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OWASP Top 10 for LLM Applications: A Guide to Secure AI

Secure your LLM applications! Learn about the OWASP Top 10 vulnerabilities, including prompt injection, data poisoning, and misinformation. Discover best practices to mitigate risks and build robust and ethical AI systems.

admin

Dec 21, 2024 • 3 min read

In this post we will see how to Secure your LLM applications! Learn about the OWASP Top 10 vulnerabilities, including prompt injection, data poisoning, and misinformation. Discover best practices to mitigate risks and build robust and ethical AI systems.

Introduction

Large Language Models (LLMs) are revolutionizing how we interact with technology. From chatbots to code generation, their applications are expanding rapidly. However, this growth also brings new security risks. The OWASP Top 10 for LLM Applications highlights the most critical security vulnerabilities in LLM applications, providing developers and security professionals with essential guidance to build secure AI systems.

Top 10 Vulnerabilities

Here's a breakdown of the OWASP Top 10 for LLM Applications:

Rank	Vulnerability	Description
LLM01	Prompt Injection	Occurs when user inputs manipulate the LLM's behavior, potentially leading to data leaks, unauthorized access, and harmful outputs.
LLM02	Sensitive Information Disclosure	Sensitive data, such as PII or proprietary algorithms, can be inadvertently exposed through LLM outputs, resulting in privacy violations and intellectual property breaches.
LLM03	Supply Chain	Vulnerabilities in third-party models, datasets, and platforms used in the LLM supply chain can compromise model integrity, leading to biased outputs or security breaches.
LLM04	Data and Model Poisoning	Manipulation of training data can introduce vulnerabilities, backdoors, or biases, leading to compromised model security, performance, or ethical behavior.
LLM05	Improper Output Handling	Insufficient validation and sanitization of LLM outputs can result in security vulnerabilities like XSS, CSRF, SSRF, privilege escalation, and remote code execution when passed downstream to other systems.
LLM06	Excessive Agency	Granting LLMs excessive autonomy or permissions can lead to unintended or damaging actions, especially when coupled with prompt injection or model hallucinations.
LLM07	System Prompt Leakage	Leakage of system prompts, which guide LLM behavior, can expose sensitive information or reveal internal workings of the application, potentially enabling attackers to bypass security controls.
LLM08	Vector and Embedding Weaknesses	Vulnerabilities in how vectors and embeddings are generated, stored, or retrieved in Retrieval Augmented Generation (RAG) can lead to data poisoning, unauthorized access, and manipulation of model outputs.
LLM09	Misinformation	LLMs can generate false or misleading information that appears credible, leading to security breaches, reputational damage, and legal liability. Hallucinations and biases contribute to this vulnerability.
LLM10	Unbounded Consumption	Excessive and uncontrolled inference requests can lead to denial of service (DoS), economic losses due to resource exhaustion, and potential model theft through extraction techniques.

Best Practices for Secure LLM Applications

Prompt Injection:

Constrain model behavior with specific instructions.
Validate expected output formats with deterministic code.
Implement input and output filtering.
Enforce privilege control and least privilege access.

Sensitive Information Disclosure:

Integrate data sanitization techniques.
Enforce strict access controls and data source restrictions.
Educate users on safe LLM usage.

Supply Chain:

Vet data sources and suppliers carefully.
Apply comprehensive AI Red Teaming and evaluations.
Maintain an up-to-date inventory of components using SBOMs.

Data and Model Poisoning:

Track data origins and transformations.
Implement strict sandboxing and anomaly detection.
Use data version control and conduct adversarial robustness tests.

Improper Output Handling:

Treat the model as an untrusted user.
Follow OWASP ASVS guidelines for input validation and output encoding.
Employ strict Content Security Policies (CSP).

Excessive Agency:

Minimize extensions, their functionality, and permissions.
Execute extensions in the user's context.
Require user approval for high-impact actions.

System Prompt Leakage:

Separate sensitive data from system prompts.
Avoid reliance on system prompts for strict behavior control.
Implement guardrails outside the LLM.

Vector and Embedding Weaknesses:

Implement fine-grained access controls for vector and embedding stores.
Validate data sources and authenticate their origin.
Monitor and log retrieval activities.

Misinformation:

Use Retrieval-Augmented Generation (RAG) to enhance output reliability.
Fine-tune models and implement cross-verification processes.
Communicate risks and limitations to users.

Unbounded Consumption:

Implement input validation and rate limiting.
Monitor resource allocation and set timeouts.
Train models for adversarial robustness and implement watermarking.

Conclusion

Securing LLM applications is paramount to ensure responsible and trustworthy AI. By understanding the OWASP Top 10 and implementing the best practices outlined, developers can mitigate risks and build robust, secure, and ethical AI systems. Remember that continuous monitoring, adaptation, and collaboration within the AI security community are crucial to stay ahead of evolving threats.