#Japan #Generative AI #Minato #EG Secure #LLM vulnerability

EG Secure Solutions Launches LLM Vulnerability Diagnosis Service

In an era where generative AI is rapidly becoming integrated into business operations, the necessity for robust security measures cannot be overstated. EG Secure Solutions, a subsidiary of e-Guardian, has recently introduced its innovative LLM Vulnerability Diagnosis Service aimed at identifying vulnerabilities within generative AI and large language model (LLM) applications. This service emerges as a response to the complex security challenges posed by these advanced technologies.

The Growing Need for Security in AI

As more companies leverage generative AI, traditional security protocols are proving inadequate against new forms of risks. One of the pressing concerns is data poisoning, where malicious entities manipulate the training data to introduce biases or generate false information. Furthermore, the sheer volume of data that generative AI systems utilize increases the risk of inadvertent leaks of confidential information. In light of these emerging threats, EG Secure Solutions responds by providing targeted security assessments tailored specifically for LLM applications.

Service Overview

EG Secure Solutions' LLM Vulnerability Diagnosis Service focuses on identifying specific vulnerabilities that may exist in chatbots and business support tools that employ LLM technology. The service is developed based on the OWASP Top 10 for LLM Applications 2025, fortified by EG Secure Solutions' extensive expertise in security assessments. By offering a specialized diagnosis, the service aims to enhance not only the security but also the reliability and brand image of implementing businesses.

Key Features

1. Risk Assessment Tailored to LLM Characteristics: The service focuses on unique risks related to the structure and functionality of LLMs, ensuring a comprehensive evaluation.
2. Use Case-Specific Diagnostics: By considering specific operational forms and dialogue designs of LLMs, the service extracts relevant risk elements to conduct more realistic validations.
3. Addressing Model-Specific Risks: The analysis extends beyond standard attack methods to explore potential risks tied to the unique behaviors of specific LLM models.

Diagnostic Focus Areas

• - Detection of personal and confidential information leakage
• - Assessment of tendencies for misinformation (hallucination)
• - Evaluation of risks related to leaked system prompts and training data
• - Validation of vulnerabilities in plugins and external integrations.

Case Study: Bengo4

The LLM Vulnerability Diagnosis Service has been initially adopted by Bengo4.com, an AI agent specializing in legal services. In legal practice, the consequences of false information can be severe, given that confidential data is frequently handled. Thus, the implementation of this service not only acts as a security checkpoint but also establishes new standards for privacy, accuracy, and trustworthiness in generative AI applications.

Conclusion

As part of the e-Guardian group, EG Secure Solutions is committed to advancing high-quality services that address the complexities of modern digital security. Their mission, encapsulated in the phrase “We Guard All,” underlines their commitment to creating safe and reliable environments for all users while contributing to the convenience and richness of life through innovative products and services. As generative AI continues to evolve, so too do the strategies that safeguard its integrity and effectiveness.

For more details on the LLM Vulnerability Diagnosis Service, visit here.