The OWASP Top 10 for Large Language Model Applications

As cybersecurity experts, we must be aware of the potential security risks when deploying and managing?Large Language Models?(LLMs). The OWASP Top 10 for Large Language Model Applications project aims to educate developers, designers, architects, managers, and organizations about the most critical security risks when working with LLMs.The project provides a list of the top 10 most critical vulnerabilities related to LLMs, which include unauthorized code execution, data leakage, and model poisoning. The goal of this project is to raise awareness of these vulnerabilities, suggest remediation strategies, and ultimately improve the security posture of LLM applications. The OWASP Top 10 for Large Language Model Applications is a draft list of important vulnerability types for Artificial Intelligence (AI) applications built on LLMs. The list is designed to initiate discussion as we work towards a vetted, first official list. More details on each issue are available in the?GitHub?repository for the project. As cybersecurity experts, we must take the necessary steps to protect our organizations against these vulnerabilities. By adopting the OWASP Top 10 for Large Language Model Applications, we can take the most effective first step towards changing our software development culture focused on producing secure code. For more information on the OWASP Top 10 for Large Language Model Applications, check out the project's website and resources page. Let's work together to improve the security posture of LLM applications and protect our organizations from potential security risks.

Here are some of the most common security risks for Large Language Model Applications:

• Unauthorized code execution: Exploiting LLMs to execute malicious code, commands, or actions on the underlying system through natural language prompts.
• Data leakage: Exploiting LLMs to perform unintended requests or access restricted resources, such as internal services, APIs, or data stores.
• Bypassing filters or manipulating the LLM: Using carefully crafted prompts that make the model ignore previous instructions or perform unintended actions.
• Insecure access controls or authentication: Not properly implementing access controls or authentication, allowing unauthorized users to interact with the LLM and potentially exploit vulnerabilities.
• Exposing error messages or debugging information: Revealing sensitive information, system details, or potential attack vectors.
• Model poisoning: Maliciously manipulating training data or fine-tuning procedures to introduce vulnerabilities.

We must take the necessary steps to protect our organizations against these vulnerabilities. By adopting the OWASP Top 10 for Large Language Model Applications, we can take the most effective first step towards changing our software development culture focused on producing secure code.

There are some tools and frameworks available to help developers identify and mitigate security risks in Large Language Model Applications.

Here are some examples:

• OWASP Top 10 for Large Language Model Applications: This project provides a list of the top 10 most critical vulnerabilities related to?LLMs, which include unauthorized code execution, data leakage, and model poisoning. The project aims to educate developers, designers, architects, managers, and organizations about the potential security risks when deploying and managing LLMs, and suggests remediation strategies to improve the security posture of LLM applications.
• Guardrails: This tool allows developers to create programmable rules for interactions between the user and AI app. It supports?LangChain, a collection of toolkits that include templates and patterns that tie together LLMs, APIs, and other software. Guardrails, adds another layer of security and another system that might call language.
• LangChain: This is a collection of toolkits that include templates and patterns that tie together LLMs, APIs, and other software. It supports Guardrails, which adds another layer of security and another system that might call language.
• Hierarchical levels of artificial neural networks: This approach uses hierarchical levels of artificial neural networks to enable?Machine Learning?(ML) algorithms to perform feature extraction and transformation. This can help identify and mitigate security risks in Large Language Model Applications.

As cybersecurity experts, we must take the necessary steps to protect our organizations against these vulnerabilities. By adopting these tools and frameworks, we can take the most effective first step towards changing our software development culture focused on producing secure code.

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https://owasp.org/www-project-top-10-for-large-language-model-applications/descriptions/