[Report Released] That AI will stop your business. 5 In-house AI vulnerabilities identified through real attacks.

MONO BRAIN Inc. (Headquarters: Shibuya, Tokyo; CEO: Masanori Kato), developer of the AI security platform 'MODEL SAFE', has released its latest safety verification report for enterprise AI applications, titled '5 In-house AI Vulnerabilities Identified through Real Attacks (April 2026 Edition)'. This report provides details on 'Critical' vulnerabilities that threaten business continuity and specific defense strategies, based on insights from over 3,000 attack detections in production environments.

▼ Download the Report (Free) https://modelsafe.jp/download/ai_vulnerability_report_202604

Background: Design and Operational 'Flaws', Not AI Performance, Create Security Risks As many companies internalize AI development, flaws in security design are emerging as 'unseen withdrawal risks'. In this empirical test using Google's open-source LLM 'Gemma 3:4B'—a class widely used for general business AI—it was revealed that sensitive information leakage and database destruction can easily occur by exploiting design blind spots.

5 Proven Vulnerabilities The report exposes critical defects in standard configurations using general business AI (Gemma 3:4B):

1. Prompt Injection (Information Leakage): Confidential internal costs and customer strategies are leaked in plain text through role-change instructions.

2. Tool Privilege Abuse (Data Destruction): Exploiting weak authentication and privilege design to delete entire customer databases externally.

3. Indirect Prompt Injection (Guardrail Bypass): Data contamination via business partner emails causes the AI to generate and output fake personal information.

4. Supply Chain Vulnerability (Judgment Takeover): A backdoor that bypasses accuracy evaluations to manipulate credit scores for specific applicants.

5. Data Poisoning (Filter Inactivation): A single piece of fake feedback reverses the system's judgment logic (e.g., spam detection).

Improvement Roadmap: Towards a Governance Foundation Based on test results, the report suggests steps for immediate action:

Phase 1 (0-1 month): Data minimization, adding authentication, restricting DB privileges. Phase 2 (1-3 months): Building input/output sanitization layers, implementing model consistency verification. Phase 3 (3 months+): Operating audit logs, conducting regular red teaming.

About the AI Security Platform 'MODEL SAFE' 'MODEL SAFE' is an integrated platform that protects corporate AI systems from prompt injection and supply chain attacks. Beyond simple defense, it provides monitoring and policy violation detection based on vast detection records in production, offering a solid governance foundation for future regulatory compliance.

[MONO BRAIN Inc. Company Profile] Representative: Masanori Kato Business: Development and operation of the AI security/governance platform 'MODEL SAFE' Full Member of the AI Governance Association

▼ Contact https://modelsafe.jp/contact

▼ MODEL SAFE Service Introduction https://modelsafe.jp/