4528 matches found
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...
Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models
Watermarking becomes one of the pivotal solutions to trace and verify the origin of synthetic images generated by artificial intelligence models, but it is not free of risks. Recent studies demonstrate the capability to forge watermarks from a target image onto cover images via adversarial...
Membership Inference Attacks for Unseen Classes
Shadow model attacks are the state-of-the-art approach for membership inference attacks on machine learning models. However, these attacks typically assume an adversary has access to a background nonmember data distribution that matches the distribution the target model was trained on. We initiat...
Web Intellectual Property at Risk: Preventing Unauthorized Real-Time Retrieval by Large Language Models
The protection of cyber Intellectual Property IP such as web content is an increasingly critical concern. The rise of large language models LLMs with online retrieval capabilities enables convenient access to information but often undermines the rights of original content creators. As users...
When Better Features Mean Greater Risks: the Performance-Privacy Trade-Off in Contrastive Learning
With the rapid advancement of deep learning technology, pre-trained encoder models have demonstrated exceptional feature extraction capabilities, playing a pivotal role in the research and application of deep learning. However, their widespread use has raised significant concerns about the risk o...
The Scales of Justitia: a Comprehensive Survey on Safety Evaluation of LLMs
With the rapid advancement of artificial intelligence technology, Large Language Models LLMs have demonstrated remarkable potential in the field of Natural Language Processing NLP, including areas such as content generation, human-computer interaction, machine translation, and code generation,...
On Automating Security Policies with Contemporary LLMs
The complexity of modern computing environments and the growing sophistication of cyber threats necessitate a more robust, adaptive, and automated approach to security enforcement. In this paper, we present a framework leveraging large language models LLMs for automating attack mitigation policy...
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...
StealthInk: a Multi-Bit and Stealthy Watermark for Large Language Models
Watermarking for large language models LLMs offers a promising approach to identifying AI-generated text. Existing approaches, however, either compromise the distribution of original generated text by LLMs or are limited to embedding zero-bit information that only allows for watermark detection b...
SECNEURON: Reliable and Flexible Abuse Control in Local LLMs Via Hybrid Neuron Encryption
Large language models LLMs with diverse capabilities are increasingly being deployed in local environments, presenting significant security and controllability challenges. These locally deployed LLMs operate outside the direct control of developers, rendering them more susceptible to abuse...
Deconstructing Obfuscation: a Four-Dimensional Framework for Evaluating Large Language Models Assembly Code Deobfuscation Capabilities
Large language models LLMs have shown promise in software engineering, yet their effectiveness for binary analysis remains unexplored. We present the first comprehensive evaluation of commercial LLMs for assembly code deobfuscation. Testing seven state-of-the-art models against four obfuscation...
Membership Inference Attacks on Sequence Models
Sequence models, such as Large Language Models LLMs and autoregressive image generators, have a tendency to memorize and inadvertently leak sensitive information. While this tendency has critical legal implications, existing tools are insufficient to audit the resulting risks. We hypothesize that...
FedShield-LLM: a Secure and Scalable Federated Fine-Tuned Large Language Model
Federated Learning FL offers a decentralized framework for training and fine-tuning Large Language Models LLMs by leveraging computational resources across organizations while keeping sensitive data on local devices. It addresses privacy and security concerns while navigating challenges associate...
Comprehensive Vulnerability Analysis Is Necessary for Trustworthy LLM-MAS
This paper argues that a comprehensive vulnerability analysis is essential for building trustworthy Large Language Model-based Multi-Agent Systems LLM-MAS. These systems, which consist of multiple LLM-powered agents working collaboratively, are increasingly deployed in high-stakes applications bu...
Toward a Human-Centered Evaluation Framework for Trustworthy LLM-Powered GUI Agents
The rise of Large Language Models LLMs has revolutionized Graphical User Interface GUI automation through LLM-powered GUI agents, yet their ability to process sensitive data with limited human oversight raises significant privacy and security risks. This position paper identifies three key risks ...
STOPA: a Database of Systematic VariaTion of DeePfake Audio for Open-Set Source Tracing and Attribution
A key research area in deepfake speech detection is source tracing - determining the origin of synthesised utterances. The approaches may involve identifying the acoustic model AM, vocoder model VM, or other generation-specific parameters. However, progress is limited by the lack of a dedicated,...
SQL Injection
Overview Affected versions of this package are vulnerable to SQL Injection via the QuerySubscribers function. An attacker can escalate privileges by injecting malicious SQL commands. Remediation Upgrade github.com/knadh/listmonk/models to version 5.0.0 or higher. References - GitHub Commit - POC...
QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality
This paper introduces QA-HFL, a quality-aware hierarchical federated learning framework that efficiently handles heterogeneous image quality across resource-constrained mobile devices. Our approach trains specialized local models for different image quality levels and aggregates their features...