OWASP Top 10 LLM Vulnerabilities: A Critical Security Guide

Introduction

As Large Language Models (LLMs) become increasingly integral to modern applications, understanding their security vulnerabilities is crucial. OWASP has identified the top 10 security risks specific to LLM implementations that every developer and security professional should know.

The Top 10 Vulnerabilities

Prompt Injection

Prompt Injection occurs when an attacker manipulates a large language model through crafty inputs, causing unintended actions by the LLM. Direct injections overwrite system prompts, while indirect ones manipulate inputs from external sources. This can lead to data exfiltration, social engineering, and other issues.

Prevention strategies include enforcing privilege control on LLM access to backend systems and adding a human in the loop for extended functionality. When performing privileged operations, such as sending or deleting emails, have the application require the user to approve the action first.

Insecure Output Handling

This vulnerability occurs when an LLM output is accepted without scrutiny, exposing backend systems. Misuse may lead to severe consequences like XSS, CSRF, SSRF, privilege escalation, or remote code execution.

To mitigate this risk, treat the model as any other user, adopting a zero-trust approach, and apply proper input validation on responses coming from the model to backend functions. Follow the OWASP ASVS (Application Security Verification Standard) guidelines to ensure effective input validation and sanitization.

Training Data Poisoning

This occurs when LLM training data is tampered with, introducing vulnerabilities or biases that compromise security, effectiveness, or ethical behavior. Sources include Common Crawl, WebText, OpenWebText, & books.

Prevention strategies include verifying the supply chain of the training data, especially when sourced externally, and maintaining attestations via the "ML-BOM" (Machine Learning Bill of Materials) methodology. Use strict vetting or input filters for specific training data or categories of data sources to control the volume of falsified data.

Model Denial of Service

Attackers cause resource-heavy operations on LLMs, leading to service degradation or high costs. The vulnerability is magnified due to the resource-intensive nature of LLMs and the unpredictability of user inputs.

To prevent this, implement input validation and sanitization, cap resource use per request or step, and enforce API rate limits. Continuously monitor the resource utilization of the LLM to identify abnormal spikes or patterns that may indicate a DoS attack.

Supply Chain Vulnerabilities

The LLM application lifecycle can be compromised by vulnerable components or services, leading to security attacks. Using third-party datasets, pre-trained models, and plugins can add vulnerabilities.

Mitigation strategies include carefully vetting data sources and suppliers, including T&Cs and their privacy policies, only using trusted suppliers. Implement sufficient monitoring to cover component and environment vulnerabilities scanning, use of unauthorized plugins, and out-of-date components.

Sensitive Information Disclosure

LLMs may inadvertently reveal confidential data in their responses, leading to unauthorized data access, privacy violations, and security breaches. It's crucial to implement data sanitization and strict user policies to mitigate this.

Prevention strategies include integrating adequate data sanitization and scrubbing techniques to prevent user data from entering the training model data. Implement robust input validation and sanitization methods to identify and filter out potential malicious inputs to prevent the model from being poisoned.

Insecure Plugin Design

LLM plugins can have insecure inputs and insufficient access control. This lack of application control makes them easier to exploit and can result in consequences like remote code execution.

To mitigate this, plugins should enforce strict parameterized input wherever possible and include type and range checks on inputs. Plugins should be designed to minimize the impact of any insecure input parameter exploitation following the OWASP ASVS Access Control Guidelines.

Excessive Agency

LLM-based systems may undertake actions leading to unintended consequences. The issue arises from excessive functionality, permissions, or autonomy granted to the LLM-based systems.

Prevention strategies include limiting the plugins/tools that LLM agents are allowed to call to only the minimum functions necessary. Limit the permissions that LLM plugins/tools are granted to other systems to the minimum necessary in order to limit the scope of undesirable actions.

Overreliance

Systems or people overly depending on LLMs without oversight may face misinformation, miscommunication, legal issues, and security vulnerabilities due to incorrect or inappropriate content generated by LLMs.

To mitigate this, regularly monitor and review the LLM outputs. Use self-consistency or voting techniques to filter out inconsistent text. Cross-check the LLM output with trusted external sources. This additional layer of validation can help ensure the information provided by the model is accurate and reliable.

Model Theft

This involves unauthorized access, copying, or exfiltration of proprietary LLM models. The impact includes economic losses, compromised competitive advantage, and potential access to sensitive information.

Prevention strategies include implementing strong access controls (e.g., RBAC and rule of least privilege) and strong authentication mechanisms to limit unauthorized access to LLM model repositories and training environments. Regularly monitor and audit access logs and activities related to LLM model repositories to detect and respond to any suspicious or unauthorized behavior promptly.

In conclusion, as AI and LLMs continue to evolve and become more integrated into our daily lives and business operations, understanding and addressing these top 10 vulnerabilities is crucial for developers. By implementing robust security measures and staying informed about emerging threats and best practices, developers can create AI systems that are not only powerful and efficient but also secure and trustworthy.

Best Practices for Mitigation

1. Regular security assessments
2. Comprehensive input/output validation
3. Monitoring and logging systems
4. Clear security policies
5. Employee training on LLM security

Conclusion

As LLM adoption grows, understanding and addressing these vulnerabilities becomes critical. Organizations must prioritize security measures to protect their AI systems and users.