LLM Voice and Text Deception for Malicious Thread Engagement
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Solution Overview
Problem
Existing chatbots lack robust security measures, making them vulnerable to exploitation by attackers, who can manipulate them to extract sensitive information, posing a risk to organizational security and data integrity.
Innovation Solution
Employing Large Language Models (LLM) to create lifelike deceptions that mimic human communication patterns, engaging attackers and gathering counterintelligence while maintaining the illusion of a genuine interaction, with capabilities for continuous learning and honeypot strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If chatbots are deployed to engage in conversations with users, then customer support and information retrieval capabilities are improved, but vulnerability to exploitation by attackers increases
Solution Approach 1:
The patent introduces an intrusion prevention system as an intermediary layer between the chatbot and attackers. This system monitors communications, analyzes behavioral patterns, and blocks malicious interactions before they can exploit the chatbot, thereby maintaining customer support functionality while preventing exploitation
Solution Approach 2:
The patent converts the chatbot's vulnerability to exploitation into a benefit by training the intrusion prevention system to recognize attack patterns. The same conversational capabilities that make the chatbot vulnerable are also what make it effective at detecting and analyzing malicious behavior, turning the weakness into a security feature
2Productivity
If chatbots are connected to databases to provide information, then information retrieval capability is improved, but risk of unauthorized access to data stores increases
Solution Approach 1:
The intrusion prevention system serves as a mediator between the chatbot and the database. It monitors and controls all data access requests, allowing legitimate information retrieval while blocking unauthorized access attempts, thus maintaining productivity while reducing security risk
Solution Approach 2:
The system performs preliminary analysis of chatbot communications to identify potential data access requests before they are executed. By pre-screening these requests and establishing access control rules in advance, the system prevents unauthorized database access while allowing legitimate information retrieval
3Reliability
If security measures are strengthened to protect chatbots, then reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The intrusion prevention system applies security measures selectively rather than uniformly. It monitors all communications but only blocks identified malicious interactions, allowing legitimate conversational flow to proceed uninterrupted while maintaining strong security protection against actual threats
Data Source
AI summary
The current technology involves a process of determining the likelihood of a received electronic communication to a first user being malicious by a content inspection service. If the communication is deemed suspicious, it will be directed to a generative artificial intelligence (AI) tool for engagement. All subsequent communications in the same thread will also be directed to the AI tool unless the first user explicitly requests control over the thread. The AI tool will then respond to the suspicious communication while posing as the first user, but without revealing any confidential information. This process helps to prevent potential attacks by remvoing the thread of malicious communications.