Insights from OWASP Top 10 for LLM Applications on Generative AI Security

The Open Web Application Security Project (OWASP) has put together the Top 10 list for LLM applications to raise awareness about application security risks in the context of generative AI. While some risks associated with large language models (LLMs) are well-known, there is often a lack of understanding of where AI security fits into the broader cybersecurity landscape. This can lead to either underestimating or overestimating the risks associated with AI deployment.

LLMs have gained significant attention in the current AI landscape, but they represent just one category of artificial intelligence. Before delving into the top 10 risks specific to LLM applications, it’s important to clarify the terminology:

• A large language model is a massive piece of code that processes text instructions to generate results through complex neural networks with billions of parameters.
• An LLM application is any software that communicates with an LLM to send data and receive outcomes. These applications span a wide range of functionalities, from chatbots to business software.

Invicti does not utilize data from large language models in its products for automated application and API security testing. The need for accurate and reliable results in testing rules out the use of LLMs.

 

For more information on how Invicti leverages machine learning for AI benefits without the drawbacks of LLMs, refer to our post on Predictive Risk Scoring.

Reworking the Top 10 for LLM apps based on risk areas

Similar to other OWASP initiatives, the Top 10 list for LLM applications serves as a guide to understanding the primary sources of application security risks. These risks are interconnected and stem from general security vulnerabilities. Let’s explore the overarching themes behind the top 10 LLM risks to gain insights into the current landscape of LLM applications.

Challenges posed by black box systems

Prompt injection attacks are a major concern with LLMs, highlighting fundamental issues associated with these black box systems. LLMs generate results without providing insight into the reasoning behind them, raising concerns around data privacy and security:

• LLM01: Prompt Injection involves manipulating system prompts to influence LLM responses.
• LLM03: Training Data Poisoning allows attackers to influence LLM outcomes by tampering with training data.
• LLM06: Sensitive Information Disclosure underscores the risk of exposing sensitive data used in LLM training.

Risks of blind reliance on LLMs

Automating processes using LLM-generated data poses unique security challenges, highlighting the need for careful handling of outputs to prevent vulnerabilities:

• LLM02: Insecure Output Handling emphasizes the importance of sanitizing LLM outputs to prevent potential attacks.
• LLM08: Excessive Agency warns against uncontrolled LLM actions that may lead to unintended consequences.
• LLM09: Overreliance cautions against blindly trusting LLM suggestions that could result in security flaws.

Threats targeting LLM models

Attacks directed at LLM models themselves can disrupt operations and compromise intellectual property, highlighting the importance of safeguarding these critical assets:

• LLM04: Model Denial of Service involves overwhelming LLM models with malicious requests to disrupt operations.
• LLM10: Model Theft underscores the risk of unauthorized access to LLM models or data.

Vulnerabilities in LLM implementations and integrations

The complex ecosystem surrounding LLMs introduces security risks related to supply chain vulnerabilities and insecure plugin designs:

• LLM05: Supply Chain Vulnerabilities warns against compromised dependencies that could lead to system breaches.
• LLM07: Insecure Plugin Design highlights the risks associated with vulnerable LLM plugins that may expose systems to attacks.

Mitigating risks in generative AI applications

Understanding the unique risks associated with LLM applications is crucial for ensuring secure deployment of generative AI technologies. While LLMs offer remarkable capabilities, their black box nature and inherent unpredictability require careful consideration and proactive security measures to mitigate potential threats.

The OWASP Top 10 for LLM applications serves as a guide to navigate the complexities of AI security and highlights the importance of approaching generative AI with caution and awareness.