404 matches found
CVE-2025-43700
Improper Preservation of Permissions vulnerability in Salesforce OmniStudio FlexCards allows exposure of encrypted data. This impacts OmniStudio: before Spring 2025...
Adversarial Text Generation with Dynamic Contextual Perturbation
Adversarial attacks on Natural Language Processing NLP models expose vulnerabilities by introducing subtle perturbations to input text, often leading to misclassification while maintaining human readability. Existing methods typically focus on word-level or local text segment alterations,...
A week in security (June 1 – June 7)
Last week on Malwarebytes Labs: What does Facebook know about me? Lock and Code S06E11 Victims risk AsyncRAT infection after being redirected to fake Booking.com sites Juice jacking warnings are back, with a new twist The North Face warns customers about potentially stolen data Scammers are...
Securing Unbounded Differential Privacy against Timing Attacks
Recent works have started to theoretically investigate how we can protect differentially private programs against timing attacks, by making the joint distribution the output and the runtime differentially private JOT-DP. However, the existing approaches to JOT-DP have some limitations, particular...
AlphaSteer: Learning Refusal Steering with Principled Null-Space Constraint
As LLMs are increasingly deployed in real-world applications, ensuring their ability to refuse malicious prompts, especially jailbreak attacks, is essential for safe and reliable use. Recently, activation steering has emerged as an effective approach for enhancing LLM safety by adding a refusal...
Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning
The rapid global adoption of electric vehicles EVs has established electric vehicle supply equipment EVSE as a critical component of smart grid infrastructure. While essential for ensuring reliable energy delivery and accessibility, EVSE systems face significant cybersecurity challenges, includin...
Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models Via Generative Continual Learning
Foundation models have revolutionized fields such as natural language processing and computer vision by enabling general-purpose learning across diverse tasks and datasets. However, building analogous models for human mobility remains challenging due to the privacy-sensitive nature of mobility da...
Network Hexagons under Attack: Secure Crowdsourcing of Geo-Referenced Data
A critical requirement for modern-day Intelligent Transportation Systems ITS is the ability to collect geo-referenced data from connected vehicles and mobile devices in a safe, secure and anonymous way. The Nexagon protocol, which builds on the IETF Locator/ID Separation Protocol LISP and the...
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...
Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning
Federated learning FL allows multiple data-owners to collaboratively train machine learning models by exchanging local gradients, while keeping their private data on-device. To simultaneously enhance privacy and training efficiency, recently parameter-efficient fine-tuning PEFT of large-scale...
SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Privacy-preserving data publication, including synthetic data sharing, often experiences trade-offs between privacy and utility. Synthetic data is generally more effective than data anonymization in balancing this trade-off, however, not without its own challenges. Synthetic data produced by...
Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment
Edge Intelligence EI serves as a critical enabler for privacy-preserving systems by providing AI-empowered computation and distributed caching services at the edge, thereby minimizing latency and enhancing data privacy. The integration of blockchain technology further augments EI frameworks by...
Security Concerns for Large Language Models: a Survey
Large Language Models LLMs such as GPT-4 and its recent iterations, Google's Gemini, Anthropic's Claude 3 models, and xAI's Grok have caused a revolution in natural language processing, but their capabilities also introduce new security vulnerabilities. In this survey, we provide a comprehensive...
Transaction Proximity: a Graph-Based Approach to Blockchain Fraud Prevention
This paper introduces a fraud-deterrent access validation system for public blockchains, leveraging two complementary concepts: "Transaction Proximity", which measures the distance between wallets in the transaction graph, and "Easily Attainable Identities EAIs", wallets with direct transaction...
A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control
Traditional Identity and Access Management IAM systems, primarily designed for human users or static machine identities via protocols such as OAuth, OpenID Connect OIDC, and SAML, prove fundamentally inadequate for the dynamic, interdependent, and often ephemeral nature of AI agents operating at...
Privacy-Preserving Inconsistency Measurement
We investigate a new form of privacy-preserving inconsistency measurement for multi-party communication. Intuitively, for two knowledge bases KA, KB of two agents A, B, our results allow to quantitatively assess the degree of inconsistency for KA U KB without having to reveal the actual contents ...
The Feasibility of Topic-Based Watermarking on Academic Peer Reviews
Large language models LLMs are increasingly integrated into academic workflows, with many conferences and journals permitting their use for tasks such as language refinement and literature summarization. However, their use in peer review remains prohibited due to concerns around confidentiality...
Semantic-Preserving Adversarial Attacks on LLMs: an Adaptive Greedy Binary Search Approach
Large Language Models LLMs increasingly rely on automatic prompt engineering in graphical user interfaces GUIs to refine user inputs and enhance response accuracy. However, the diversity of user requirements often leads to unintended misinterpretations, where automated optimizations distort...
Privacy-Preserving Bathroom Monitoring for Elderly Emergencies Using PIR and LiDAR Sensors
In-home elderly monitoring requires systems that can detect emergency events - such as falls or prolonged inactivity - while preserving privacy and requiring no user input. These systems must be embedded into the surrounding environment, capable of capturing activity, and responding promptly. Thi...
