A Decompilation-Driven Framework for Malware Detection with Large Language Models

The parallel evolution of Large Language Models LLMs with advanced code-understanding capabilities and the increasing sophistication of malware presents a new frontier for cybersecurity research. This paper evaluates the efficacy of state-of-the-art LLMs in classifying executable code as either...

Microsoft Inbox COM Objects 资源管理错误漏洞

Microsoft Inbox COM Objects is a built-in COM component for the Windows operating system from Microsoft Corporation USA. A resource management error vulnerability exists in Microsoft Inbox COM Objects. An attacker can exploit this vulnerability to remotely execute code...

Mozilla Firefox和Mozilla Firefox ESR 安全漏洞

Mozilla Firefox is an open source web browser from the Mozilla Foundation.Mozilla Firefox ESR is an extended support version of Firefox web browser from the Mozilla Foundation.Mozilla Thunderbird is a suite of e-mail client software from the Mozilla Foundation that is separate from the Mozilla...

CVE-2024-58340

LangChain

CVE-2025-15514 Ollama Multi-Modal Model Image Processing NULL Pointer Dereference

Ollama 0.11.5-rc0 through current version 0.13.5 contain a null pointer dereference vulnerability in the multi-modal model image processing functionality. When processing base64-encoded image data via the /api/chat endpoint, the application fails to validate that the decoded data represents valid...

aiptx-cyber-mcp

Cyber MCPs - Security Tools for AI !MCP Securityhttps://...

Corrupting LLMs Through Weird Generalizations

Fascinating research: Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs. Abstract LLMs are useful because they generalize so well. But can you have too much of a good thing? We show that a small amount of finetuning in narrow contexts can dramatically shift behavior outside...

CVE-2025-69275 Spectrum outdated java library in class-path

Dependency on Vulnerable Third-Party Component vulnerability in Broadcom DX NetOps Spectrum on Windows, Linux allows DOM-Based XSS.This issue affects DX NetOps Spectrum: 24.3.9 and earlier...

opencode 安全漏洞

opencode is an AI programming intelligence open-sourced by Anomaly. A security vulnerability exists in versions prior to opencode 1.1.10, which stems from the Markdown renderer not cleaning up the LLM response, and could lead to the execution of JavaScript via HTML injection...

📄 LibreChat MCP Remote Command Execution

LibreChat's Model Context Protocol MCP implementation contained a remote command execution vulnerability that allowed any authenticated user to execute commands as root on the Docker container. A single API request could trigger the exploit by taking advantage of the exposure of the stdio transpo...

Allocation of Resources Without Limits or Throttling

Overview vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling in the processimageinput in the idefics3 model implementation. An attacker can cause the server...

PYSEC-2026-143

vLLM is an inference and serving engine for large language models LLMs. In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimensi...

PYSEC-2026-143

vLLM is an inference and serving engine for large language models LLMs. In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimensi...

CVE-2026-22773

CVE-2026-22773 affects vLLM (inference/serving engine) versions 0.6.4 through before 0.12.0 that serve multimodal models using the Idefics3 vision model. A crafted 1x1 pixel image triggers a tensor dimension mismatch in the image input processing, causing an unhandled runtime error and enabling a...

CVE-2026-22773 vLLM is vulnerable to DoS in Idefics3 vision models via image payload with ambiguous dimensions

vLLM is an inference and serving engine for large language models LLMs. In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimensi...

PT-2026-2260

Name of the Vulnerable Software and Affected Versions vLLM versions 0.6.4 through 0.11.9 Description vLLM is an inference and serving engine for large language models LLMs. Users can cause the vLLM engine to crash when serving multimodal models that utilize the Idefics3 vision model implementatio...

QES-Backed Virtual FIDO2 Authenticators: Architectural Options for Secure, Synchronizable WebAuthn Credentials

FIDO2 and the WebAuthn standard offer phishing-resistant, public-key based authentication but traditionally rely on device-bound cryptographic keys that are not naturally portable across user devices. Recent passkey deployments address this limitation by enabling multi-device credentials...

ZkRansomware: Proof-Of-Data Recoverability and Multi-Round Game Theoretic Modeling of Ransomware Decisions

Ransomware is still one of the most serious cybersecurity threats. Victims often pay but fail to regain access to their data, while also facing the danger of losing data privacy. These uncertainties heavily shape the attacker-victim dynamics in decision-making. In this paper, we introduce and...

ALFA: A Safe-By-Design Approach to Mitigate Quishing Attacks Launched Via Fancy QR Codes

Phishing with Quick Response QR codes is termed as Quishing. The attackers exploit this method to manipulate individuals into revealing their confidential data. Recently, we see the colorful and fancy representations of QR codes, the 2D matrix of QR codes which does not reflect a typical mixture ...

CVE-2025-13772

A flaw was found in GitLab. An authenticated user could exploit this vulnerability by manipulating namespace identifiers in API requests. This could allow them to access and utilize AI model settings from unauthorized namespaces, leading to information disclosure and potential misuse of AI...