A Practical Adversarial Attack against Sequence-Based Deep Learning Malware Classifiers

Sequence-based deep learning models e.g., RNNs, can detect malware by analyzing its behavioral sequences. Meanwhile, these models are susceptible to adversarial attacks. Attackers can create adversarial samples that alter the sequence characteristics of behavior sequences to deceive malware...

PT-2025-37463

Name of the Vulnerable Software and Affected Versions: D-Link DI-8100 versions 16.07.26A1, 17.12.20A1, and 19.12.10A1 D-Link DI-8100G versions 16.07.26A1, 17.12.20A1, and 19.12.10A1 D-Link DI-8200 versions 16.07.26A1, 17.12.20A1, and 19.12.10A1 D-Link DI-8200G versions 16.07.26A1, 17.12.20A1, and...

Ensembling Large Language Models for Code Vulnerability Detection: an Empirical Evaluation

Code vulnerability detection is crucial for ensuring the security and reliability of modern software systems. Recently, Large Language Models LLMs have shown promising capabilities in this domain. However, notable discrepancies in detection results often arise when analyzing identical code segmen...

CVE-2025-10399: Improper Neutralization of Special Elements used in an SQL Command

A weakness has been identified in Korzh EasyQuery up to 7.4.0. This issue affects some unknown processing of the file /api/easyquery/models/nwind/fetch of the component Query Builder UI. This manipulation causes sql injection. The attack may be initiated remotely. The exploit has been made...

SoK: How Sensor Attacks Disrupt Autonomous Vehicles: an End-To-End Analysis, Challenges, and Missed Threats

Autonomous vehicles, including self-driving cars, robotic ground vehicles, and drones, rely on complex sensor pipelines to ensure safe and reliable operation. However, these safety-critical systems remain vulnerable to adversarial sensor attacks that can compromise their performance and mission...

ENJ: Optimizing Noise with Genetic Algorithms to Jailbreak LSMs

The widespread application of Large Speech Models LSMs has made their security risks increasingly prominent. Traditional speech adversarial attack methods face challenges in balancing effectiveness and stealth. This paper proposes Evolutionary Noise Jailbreak ENJ, which utilizes a genetic algorit...

Your Compiler Is Backdooring Your Model: Understanding and Exploiting Compilation Inconsistency Vulnerabilities in Deep Learning Compilers

Deep learning DL compilers are core infrastructure in modern DL systems, offering flexibility and scalability beyond vendor-specific libraries. This work uncovers a fundamental vulnerability in their design: can an official, unmodified compiler alter a model's semantics during compilation and...

A Comparison of Selected Image Transformation Techniques for Malware Classification

Recently, a considerable amount of malware research has focused on the use of powerful image-based machine learning techniques, which generally yield impressive results. However, before image-based techniques can be applied to malware, the samples must be converted to images, and there is no...

Large Language Models for Security Operations Centers: a Comprehensive Survey

Large Language Models LLMs have emerged as powerful tools capable of understanding and generating human-like text, offering transformative potential across diverse domains. The Security Operations Center SOC, responsible for safeguarding digital infrastructure, represents one of these domains. SO...

allennlp (>=1.0.0rc6.dev20200602, <=1.1.0rc1.dev20200706), allennlp-models (>=1.1.0rc1.dev20200630, <=1.1.0rc1.dev20200706) +6 more potentially affected by CVE-2025-6638 via transformers (2.10.0)

transformers PYPI version 2.10.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - allennlp =1.0.0rc6.dev20200602, =1.1.0rc1.dev20200630, =0.0.6, =2.7.3, =0.2.15, =0.2.0, =0.3.0 Source cves: CVE-2025-6638 Source...

allennlp (>=1.2.0rc1.dev20201022, <=1.2.1), allennlp-models (>=1.2.0rc1.dev20201022, <=1.2.1) +5 more potentially affected by CVE-2025-6638 via transformers (3.4.0)

transformers PYPI version 3.4.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - allennlp =1.2.0rc1.dev20201022, =1.2.0rc1.dev20201022, =0.0.4, =0.0.1, =2.2.5, =2.3.1 Source cves: CVE-2025-6638 Source advisory:...

allennlp (>=1.0.0rc4.dev20200513, <=1.0.0rc6.dev20200601), allennlp-models (>=1.0.0rc4, <=1.0.0rc5) potentially affected by CVE-2025-6638 via transformers (2.9.1)

transformers PYPI version 2.9.1 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - allennlp =1.0.0rc4.dev20200513, =1.0.0rc4, =1.0.0rc5 Source cves: CVE-2025-6638 Source advisory: OSV:GHSA-59P9-H35M-WG4G...

CVE-2025-54084

OS Command 'OS Command Injection' vulnerability in Calix GigaCenter ONT Quantenna SoC modules allows authenticated attackers with 'super' user credentials to execute arbitrary OS commands through improper input validation, potentially leading to full system compromise.This issue affects GigaCente...

CVE-2025-53913

Excessive Privileges vulnerability in Calix GigaCenter ONT Quantenna SoC modules allows Privilege Abuse.This issue affects GigaCenter ONT: 844E, 844G, 844GE, 854GE, 812G, 813G, 818G...

CVE-2025-53914

Excessive Privileges vulnerability in Calix GigaCenter ONT Broadcom SoC modules allows Privilege Abuse.This issue affects GigaCenter ONT: 844E, 844G, 844GE, 854GE, 812G, 813G, 818G...

Malicious code in sos-models (npm)

The package sos-models was found to contain malicious code. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 7171745aec44dc0f96e3426f39966e6f22281703919bc10a7a161efef9b928cf Any computer that has this package installed or running should be considered fully compromise...

Malicious Package

Overview sos-models is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this package authorship...

MAL-2025-47089 Malicious code in sos-models (npm)

The package sos-models was found to contain malicious code. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 7171745aec44dc0f96e3426f39966e6f22281703919bc10a7a161efef9b928cf Any computer that has this package installed or running should be considered fully compromise...

Malware in sos-models

Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be rotated immediately from a different computer. The package should be removed, but as full control of the computer may have been given to an outsid...

Malicious Package

Overview @team-event/models is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this package...