How to Secure Your Large Language Model Applications - at least partially
OWASP Top 10 for LLM
Large Language Models (LLMs) are powerful tools that can generate natural language texts, answer questions, summarize documents, and more. However, they also pose significant security risks that need to be addressed before deploying them in production. In this blog post, we will explore the OWASP Top 10 for LLM Applications, a list of the most common and critical vulnerabilities that affect LLM applications, and how to prevent or mitigate them. I strongly recommend to read the document if you are building applications leveraging large language models. This blog is mainly intended to provide a short concise summary and raise the awareness for the topic.
Why are Large Language Models risky?
LLMs are risky because they can be exploited by malicious actors to perform various attacks, such as:
Prompt injection: manipulating the input or output of an LLM to alter its behavior or produce harmful content.
Data leakage: extracting sensitive or private information from an LLM's training data or internal state.
Inadequate sandboxing: allowing an LLM to access or execute unauthorized resources or code on the host system or network.
Unauthorized code execution: injecting or executing malicious code through an LLM's input or output.
Adversarial examples: crafting inputs that cause an LLM to produce incorrect or misleading outputs.
Model poisoning: tampering with an LLM's training data or parameters to degrade its performance or introduce biases.
Model stealing: copying or reverse-engineering an LLM's architecture or weights without authorization.
Model evasion: bypassing or fooling an LLM's security mechanisms or defenses.
These attacks can have serious consequences, such as compromising the confidentiality, integrity, or availability of an LLM application, violating the privacy or rights of users or data subjects, causing financial losses or reputational damage, or even endangering human lives.
What is the OWASP Top 10 for LLM Applications?
The OWASP Top 10 for LLM Applications is a standard awareness document for software engineers and architects. It represents a broad consensus about the most critical security risks to LLM applications. It is based on data analysis, expert opinions, and community feedback. It provides a concise description of each risk, its potential impact, ease of exploitation, and prevalence in real-world applications. It also suggests some remediation strategies and best practices to prevent or mitigate each risk.
The OWASP Top 10 for LLM Applications consists of the following categories:
A01: Prompt Injection
A02: Data Leakage
A03: Inadequate Sandboxing
A04: Unauthorized Code Execution
A05: Adversarial Examples
A06: Model Poisoning
A07: Model Stealing
A08: Model Evasion
A09: Insecure Design
A10: Insufficient Logging and Monitoring
How to secure your LLM applications?
Securing your LLM applications requires a holistic approach that covers the entire lifecycle of an LLM project, from design to deployment to maintenance. Some examples for general principles and practices that can help you secure your LLM applications are:
Follow the principle of least privilege: limit the access and permissions of an LLM to the minimum necessary for its functionality.
Implement input and output validation: check and sanitize the inputs and outputs of an LLM to prevent injection attacks or harmful content.
Use secure communication channels: encrypt and authenticate the data exchanged between an LLM and other components or users.
Isolate and sandbox an LLM: run an LLM in a separate environment that restricts its access to resources or code on the host system or network.
Monitor and audit an LLM: collect and analyze logs and metrics of an LLM's activity and performance to detect anomalies or incidents.
Test and evaluate an LLM: perform regular security testing and evaluation of an LLM using various methods and tools.
Educate and train users: inform and train users about the capabilities and limitations of an LLM, as well as their responsibilities and risks when using it.
Why Prompt Injection cannot be prevented completely?
Prompt injection works by exploiting the nature of LLMs, which do not segregate instructions and external data from each other. Instructions are commands or queries that tell an LLM what to do or generate. External data are information or content that come from outside sources, such as users or websites. Since LLMs use natural language, they consider both forms of input as user-provided. Consequently, there is no fool-proof prevention within the LLM, there are only measures, which can mitigate the impact of prompt injections (e.g. least privilege, trust boundaries, human in the loop, ...).
Image was generated with Bing Image Creator
What are some examples of prompt injection attacks?
Prompt injection attacks can take various forms and have different objectives, depending on the attacker's goals and the LLM's functionality. Some examples of prompt injection attacks are:
Data leakage: An attacker injects a query into an LLM's input that asks it to reveal sensitive or private information from its training data or internal state. For example, an attacker could ask an LLM that generates summaries for documents to summarize its own source code or credentials.
Unauthorized code execution: An attacker injects a code snippet into an LLM's input or output that executes arbitrary code on the host system or network. For example, an attacker could inject a script tag into an LLM that generates captions for images that runs malicious code on the user's browser.
Adversarial examples: An attacker injects a misleading or offensive text into an LLM's output that causes it to produce incorrect or harmful outputs. For example, an attacker could inject a negative sentiment into an LLM that generates product reviews that influences the user's perception or decision.
How can we learn more about prompt injection?
Prompt injection is a complex and evolving problem that requires constant research and awareness. If you want to learn more about prompt injection, you can refer to the following resources:
OWASP Top 10 for LLM Applications: A standard awareness document for developers and web application security that provides a concise description of prompt injection and other common and critical security risks to LLM applications.
Defensive Measures: A collection of resources and best practices for mitigating prompt injection attacks.
How can we mitigate prompt injection?
Prompt injection cannot be prevented completely due to the nature of LLMs, but it can be mitigated by applying some security principles and practices throughout the lifecycle of an LLM project. Some examples of these principles and practices are:
Input and output validation: Checking and sanitizing the inputs and outputs of an LLM to prevent injection attacks or harmful content.
Isolation and sandboxing: Running an LLM in a separate environment that restricts its access to resources or code on the host system or network.
Human in the loop: Involving a human moderator or reviewer in the process of generating or consuming outputs from an LLM.
Conclusion
LLMs are amazing technologies that can enable many innovative applications and services. However, they also pose significant security challenges that need to be addressed before deploying them in production. The OWASP Top 10 for LLM Applications is a valuable resource that can help you understand and mitigate the most common and critical security risks to LLM applications. By following the recommendations and best practices in this document, you can improve the security posture of your LLM applications and protect them from potential attacks. At the same time you need to be aware that some risks can hardly be mitigated e.g. prompt injection.
Resources
OWASP Top 10 for Large Language Model Applications. https://owasp.org/www-project-top-10-for-large-language-model-applications/
OWASP Top Ten | OWASP Foundation. https://owasp.org/www-project-top-ten/
OWASP/www-project-top-10-for-large-language-model-applications. https://github.com/OWASP/www-project-top-10-for-large-language-model-applications/
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