Automated Penetration Testing: Formalization and Realization
- URL: http://arxiv.org/abs/2412.12745v1
- Date: Tue, 17 Dec 2024 10:09:41 GMT
- Title: Automated Penetration Testing: Formalization and Realization
- Authors: Charilaos Skandylas, Mikael Asplund,
- Abstract summary: Recent changes in standards and regulations mandate increased security testing of software systems.
Manual penetration testing is labor-intensive and requires highly skilled practitioners.
We propose a general self-organizing architecture that can be instantiated to automate penetration testing of real systems.
- Score: 0.8684482273743697
- License:
- Abstract: Recent changes in standards and regulations, driven by the increasing importance of software systems in meeting societal needs, mandate increased security testing of software systems. Penetration testing has been shown to be a reliable method to asses software system security. However, manual penetration testing is labor-intensive and requires highly skilled practitioners. Given the shortage of cybersecurity experts and current societal needs, increasing the degree of automation involved in penetration testing can aid in fulfilling the demands for increased security testing. In this work, we formally express the penetration testing problem at the architectural level and suggest a general self-organizing architecture that can be instantiated to automate penetration testing of real systems. We further describe and implement a specialization of the architecture in the ADAPT tool, targeting systems composed of hosts and services. We evaluate and demonstrate the feasibility of ADAPT by automatically performing penetration tests with success against: Metasploitable2, Metasploitable3, and a realistic virtual network used as a lab environment for penetration tester training.
Related papers
- VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Framework [4.802551205178858]
Existing large language model (LLM)-assisted or automated penetration testing approaches often suffer from inefficiencies.
VulnBot decomposes complex tasks into three specialized phases: reconnaissance, scanning, and exploitation.
Key design features include role specialization, penetration path planning, inter-agent communication, and generative penetration behavior.
arXiv Detail & Related papers (2025-01-23T06:33:05Z) - PentestAgent: Incorporating LLM Agents to Automated Penetration Testing [6.815381197173165]
Manual penetration testing is time-consuming and expensive.
Recent advancements in large language models (LLMs) offer new opportunities for enhancing penetration testing.
We propose PentestAgent, a novel LLM-based automated penetration testing framework.
arXiv Detail & Related papers (2024-11-07T21:10:39Z) - AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? [54.65079443902714]
We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs.
Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
arXiv Detail & Related papers (2024-11-02T13:24:30Z) - ADAPT: A Game-Theoretic and Neuro-Symbolic Framework for Automated Distributed Adaptive Penetration Testing [13.101825065498552]
The integration of AI into modern critical infrastructure systems, such as healthcare, has introduced new vulnerabilities.
ADAPT is a game-theoretic and neuro-symbolic framework for automated distributed adaptive penetration testing.
arXiv Detail & Related papers (2024-10-31T21:32:17Z) - Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs)
It examines the challenges introduced by AI components and the impact on testing procedures.
The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
arXiv Detail & Related papers (2024-02-21T08:29:42Z) - A General Framework for Verification and Control of Dynamical Models via Certificate Synthesis [54.959571890098786]
We provide a framework to encode system specifications and define corresponding certificates.
We present an automated approach to formally synthesise controllers and certificates.
Our approach contributes to the broad field of safe learning for control, exploiting the flexibility of neural networks.
arXiv Detail & Related papers (2023-09-12T09:37:26Z) - Getting pwn'd by AI: Penetration Testing with Large Language Models [0.0]
This paper explores the potential usage of large-language models, such as GPT3.5, to augment penetration testers with AI sparring partners.
We explore the feasibility of supplementing penetration testers with AI models for two distinct use cases: high-level task planning for security testing assignments and low-level vulnerability hunting within a vulnerable virtual machine.
arXiv Detail & Related papers (2023-07-24T19:59:22Z) - A Requirements-Driven Platform for Validating Field Operations of Small
Uncrewed Aerial Vehicles [48.67061953896227]
DroneReqValidator (DRV) allows sUAS developers to define the operating context, configure multi-sUAS mission requirements, specify safety properties, and deploy their own custom sUAS applications in a high-fidelity 3D environment.
The DRV Monitoring system collects runtime data from sUAS and the environment, analyzes compliance with safety properties, and captures violations.
arXiv Detail & Related papers (2023-07-01T02:03:49Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - Autosploit: A Fully Automated Framework for Evaluating the
Exploitability of Security Vulnerabilities [47.748732208602355]
Autosploit is an automated framework for evaluating the exploitability of vulnerabilities.
It automatically tests the exploits on different configurations of the environment.
It is able to identify the system properties that affect the ability to exploit a vulnerability in both noiseless and noisy environments.
arXiv Detail & Related papers (2020-06-30T18:49:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.