Silicon Labs Series 2 安全漏洞

Silicon Labs Series 2 is an IoT system platform developed by Silicon Corporation in the United States. There is a security vulnerability in Silicon Labs Series 2, which stems from the fact that the DPA counter does not reseed under certain conditions, potentially allowing key extraction through D...

Trojan Attacks on Neural Network Controllers for Robotic Systems

Neural network controllers are increasingly deployed in robotic systems for tasks such as trajectory tracking and pose stabilization. However, their reliance on potentially untrusted training pipelines or supply chains introduces significant security vulnerabilities. This paper investigates...

Exploit for Improper Initialization in Linux Linux_Kernel

Naive detector and reproducer of CVE-2022-0847 dirty pipe. Use...

OpenProject command injection vulnerability

OpenProject is an open-source web-based project management software. Versions of OpenProject prior to 16.6.6 and 17.0.2 had a command injection vulnerability. This vulnerability stemmed from arbitrary file writing at the repository differential download endpoint, which could lead to data loss and...

PrivFly: A Privacy-Preserving Self-Supervised Framework for Rare Attack Detection in IoFT

The Internet of Flying Things IoFT plays a vital role in modern applications such as aerial surveillance and smart mobility. However, it remains highly vulnerable to cyberattacks that threaten the confidentiality, integrity, and availability of sensitive data. Developing effective intrusion...

Advanced Encryption Technique for Multimedia Data Using Sudoku-Based Algorithms for Enhanced Security

Encryption and Decryption is the process of sending a message in a ciphered way that appears meaningless and could be deciphered using a key for security purposes to avoid data breaches. This paper expands on the previous work on Sudoku-based encryption methods, applying it to other forms of medi...

A Critical Analysis of the Medibank Health Data Breach and Differential Privacy Solutions

This paper critically examines the 2022 Medibank health insurance data breach, which exposed sensitive medical records of 9.7 million individuals due to unencrypted storage, centralized access, and the absence of privacy-preserving analytics. To address these vulnerabilities, we propose an...

Exploring the Integration of Differential Privacy in Cybersecurity Analytics: Balancing Data Utility and Privacy in Threat Intelligence

To resolve the acute problem of privacy protection and guarantee that data can be used in the context of threat intelligence, this paper considers the implementation of Differential Privacy DP in cybersecurity analytics. DP, which is a sound mathematical framework, ensures privacy by adding a...

LegionITS: A Federated Intrusion-Tolerant System Architecture

The growing sophistication, frequency, and diversity of cyberattacks increasingly exceed the capacity of individual entities to fully understand and counter them. While existing solutions, such as Security Information and Event Management SIEM systems, Security Orchestration, Automation, and...

EUVD-2025-201812

The ruby-saml library is for implementing the client side of a SAML authorization. ruby-saml versions up to and including 1.12.4 contain an authentication bypass vulnerability due to an incomplete fix for CVE-2025-25292. ReXML and Nokogiri parse XML differently, generating entirely different...

SmartPoC: Generating Executable and Validated PoCs for Smart Contract Bug Reports

Smart contracts are prone to vulnerabilities and are analyzed by experts as well as automated systems, such as static analysis and AI-assisted solutions. However, audit artifacts are heterogeneous and often lack reproducible, executable PoC tests suitable for automated validation, leading to...

Enhancing Deep Learning-Based Rotational-XOR Attacks on Lightweight Block Ciphers Simon32/64 and Simeck32/64

At CRYPTO 2019, Gohr pioneered neural cryptanalysis by introducing differential-based neural distinguishers to attack Speck32/64, establishing a novel paradigm combining deep learning with differential cryptanalysis.Since then, constructing neural distinguishers has become a significant approach ...

EUVD-2025-35214

uv has differential in tar extraction with PAX headers...

PT-2026-29492

Name of the Vulnerable Software and Affected Versions Linux kernel affected versions not specified Description The Linux kernel contained a flaw in its AppArmor implementation related to differential encoding verification. This flaw allowed for the creation of loops if abused, potentially leading...

EUVD-2015-3070

Malware in sbrugna...

EUVD-2025-6414

Malicious code in bioql PyPI...

Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation

Fine-tuning large language models LLMs with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific domains. Given the shared characteristics in data across different organizations, the idea of collaboratively fine-tuning an LLM using data from multiple...

VulnRepairEval: an Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities

The adoption of Large Language Models LLMs for automated software vulnerability patching has shown promising outcomes on carefully curated evaluation sets. Nevertheless, existing datasets predominantly rely on superficial validation methods rather than exploit-based verification, leading to...

Risk Assessment and Security Analysis of Large Language Models

As large language models LLMs expose systemic security challenges in high risk applications, including privacy leaks, bias amplification, and malicious abuse, there is an urgent need for a dynamic risk assessment and collaborative defence framework that covers their entire life cycle. This paper...

On the Security and Privacy of Federated Learning: a Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions

Federated Learning FL is an emerging distributed machine learning paradigm enabling multiple clients to train a global model collaboratively without sharing their raw data. While FL enhances data privacy by design, it remains vulnerable to various security and privacy threats. This survey provide...