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...

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Source: amazon-inspector d616ee2c6efd301a0e35c06c3eff20895961db3ec536172406710e23d0a6dc85 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js, autopublish.js, autopublish2.js, autopublish3.js designed ...

Malicious code in evolutionary-amber-rhinoceros (npm)

Source: amazon-inspector 3e30befce3cb5ace85b22d15ae5039ff8679def961ca8e9634b0948bd7a1bf25 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js, autopublish.js, autopublish2.js, autopublish3.js designed ...

Malicious code in evolutionary-beige-owl (npm)

Source: amazon-inspector 8d5e48972d96ed25c00f6d2160d9dbda71c1c211617388949b22114b8da3fb2f This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js, autopublish.js, autopublish2.js, autopublish3.js designed ...

Malicious code in evolutionary-yellow-starfish (npm)

Source: amazon-inspector 8b2fd9c0b09d88a32452374dc34b16b8852c105d6f7f7573b5873b062a297328 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js, autopublish.js, autopublish2.js, autopublish3.js designed ...

Malicious code in evolutionary-beige-seahorse (npm)

Source: amazon-inspector 293cc379c5ffc59001701f8903c5ed76a90fced087fe49b2bded79d7a749cbe0 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js, autopublish.js, autopublish2.js, autopublish3.js designed ...

EvoFlint: An Evolutionary Atlas of Multi-Turn LLM Vulnerabilities

Frontier language models that refuse harmful single-turn prompts often comply when the same intent is reached gradually over many turns, making multi-turn attacks one of the least understood failure modes of large language models. Most automated red-teaming methods treat this as a generation...

SAEFUZZ: Smart Contract Vulnerability Detection through Statically Guided Evolutionary Fuzzing

The effectiveness of smart contract fuzzing depends strongly on whether generated transactions reach deep, state-dependent execution paths. Existing fuzzers often generate highly random call sequences, wasting executions on semantically invalid or low-value states and leaving vulnerabilities that...

Network Reciprocity Shapes Evolutionary Cybersecurity Dynamics

AI-assisted cybersecurity systems are characterised by continuous adaptation between attackers and defenders, making evolutionary game theory a natural framework for studying their long-term behaviour. However, existing evolutionary cybersecurity models have primarily focused on homogeneous...

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...

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...

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...

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...