T-MAP

T-MAP: Red-Teaming de Agentes LLM con Búsqueda Evolutiva Consciente de la Trayectoria T-MAP es un marco de búsqueda evolutiva consciente de la trayectoria para el red-teaming de agentes LLM sobre servidores MCP. Genera y muta iterativamente prompts adversariales guiados por trayectorias de...

fluffi

FLUFFI FLUFFI - 面向渗透测试人员的分布式进化二进制模糊测试工具。 关于项目 高级概览 初学者教程 入门指南 使用方法 HOWTOs 技术细节 为 FLUFFI 做贡献 许可证 发现的漏洞 到目前为止，FLUFFI 几乎只用于西门子的产品和解决方案。其中发现的漏洞将不会公开。 然而，FLUFFI 发现了以下已公开的漏洞（请帮助我们保持此列表更新）： bcshiftaddsub 中的缓冲区下溢CVE-2019-11046...

choronzon

Choronzon - 一种基于进化知识的模糊测试工具 简介 本文档旨在简要解释 Choronzon 背后的理论。此外，它还提供了关于其内部机制以及如何扩展 Choronzon 以满足新需求的详细信息。Choronzon 架构的概述最初在 ZeroNights 2015 会议 上提出。该演讲的录像和幻灯片也已提供。 Choronzon 是一种进化模糊测试工具。它试图模仿进化过程，以持续产生更好的结果。为此，它配备了一套评估系统，用于判断哪些被模糊处理过的文件是有趣的，哪些应该被丢弃。 此外，Choronzon...

MalTree: Tracing Malware Evolution from Embeddings at Scale

Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary relationships among malware families can inform proactive defense, but traditional reverse engineering can take months to years to uncover such lineag...

Reasoning As an Attack Surface: Adaptive Evolutionary CoT Jailbreaks for LLMs

Large Reasoning Models LRMs have demonstrated remarkable capabilities in reasoning and generation tasks and are increasingly deployed in real-world applications. However, their explicit chain-of-thought CoT mechanism introduces new security risks, making them particularly vulnerable to jailbreak...

FuzzAgent: Multi-Agent System for Evolutionary Library Fuzzing

Library fuzzing is essential for hardening the software supply chain, but adopting it at scale remains expensive. Practitioners still spend substantial effort on environment setup, struggle to generate harnesses that respect intricate API constraints, and lack reliable means to tell genuine libra...

ContextualJailbreak: Evolutionary Red-Teaming Via Simulated Conversational Priming

Large language models LLMs remain vulnerable to jailbreak attacks that bypass safety alignment and elicit harmful responses. A growing body of work shows that contextual priming, where earlier turns covertly bias later replies, constitutes a powerful attack surface, with hand-crafted multi-turn...

PIIGuard: Mitigating PII Harvesting under Adversarial Sanitization

Browsing-enabled LLM assistants can fetch webpages and answer contact-seeking queries, creating a practical channel for scraping contact-style personally identifiable information PII from public pages. Many prior defenses are deployed at the model, service, or agent layer rather than at the webpa...

FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework

Modern fuzzers increasingly use Large Language Models LLMs to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initialization and sampling variance, which can reduce exploration efficiency and lead to redundant inputs. We present FunFuzz, a multi-island evolutionary fuzzi...

LLM-Guided Prompt Evolution for Password Guessing

Passwords still remain a dominant authentication method, yet their security is routinely subverted by predictable user choices and large-scale credential leaks. Automated password guessing is a key tool for stress-testing password policies and modeling attacker behavior. This paper applies...

T-MAP: Red-Teaming LLM Agents with Trajectory-Aware Evolutionary Search

While prior red-teaming efforts have focused on eliciting harmful text outputs from large language models LLMs, such approaches fail to capture agent-specific vulnerabilities that emerge through multi-step tool execution, particularly in rapidly growing ecosystems such as the Model Context Protoc...

Defining Cost Function of Steganography with Large Language Models

In this paper, we make the first attempt towards defining cost function of steganography with large language models LLMs, which is totally different from previous works that rely heavily on expert knowledge or require large-scale datasets for cost learning. To achieve this goal, a two-stage...

Digital Twin-Driven Secure Access Strategy for SAGIN-Enabled IoT Networks

In space-air-ground integrated networks SAGIN-enabled IoT networks, secure access has become a significant challenge due to the increasing risks of eavesdropping attacks. To address these threats to data confidentiality, this paper proposes a Digital Twin DT-driven secure access strategy. The...

Trustworthy GenAI over 6G: Integrated Applications and Security Frameworks

The integration of generative artificial intelligence GenAI into 6G networks promises substantial performance gains while simultaneously exposing novel security vulnerabilities rooted in multimodal data processing and autonomous reasoning. This article presents a unified perspective on cross-doma...

GRAPHTEXTACK: A Realistic Black-Box Node Injection Attack on LLM-Enhanced GNNs

Text-attributed graphs TAGs, which combine structural and textual node information, are ubiquitous across many domains. Recent work integrates Large Language Models LLMs with Graph Neural Networks GNNs to jointly model semantics and structure, resulting in more general and expressive models that...

EUVD-2025-117405

Malicious code in evolutionary-beige-owl npm...

EUVD-2025-117406

Malicious code in evolutionary-amber-rhinoceros npm...

EUVD-2025-117404

Malicious code in evolutionary-teal-leopon npm...

EUVD-2025-99111

Malicious code in evolutionaryeelz3n npm...

Malicious code in evolutionary_eel_z3n (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 9cde45ac55c22864bbd04bb6f0a27f076bdb536f75c41504a45b8cb9c7516de2 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js,...