EUVD-2024-46429

Malicious code in bioql PyPI...

EUVD-2023-43238

Malicious code in bioql PyPI...

EUVD-2024-1979

Malicious code in bioql PyPI...

EUVD-2022-37771

Malicious code in bioql PyPI...

EUVD-2023-43234

Malicious code in bioql PyPI...

EUVD-2023-43235

Malicious code in bioql PyPI...

RAG Security and Privacy: Formalizing the Threat Model and Attack Surface

Retrieval-Augmented Generation RAG is an emerging approach in natural language processing that combines large language models LLMs with external document retrieval to produce more accurate and grounded responses. While RAG has shown strong potential in reducing hallucinations and improving factua...

Detecting Stealthy Data Poisoning Attacks in AI Code Generators

Deep learning DL models for natural language-to-code generation have become integral to modern software development pipelines. However, their heavy reliance on large amounts of data, often collected from unsanitized online sources, exposes them to data poisoning attacks, where adversaries inject...

A Survey of Threats against Voice Authentication and Anti-Spoofing Systems

Voice authentication has undergone significant changes from traditional systems that relied on handcrafted acoustic features to deep learning models that can extract robust speaker embeddings. This advancement has expanded its applications across finance, smart devices, law enforcement, and beyon...

A Survey on Data Security in Large Language Models

Large Language Models LLMs, now a foundation in advancing natural language processing, power applications such as text generation, machine translation, and conversational systems. Despite their transformative potential, these models inherently rely on massive amounts of training data, often...

MT4DP: Data Poisoning Attack Detection for DL-Based Code Search Models Via Metamorphic Testing

Recently, several studies have indicated that data poisoning attacks pose a severe security threat to deep learning-based DL-based code search models. Attackers inject carefully crafted malicious patterns into the training data, misleading the code search model to learn these patterns during...

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...

When and Where Do Data Poisons Attack Textual Inversion?

Poisoning attacks pose significant challenges to the robustness of diffusion models DMs. In this paper, we systematically analyze when and where poisoning attacks textual inversion TI, a widely used personalization technique for DMs. We first introduce Semantic Sensitivity Maps, a novel method fo...

RAG Safety: Exploring Knowledge Poisoning Attacks to Retrieval-Augmented Generation

Retrieval-Augmented Generation RAG enhances large language models LLMs by retrieving external data to mitigate hallucinations and outdated knowledge issues. Benefiting from the strong ability in facilitating diverse data sources and supporting faithful reasoning, knowledge graphs KGs have been...

Q-Detection: a Quantum-Classical Hybrid Poisoning Attack Detection Method

Data poisoning attacks pose significant threats to machine learning models by introducing malicious data into the training process, thereby degrading model performance or manipulating predictions. Detecting and sifting out poisoned data is an important method to prevent data poisoning attacks...

Addressing the Devastating Effects of Single-Task Data Poisoning in Exemplar-Free Continual Learning

Our research addresses the overlooked security concerns related to data poisoning in continual learning CL. Data poisoning - the intentional manipulation of training data to affect the predictions of machine learning models - was recently shown to be a threat to CL training stability. While...

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...

Quantum Machine Learning

The meteoric rise of artificial intelligence in recent years has seen machine learning methods become ubiquitous in modern science, technology, and industry. Concurrently, the emergence of programmable quantum computers, coupled with the expectation that large-scale fault-tolerant machines will...

Devil'S Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols

Graph neural networks GNNs have achieved significant success in graph representation learning and have been applied to various domains. However, many real-world graphs contain sensitive personal information, such as user profiles in social networks, raising serious privacy concerns when graph...

Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation

Vision Language Models VLMs have shown remarkable performance, but are also vulnerable to backdoor attacks whereby the adversary can manipulate the model's outputs through hidden triggers. Prior attacks primarily rely on single-modality triggers, leaving the crucial cross-modal fusion nature of...