CVE-2025-41232 CVE-2025-41232: Spring Security authorization bypass for method security annotations on private methods

Spring Security Aspects may not correctly locate method security annotations on private methods. This can cause an authorization bypass. Your application may be affected by this if the following are true: You are using @EnableMethodSecuritymode=ASPECTJ and spring-security-aspects, and You have...

CVE-2025-41232

CVE-2025-41232 affects multiple IBM and Spring-based products where Spring Security Aspects may fail to locate method security annotations on private methods, enabling potential authorization bypass when using @EnableMethodSecurity(mode=ASPECTJ) with spring-security-aspects and private annotated ...

CVE-2025-41232 CVE-2025-41232: Spring Security authorization bypass for method security annotations on private methods

Spring Security Aspects may not correctly locate method security annotations on private methods. This can cause an authorization bypass. Your application may be affected by this if the following are true: You are using @EnableMethodSecuritymode=ASPECTJ and spring-security-aspects, and You have...

Outsourcing SAT-Based Verification Computations in Network Security

The emergence of cloud computing gives huge impact on large computations. Cloud computing platforms offer servers with large computation power to be available for customers. These servers can be used efficiently to solve problems that are complex by nature, for example, satisfiability SAT problem...

VMware Spring Security 安全漏洞

VMware Spring Security is a set of security frameworks from VMware, Inc. that provide illustrative security protection for Spring-based applications. A security vulnerability exists in VMware Spring Security versions 6.4.0 through 6.4.5 that stems from not properly locating security annotations o...

UBUNTU-CVE-2025-37905

In the Linux kernel, the following vulnerability has been resolved: firmware: armscmi: Balance device refcount when destroying devices Using devicefindchild to lookup the proper SCMI device to destroy causes an unbalance in device refcount, since devicefindchild calls an implicit getdevice: this,...

Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge

Artificial neural network pruning is a method in which artificial neural network sizes can be reduced while attempting to preserve the predicting capabilities of the network. This is done to make the model smaller or faster during inference time. In this work we analyze the ability of a selection...

BeamClean: Language Aware Embedding Reconstruction

In this work, we consider an inversion attack on the obfuscated input embeddings sent to a language model on a server, where the adversary has no access to the language model or the obfuscation mechanism and sees only the obfuscated embeddings along with the model's embedding table. We propose...

The vulnerability of the Naumen Service Management Platform, related to the use of dangerous methods or functions, allows a perpetrator to execute arbitrary code.

The vulnerability of the Naumen Service Management Platform is related to the use of dangerous methods or functions. Exploiting this vulnerability allows a malicious actor to execute arbitrary code remotely...

Fake CAPTCHA Attacks Deploy Infostealers and RATs in a Multistage Payload Chain

We have detected a new tactic involving fake CAPTCHA pages that trick users into executing harmful commands in Windows. This scheme uses disguised files sent via phishing and other malicious methods...

PT-2025-22336 · Spring · Spring Security Aspects

Name of the Vulnerable Software and Affected Versions: Spring Security Aspects affected versions not specified Description: The issue concerns Spring Security Aspects not correctly locating method security annotations on private methods, potentially causing an authorization bypass. This can affec...

Fragments to Facts: Partial-Information Fragment Inference from LLMs

Large language models LLMs can leak sensitive training data through memorization and membership inference attacks. Prior work has primarily focused on strong adversarial assumptions, including attacker access to entire samples or long, ordered prefixes, leaving open the question of how vulnerable...

Spring Security authorization bypass for method security annotations on private methods

Spring Security Aspects may not correctly locate method security annotations on private methods. This can cause an authorization bypass. Your application may be affected by this if the following are true: In that case, the target method may be able to be invoked without proper authorization. You...

[SECURITY] Fedora 41 Update: syslog-ng-4.8.2-1.fc41

syslog-ng is an enhanced log daemon, supporting a wide range of input and output methods: syslog, unstructured text, message queues, databases SQL and NoSQL alike and more. Key features: receive and send RFC3164 and RFC5424 style syslog messages work with any kind of unstructured data receive and...

Server-Side Template Injection Vulnerabilities and Exploitation Techniques

Research article called Server-Side Template Injection SSTI Vulnerabilities and Exploitation Techniques. The paper provides a structured methodology for detecting and exploiting SSTI vulnerabilities across multiple template engines, along with real-world case studies and mitigation strategies...

CVE-2025-31493

Kirby is an open-source content management system. A vulnerability in versions prior to 3.9.8.3, 3.10.1.2, and 4.7.1 affects all Kirby sites that use the collection helper or $kirby-collection method with a dynamic collection name such as a collection name that depends on request or user data...

Description of the security update for Office 2016: May 13, 2025 (KB5002695)

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Description of the security update for Office Online Server: May 13, 2025 (KB5002707)

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MUBox: a Critical Evaluation Framework of Deep Machine Unlearning

Recent legal frameworks have mandated the right to be forgotten, obligating the removal of specific data upon user requests. Machine Unlearning has emerged as a promising solution by selectively removing learned information from machine learning models. This paper presents MUBox, a comprehensive...

Mirror Mirror on the Wall, Have I Forgotten It All? A New Framework for Evaluating Machine Unlearning

Machine unlearning methods take a model trained on a dataset and a forget set, then attempt to produce a model as if it had only been trained on the examples not in the forget set. We empirically show that an adversary is able to distinguish between a mirror model a control model produced by...