66 matches found
choronzon
Choronzon - An evolutionary knowledge-based fuzzer Introduction This document aims to explain in brief the theory behind Choronzon. Moreover, it provides details about its internals and how one can extend Choronzon to meet new requirements. An overview of the architecture of Choronzon was initial...
T-MAP
T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search T-MAP is a trajectory-aware evolutionary search framework for red-teaming LLM agents over MCP servers. It iteratively generates and mutates adversarial prompts guided by execution trajectories, mapping the agent's vulnerabili...
fluffi
FLUFFI FLUFFI - Un fuzzer binario evolutivo distribuido para pentesters. Acerca del proyecto Visión general de alto nivel Tutorial para principiantes Primeros pasos Uso HOWTOs Detalles técnicos Contribuir a FLUFFI LICENCIA Bugs encontrados Hasta ahora, FLUFFI se ha utilizado casi exclusivamente e...
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...
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...
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...
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...
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,...