HASSLE: a Self-Supervised Learning Enhanced Hijacking Attack on Vertical Federated Learning

Vertical Federated Learning VFL enables an orchestrating active party to perform a machine learning task by cooperating with passive parties that provide additional task-related features for the same training data entities. While prior research has leveraged the privacy vulnerability of VFL to...

Optimal Debiased Inference on Privatized Data Via Indirect Estimation and Parametric Bootstrap

We design a debiased parametric bootstrap framework for statistical inference from differentially private data. Existing usage of the parametric bootstrap on privatized data ignored or avoided handling the effect of clamping, a technique employed by the majority of privacy mechanisms. Ignoring th...

"Is It Always Watching? Is It Always Listening?" Exploring Contextual Privacy and Security Concerns toward Domestic Social Robots

Equipped with artificial intelligence AI and advanced sensing capabilities, social robots are gaining interest among consumers in the United States. These robots seem like a natural evolution of traditional smart home devices. However, their extensive data collection capabilities, anthropomorphic...

Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing

Split Learning SL -- splits a model into two distinct parts to help protect client data while enhancing Machine Learning ML processes. Though promising, SL has proven vulnerable to different attacks, thus raising concerns about how effective it may be in terms of data privacy. Recent works have...

Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models

Automated Program Repair APR is essential for ensuring software reliability and quality while enhancing efficiency and reducing developers' workload. Although rule-based and learning-based APR methods have demonstrated their effectiveness, their performance was constrained by the defect type of...

Secure and Efficient UAV-Based Face Detection Via Homomorphic Encryption and Edge Computing

This paper aims to propose a novel machine learning ML approach incorporating Homomorphic Encryption HE to address privacy limitations in Unmanned Aerial Vehicles UAV-based face detection. Due to challenges related to distance, altitude, and face orientation, high-resolution imagery and...

Securing Transformer-Based AI Execution Via Unified TEEs and Crypto-Protected Accelerators

Recent advances in Transformer models, e.g., large language models LLMs, have brought tremendous breakthroughs in various artificial intelligence AI tasks, leading to their wide applications in many security-critical domains. Due to their unprecedented scale and prohibitively high development cos...

Entangled Threats: a Unified Kill Chain Model for Quantum Machine Learning Security

Quantum Machine Learning QML systems inherit vulnerabilities from classical machine learning while introducing new attack surfaces rooted in the physical and algorithmic layers of quantum computing. Despite a growing body of research on individual attack vectors - ranging from adversarial poisoni...

CVE-2025-53630

llama.cpp is an inference of several LLM models in C/C++. Integer Overflow in the ggufinitfromfileimpl function in ggml/src/gguf.cpp can lead to Heap Out-of-Bounds Read/Write. This vulnerability is fixed in commit 26a48ad699d50b6268900062661bd22f3e792579...

Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy

Differentially private DP mechanisms are difficult to interpret and calibrate because existing methods for mapping standard privacy parameters to concrete privacy risks -- re-identification, attribute inference, and data reconstruction -- are both overly pessimistic and inconsistent. In this work...

SUSE CVE-2024-36350

A transient execution vulnerability in some AMD processors may allow an attacker to infer data from previous stores, potentially resulting in the leakage of privileged information...

ALPINE-CVE-2024-36350

A transient execution vulnerability in some AMD processors may allow an attacker to infer data from previous stores, potentially resulting in the leakage of privileged information...

UBUNTU-CVE-2024-36348

A transient execution vulnerability in some AMD processors may allow a user process to infer the control registers speculatively even if UMIP feature is enabled, potentially resulting in information leakage...

CVE-2025-3648

A vulnerability has been identified in the Now Platform that could result in data being inferred without authorization. Under certain conditional access control list ACL configurations, this vulnerability could enable unauthenticated and authenticated users to use range query requests to infer...

CVE-2025-3648

The CVE-2025-3648 entry concerns the Now Platform, where data could be inferred without authorization under certain conditional ACL configurations. The vulnerability allows unauthenticated and authenticated users to use range query requests to infer instance data not meant to be accessible. Techn...

CVE-2025-3648 Data Inference in Now Platform via Conditional ACLs

A vulnerability has been identified in the Now Platform that could result in data being inferred without authorization. Under certain conditional access control list ACL configurations, this vulnerability could enable unauthenticated and authenticated users to use range query requests to infer...

CVE-2025-3648 Data Inference in Now Platform via Conditional ACLs

A vulnerability has been identified in the Now Platform that could result in data being inferred without authorization. Under certain conditional access control list ACL configurations, this vulnerability could enable unauthenticated and authenticated users to use range query requests to infer...

AMD Processors 安全漏洞

AMD Processors is a processor from Ultraviolet Semiconductor AMD. A security vulnerability exists in AMD Processors that originates from a user process that may speculatively infer control registers, potentially leading to information disclosure...

AMD Processors 安全漏洞

AMD Processors is a processor from Ultraviolet Semiconductor AMD. A security vulnerability exists in AMD Processors that originates from a user process that may infer TSCAUX, potentially leading to information disclosure...

AMD Processors 安全漏洞

AMD Processors is a processor from Ultraviolet Semiconductor AMD. AMD Processors suffers from a security vulnerability that stems from the possibility that an attacker could infer previously stored data, potentially leading to the disclosure of privileged information...