CVE-2026-2424 Reward Video Ad for WordPress <= 1.6 - Authenticated (Administrator+) Stored Cross-Site Scripting via Admin Settings

The Reward Video Ad for WordPress plugin for WordPress is vulnerable to Stored Cross-Site Scripting via admin settings in all versions up to, and including, 1.6. This is due to insufficient input sanitization and output escaping on plugin settings such as the 'Account ID', 'Message before the...

CVE-2026-2424

The CVE-2026-2424 entry describes a Stored Cross-Site Scripting vulnerability in the Reward Video Ad for WordPress plugin for WordPress, affecting all versions up to 1.6. The issue arises from insufficient input sanitization and output escaping in admin settings (e.g., Account ID, Message before ...

WordPress plugin Reward Video Ad for WordPress 跨站脚本漏洞

WordPress and WordPress plugins are both products of the WordPress Foundation. WordPress is a blog platform developed using the PHP language. This platform allows users to create personal blog websites on servers based on PHP and MySQL. A WordPress plugin is an application that can be added to a...

EUVD-2026-12954

OmniGen2-RL contains an unauthenticated remote code execution vulnerability in the reward server component that allows remote attackers to execute arbitrary commands by sending malicious HTTP POST requests. Attackers can exploit insecure pickle deserialization of request bodies to achieve code...

CVE-2026-25873

OmniGen2-RL contains an unauthenticated remote code execution vulnerability in the reward server component that allows remote attackers to execute arbitrary commands by sending malicious HTTP POST requests. Attackers can exploit insecure pickle deserialization of request bodies to achieve code...

CVE-2026-25873 OmniGen2-RL Reward Server Unsafe Deserialization RCE

OmniGen2-RL contains an unauthenticated remote code execution vulnerability in the reward server component that allows remote attackers to execute arbitrary commands by sending malicious HTTP POST requests. Attackers can exploit insecure pickle deserialization of request bodies to achieve code...

CVE-2026-25873

The CVE-2026-25873 entry concerns OmniGen2-RL, specifically the reward-server component. The vulnerability is an unauthenticated remote code execution via insecure pickle deserialization of HTTP POST request bodies, enabling an attacker to execute arbitrary commands on the host running the expose...

CVE-2026-25873 OmniGen2-RL Reward Server Unsafe Deserialization RCE

OmniGen2-RL contains an unauthenticated remote code execution vulnerability in the reward server component that allows remote attackers to execute arbitrary commands by sending malicious HTTP POST requests. Attackers can exploit insecure pickle deserialization of request bodies to achieve code...

CVE-2026-25873

OmniGen2-RL contains an unauthenticated remote code execution vulnerability in the reward server component that allows remote attackers to execute arbitrary commands by sending malicious HTTP POST requests. Attackers can exploit insecure pickle deserialization of request bodies to achieve code...

OmniGen2 代码问题漏洞

OmniGen2 is a model for command-driven image editing, open-sourced by VectorSpaceLab. OmniGen2 has a code vulnerability that stems from insecure pickle deserialization in the reward server component, which may lead to remote code execution...

PT-2026-26152

OmniGen2-RL contains an unauthenticated remote code execution vulnerability in the reward server component that allows remote attackers to execute arbitrary commands by sending malicious HTTP POST requests. Attackers can exploit insecure pickle deserialization of request bodies to achieve code...

From SFT to RL: Demystifying the Post-Training Pipeline for LLM-Based Vulnerability Detection

The integration of LLMs into vulnerability detection VD has shifted the field toward interpretable and context-aware analysis. While post-training methods have shown promise in general coding tasks, their systematic application to VD remains underexplored. In this paper, we present the first...

SecCodePRM: A Process Reward Model for Code Security

Large Language Models are rapidly becoming core components of modern software development workflows, yet ensuring code security remains challenging. Existing vulnerability detection pipelines either rely on static analyzers or use LLM/GNN-based detectors trained with coarse program-level...

Watch out for AT&amp;T rewards phishing text that wants your personal details

A coworker shared this suspicious SMS where AT&T supposedly warns the recipient that their reward points are about to expire. Phishing attacks are growing increasingly sophisticated, likely with help from AI. They're getting better at mimicking major brands—not just in look, but in behavior...

CVE-2022-37450

Go Ethereum aka geth through 1.10.21 allows attackers to increase rewards by mining blocks in certain situations, and using a manipulation of time-difference values to achieve replacement of main-chain blocks, aka Riskless Uncle Making RUM, as exploited in the wild in 2020 through 2022...

Large Empirical Case Study: Go-Explore Adapted for AI Red Team Testing

Production LLM agents with tool-using capabilities require security testing despite their safety training. We adapt Go-Explore to evaluate GPT-4o-mini across 28 experimental runs spanning six research questions. We find that random-seed variance dominates algorithmic parameters, yielding an 8x...

Exposing Vulnerabilities in RL: A Novel Stealthy Backdoor Attack through Reward Poisoning

Reinforcement learning RL has achieved remarkable success across diverse domains, enabling autonomous systems to learn and adapt to dynamic environments by optimizing a reward function. However, this reliance on reward signals creates a significant security vulnerability. In this paper, we study ...

Multi-Faceted Attack: Exposing Cross-Model Vulnerabilities in Defense-Equipped Vision-Language Models

The growing misuse of Vision-Language Models VLMs has led providers to deploy multiple safeguards, including alignment tuning, system prompts, and content moderation. However, the real-world robustness of these defenses against adversarial attacks remains underexplored. We introduce Multi-Faceted...

VULPO: Context-Aware Vulnerability Detection Via On-Policy LLM Optimization

The widespread reliance on open-source software dramatically increases the risk of vulnerability exploitation, underscoring the need for effective and scalable vulnerability detection VD. Existing VD techniques, whether traditional machine learning-based or LLM-based approaches like prompt...

Malicious code in hapi-protoplanetarydisk-halley-aether (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 14eed49e06d77509c188f6aa28a040fdcf9a987da14db1f3667f27101d6633d8 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js,...