EUVD-2014-7854

Malware in sbrugna...

EUVD-2010-0918

Malware in sbrugna...

EUVD-2010-4433

Malware in sbrugna...

EUVD-2015-4320

Malware in sbrugna...

EUVD-2010-0939

Malware in sbrugna...

EUVD-2016-2465

Malware in sbrugna...

EUVD-2021-21359

Malware in sbrugna...

EUVD-2015-0789

Malware in sbrugna...

EUVD-2014-3392

Malware in sbrugna...

EUVD-2023-26014

Malicious code in bioql PyPI...

EUVD-2021-6861

Malicious code in bioql PyPI...

EUVD-2024-18032

Malicious code in bioql PyPI...

Denial Of Service (DoS)

github.com/consensys/gnark is vulnerable to Denial of Service DoS. The vulnerability is due to the fake-GLV scalar multiplication algorithm not converging quickly enough for certain inputs, which allows an attacker to trigger excessive computation and cause service disruption...

Efficient Decoding Methods for Language Models on Encrypted Data

Large language models LLMs power modern AI applications, but processing sensitive data on untrusted servers raises privacy concerns. Homomorphic encryption HE enables computation on encrypted data for secure inference. However, neural text generation requires decoding methods like argmax and...

Risks and Compliance with the EU'S Core Cyber Security Legislation

The European Union EU has long favored a risk-based approach to regulation. Such an approach is also used in recent cyber security legislation enacted in the EU. Risks are also inherently related to compliance with the new legislation. Objective: The paper investigates how risks are framed in the...

PT-2025-44139

Name of the Vulnerable Software and Affected Versions Linux kernel affected versions not specified Description The Linux kernel contains a flaw within the tty subsystem, specifically in the n gsm component. The issue arises from the potential to block the input queue while waiting for a Modem...

Enhancing Privacy in Decentralized Min-Max Optimization: a Differentially Private Approach

Decentralized min-max optimization allows multi-agent systems to collaboratively solve global min-max optimization problems by facilitating the exchange of model updates among neighboring agents, eliminating the need for a central server. However, sharing model updates in such systems carry a ris...

ModShift: Model Privacy Via Designed Shifts

In this paper, shifts are introduced to preserve model privacy against an eavesdropper in federated learning. Model learning is treated as a parameter estimation problem. This perspective allows us to derive the Fisher Information matrix of the model updates from the shifted updates and drive the...

Learning-Based Cost-Aware Defense of Parallel Server Systems against Malicious Attacks

We consider the cyber-physical security of parallel server systems, which is relevant for a variety of engineering applications such as networking, manufacturing, and transportation. These systems rely on feedback control and may thus be vulnerable to malicious attacks such as denial-of-service,...

The Exposure Convergence: Why Identity, Infrastructure, and Intelligence Are Converging

Running short on time but still want to stay in the know? Well, we’ve got you covered! We’ve condensed all the key takeaways into a handy audio summary. Our AI-driven podcasts are fit for on the go. The cybersecurity industry is experiencing a fundamental convergence around "exposure management" ...