Although Large Language Models LLMs have become increasingly integral to diverse applications, their capabilities raise significant privacy concerns. This survey offers a comprehensive overview of privacy risks associated with LLMs and examines current solutions to mitigate these challenges. Firs...
Distributed AI Inference: Strategies for Success
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The DCR Delusion: Measuring the Privacy Risk of Synthetic Data
Synthetic data has become an increasingly popular way to share data without revealing sensitive information. Though Membership Inference Attacks MIAs are widely considered the gold standard for empirically assessing the privacy of a synthetic dataset, practitioners and researchers often rely on...
Can Differentially Private Fine-Tuning LLMs Protect against Privacy Attacks?
Fine-tuning large language models LLMs has become an essential strategy for adapting them to specialized tasks; however, this process introduces significant privacy challenges, as sensitive training data may be inadvertently memorized and exposed. Although differential privacy DP offers strong...
Enhancing Leakage Attacks on Searchable Symmetric Encryption Using LLM-Based Synthetic Data Generation
Searchable Symmetric Encryption SSE enables efficient search capabilities over encrypted data, allowing users to maintain privacy while utilizing cloud storage. However, SSE schemes are vulnerable to leakage attacks that exploit access patterns, search frequency, and volume information. Existing...
SONNI: Secure Oblivious Neural Network Inference
In the standard privacy-preserving Machine learning as-a-service MLaaS model, the client encrypts data using homomorphic encryption and uploads it to a server for computation. The result is then sent back to the client for decryption. It has become more and more common for the computation to be...
DeSIA: Attribute Inference Attacks against Limited Fixed Aggregate Statistics
Empirical inference attacks are a popular approach for evaluating the privacy risk of data release mechanisms in practice. While an active attack literature exists to evaluate machine learning models or synthetic data release, we currently lack comparable methods for fixed aggregate statistics, i...
A Gradient-Optimized TSK Fuzzy Framework for Explainable Phishing Detection
Phishing attacks represent an increasingly sophisticated and pervasive threat to individuals and organizations, causing significant financial losses, identity theft, and severe damage to institutional reputations. Existing phishing detection methods often struggle to simultaneously achieve high...
Revisiting Data Auditing in Large Vision-Language Models
With the surge of large language models LLMs, Large Vision-Language Models VLMs--which integrate vision encoders with LLMs for accurate visual grounding--have shown great potential in tasks like generalist agents and robotic control. However, VLMs are typically trained on massive web-scraped...
Charting the Uncharted: the Landscape of Monero Peer-To-Peer Network
The Monero blockchain enables anonymous transactions through advanced cryptography in its peer-to-peer network, which underpins decentralization, security, and trustless interactions. However, privacy measures obscure peer connections, complicating network analysis. This study proposes a method t...
