AI Penetration Testing Agents for Social Engineering Vulnerability Detection
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Solution Overview
Problem
Conventional penetration testing is costly, time-consuming, and struggles to keep pace with evolving cybersecurity threats, particularly due to the need for human experts and the difficulty in simulating social engineering attacks accurately.
Innovation Solution
Utilizing automated reinforcement learning (ARL) with large language models (LLMs) to create a computer agent that performs penetration tests, incorporating social engineering methods, enabling rapid and iterative vulnerability detection in simulated or real environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts conduct penetration testing, then security vulnerability detection accuracy is improved, but testing cost and time consumption increase
Solution Approach 1:
The patent creates a simulated environment that copies real-world network conditions, devices, and vulnerabilities. This simulation allows automated agents to practice and learn penetration testing techniques without requiring human experts for every test, thereby reducing time consumption while maintaining detection accuracy through realistic scenario replication.
Solution Approach 2:
The patent replaces human experts (mechanical system) with automated reinforcement learning agents. These agents use machine learning algorithms to perform penetration testing tasks autonomously, eliminating the time-consuming manual processes while maintaining or improving detection accuracy through systematic exploration of vulnerability patterns.
2Loss of information
If human experts conduct penetration testing, then security knowledge and insights are improved, but resource requirements and cost increase
Solution Approach 1:
The patent develops a multi-functional automated agent system that can perform various penetration testing tasks across different network environments and vulnerability types. This universal agent replaces the need for multiple specialized human experts, reducing resource requirements while maintaining comprehensive security knowledge through integrated learning capabilities.
Solution Approach 2:
The simulated environment copies complex real-world security scenarios, allowing automated agents to acquire security knowledge through repeated practice and learning. This eliminates the need for expensive human expert resources while preserving comprehensive security insights through realistic scenario replication and automated knowledge accumulation.
3Speed
If penetration testing is performed rapidly, then response speed to evolving threats is improved, but testing accuracy and thoroughness may worsen
Solution Approach 1:
The patent implements preliminary training of automated agents in simulated environments before deploying them to real systems. This preliminary action allows agents to develop accurate vulnerability detection capabilities through extensive practice, enabling rapid real-world testing without sacrificing accuracy since the agents have already learned thorough detection patterns in advance.
Solution Approach 2:
The patent enables continuous automated penetration testing through reinforcement learning agents that can operate without interruption. The agents continuously learn from each test result and improve their detection accuracy over time, maintaining high precision even at rapid speeds through uninterrupted learning and adaptation cycles.
4Object-affected harmful factors
If simulated environments are used for training, then testing cost and risk are reduced, but realism and applicability to production networks may worsen
Solution Approach 1:
The patent uses simulated environments for preliminary training of automated agents before deployment to production networks. This staged approach allows agents to learn basic penetration testing skills safely in simulation, then gradually transition to more realistic environments, ensuring both low training risk and high production applicability through progressive adaptation.
Solution Approach 2:
The patent implements dynamic training environments that can adjust their realism level based on agent development stage. Early training uses simplified simulations for safety, while later stages introduce progressively more complex and realistic scenarios, optimizing both risk reduction and production network applicability through adaptive environment complexity.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for determining a security vulnerability of a computer system. An example method includes initializing a policy based on initial policy data. The example method further includes selecting an action based on the policy and executing, by agent circuitry, the action in the environment. The example method further includes, subsequent to executing the action in the environment, receiving an observation of the environment and determining an updated state from the set of states based on the observation. The example method further includes determining, by the policy, a reward based on the updated state and updating the policy based on the updated state.