BreachSeek: A Multi-Agent Automated Penetration Tester
The increasing complexity and scale of modern digital environments have exposed significant gaps in traditional cybersecurity penetration testing methods, which are often time-consuming, labor-intensive, and unable to rapidly adapt to emerging threats. There is a critical need for an automated solution that can efficiently identify and exploit vulnerabilities across diverse systems without extensive human intervention. BreachSeek addresses this challenge by providing an AI-driven multi-agent software platform that leverages Large Language Models (LLMs) integrated through LangChain and LangGraph in Python. This system enables autonomous agents to conduct thorough penetration testing by identifying vulnerabilities, simulating a variety of cyberattacks, executing exploits, and generating comprehensive security reports. In preliminary evaluations, BreachSeek successfully exploited vulnerabilities in exploitable machines within local networks, demonstrating its practical effectiveness. Future developments aim to expand its capabilities, positioning it as an indispensable tool for cybersecurity professionals.
Code (1)
Similar Papers 제목 키워드 기반
AutoPentester: An LLM Agent-based Framework for Automated Pentesting
Penetration testing and vulnerability assessment are essential industry practices for safeguarding computer systems. As cyber threats grow in scale and complexity, the demand for pentesting has surged, surpassing the cap…
RefPentester: A Knowledge-Informed Self-Reflective Penetration Testing Framework Based on Large Language Models
Automated penetration testing (AutoPT) powered by large language models (LLMs) has gained attention for its ability to automate ethical hacking processes and identify vulnerabilities in target systems by leveraging the i…
Automated Video Game Testing Using Synthetic and Human-Like Agents
In this paper, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents -synthetic and human-like- and two distinct approaches to create them. Our agents ar…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)RapidPen: Fully Automated IP-to-Shell Penetration Testing with LLM-based Agents
We present RapidPen, a fully automated penetration testing (pentesting) framework that addresses the challenge of achieving an initial foothold (IP-to-Shell) without human intervention. Unlike prior approaches that focus…
Task PlanningRaijū: Reinforcement Learning-Guided Post-Exploitation for Automating Security Assessment of Network Systems
In order to assess the risks of a network system, it is important to investigate the behaviors of attackers after successful exploitation, which is called post-exploitation. Although there are various efficient tools sup…
Reinforcement Learning (RL)