shennina

Shennina Automatizando la Explotación de Hosts con IA Resumen Shennina es un marco de trabajo automatizado de explotación de hosts. La misión del proyecto es automatizar completamente el escaneo, el análisis/escanéo de vulnerabilidades y la explotación utilizando Inteligencia Artificial. Shennina...

sonicwall-audit-toolkit

SonicWall Security Audit Toolkit Automated security assessment framework for SonicWall appliances. For authorized penetration testing engagements only. Quick Start Install dependencies pip install -r requirements.txt Run all modules against a target python3 sonicwallaudit.py --target 192.168.1.1...

An Automated Framework for Generating Stealthy Cell-Embedded Hardware Trojans

Hardware Trojans HTs pose significant threats across the Integrated Circuit IC design lifecycle because they can be inserted by untrusted entities at different stages under the zero-trust model. When triggered under rare conditions, HTs can compromise the functionality, reliability, or security o...

Knowledge over Parameters: Evolving Smart Contract Vulnerability Detection

Smart contract vulnerabilities are predominantly logic bugs whose detection requires structured, step-by-step procedural knowledge of attack patterns and contract semantics. Existing LLM-based methods struggle to generate this knowledge automatically: prompt-based methods rely on manually crafted...

Investigating Metamorphic Fuzz Oracle Enhancement Via Large Language Models

Fuzz drivers are essential components of greybox fuzzing, as they encapsulate target interfaces, define test spaces, and largely determine fuzzing effectiveness. Existing fuzz drivers typically rely on crash-based oracles for security testing, overlooking library functionality and limiting bug...

How to Compare the Security of Code Written by Humans to LLM-Generated Code

Large language models LLMs are rapidly transforming how software is created and maintained. Comparing LLM-generated code against human-written standards is essential to determine whether these new tools uphold or erode the security baselines established by professional developers. Yet, we lack a...

IPDevicePenTest

IPDevicePenTest Automated penetration testing framework for...

Automated Framework to Evaluate and Harden LLM System Instructions against Encoding Attacks

System Instructions in Large Language Models LLMs are commonly used to enforce safety policies, define agent behavior, and protect sensitive operational context in agentic AI applications. These instructions may contain sensitive information such as API credentials, internal policies, and...

Exploit for Deserialization of Untrusted Data in Siemens 6Bk1602-0Aa12-0Tp0_Firmware

⚡ Pentest Automation !Versionhttps://img.shields.io/badg...

Breaking the Illusion: Automated Reasoning of GDPR Consent Violations

Recent privacy regulations such as the General Data Protection Regulation GDPR and the California Consumer Privacy Act CCPA have established legal requirements for obtaining user consent regarding the collection, use, and sharing of personal data. These regulations emphasize that consent must be...

Network-Vuln

🔍 Network Vulnerability Scanner !Pythonhttps://img.shiel...

STAC: When Innocent Tools Form Dangerous Chains to Jailbreak LLM Agents

As LLMs advance into autonomous agents with tool-use capabilities, they introduce security challenges that extend beyond traditional content-based LLM safety concerns. This paper introduces Sequential Tool Attack Chaining STAC, a novel multi-turn attack framework that exploits agent tool use. STA...

Automatic Red Teaming LLM-Based Agents with Model Context Protocol Tools

The remarkable capability of large language models LLMs has led to the wide application of LLM-based agents in various domains. To standardize interactions between LLM-based agents and their environments, model context protocol MCP tools have become the de facto standard and are now widely...

AutoRAN: Weak-To-Strong Jailbreaking of Large Reasoning Models

This paper presents AutoRAN, the first automated, weak-to-strong jailbreak attack framework targeting large reasoning models LRMs. At its core, AutoRAN leverages a weak, less-aligned reasoning model to simulate the target model's high-level reasoning structures, generates narrative prompts, and...

grapheneX - Automated System Hardening Framework

grapheneX In computing, hardening is usually the process of securing a system by reducing its surface of vulnerability, which is larger when a system performs more functions; in principle a single-function system is more secure than a multipurpose one. Reducing available ways of attack typically...