OWASP LLM Top 10: ENISA

# OWASP Top 10 for LLMs Mapped to ENISA Recommendations

This document outlines how the [OWASP Top 10 for Large Language Model Applications](https://owasp.org/www-project-top-10-for-large-language-model-applications/#) can be addressed using the [European Union Agency for Cybersecurity (ENISA) recommendations](https://www.enisa.europa.eu/). ENISA provides comprehensive guidelines for improving cybersecurity, which can be leveraged to mitigate risks associated with LLM vulnerabilities.

## ENISA Cybersecurity Recommendations

To enhance the security posture of LLMs and mitigate vulnerabilities, organizations can adopt the following ENISA recommendations:

### LLM01: Prompt Injection

- **Risk Management**: Implement a thorough risk assessment process to identify and mitigate risks associated with prompt injection vulnerabilities.
- **Security Measures**: Apply input validation and sanitization techniques to prevent malicious inputs from affecting LLM outputs.

### LLM02: Insecure Output Handling

- **Data Protection**: Ensure that data handling and output generation processes incorporate security measures to prevent data leaks and tampering.
- **Incident Response**: Develop and implement an incident response plan that includes procedures for handling incidents involving insecure output handling.

### LLM03: Training Data Poisoning

- **Supply Chain Security**: Secure the supply chain for training data to prevent poisoning attacks, including vetting sources and implementing integrity checks.
- **Awareness and Training**: Increase awareness among stakeholders involved in data collection and model training about the risks of data poisoning.

### LLM04: Model Denial of Service

- **Business Continuity**: Prepare business continuity and disaster recovery plans that include scenarios for dealing with model denial of service attacks.
- **Technical Measures**: Implement rate limiting, resource allocation, and other technical measures to protect against DoS attacks.

### LLM05: Supply-Chain Vulnerabilities

- **Supply Chain Security**: Conduct security assessments of third-party vendors and integrate security considerations into the procurement process.
- **Third-Party Risk Management**: Establish a comprehensive third-party risk management framework to continuously monitor and manage the security of third-party components.

### LLM06: Sensitive Information Disclosure

- **Data Protection and Privacy**: Apply strict data protection measures, including encryption and access control, to safeguard sensitive information processed by LLMs.
- **Compliance**: Ensure compliance with relevant data protection regulations, such as the [General Data Protection Regulation (GDPR)](https://gdpr-info.eu/), to prevent unauthorized disclosure of personal data.

### LLM07: Insecure Plugin Design

- **Secure Development**: Follow secure development practices for the design and implementation of plugins, including security testing and code reviews.
- **Vendor Security Assessment**: Assess the security of third-party plugins before integration into the LLM ecosystem.

### LLM08: Excessive Agency

- **Ethical Considerations**: Address ethical considerations in the design and deployment of LLMs to ensure that they do not exceed their intended agency.
- **Security by Design**: Incorporate security and ethical guidelines into the development lifecycle of LLMs to control their decision-making capabilities.

### LLM09: Overreliance

- **Awareness and Training**: Conduct training sessions to educate users about the limitations of LLMs and the risks associated with overreliance.
- **Human Oversight**: Implement mechanisms for human oversight and intervention in critical decision-making processes involving LLMs.

### LLM10: Model Theft

- **Intellectual Property Protection**: Protect intellectual property related to LLMs through legal and technical measures, including access controls and encryption.
- **Incident Response**: Include model theft scenarios in the incident response plan, outlining steps to detect, respond to, and recover from such incidents.

Note: The application of ENISA's recommendations requires a strategic approach tailored to the specific context and risks associated with LLM technologies. Organizations should engage in continuous improvement processes to address emerging vulnerabilities and threats in the evolving landscape of large language models.