How to Test the OWASP Top 10 Critical Vulnerabilities for LLMs
While Large Language Model (LLM) applications are driving significant advancements in fields from personal creativity to business productivity, they pose new risks and vulnerabilities that need to be properly tested and handled. The OWASP Top 10 for LLMs list identifies the most critical vulnerabilities in LLM applications. The list was proposed by OWASP, an open community of security experts that has become a global authority in software security.
Traditional software testing methods like unit testing are ineffective for LLMs, because LLMs are probabilistic systems. Comprehensive and statistically significant testing is essential to identify and address vulnerabilities in LLM applications before deployment. However, generating such test data is challenging, as it requires simulating thousands of interactions between users and the LLM application. These simulations must encompass typical use cases, rare edge scenarios, and even adversarial attacks.
Our Maihem platform conducts comprehensive testing for all vulnerabilities outlined in the OWASP Top 10 List for LLMs, and provides actionable guidance on how to address any detected issues in your LLM applications.
Below is an overview of each vulnerability in the OWASP Top 10, along with example for a testing method.
LLM01: Prompt Injection
Prompt injection attacks can manipulate an LLM to produce harmful or unintended outputs. For instance, an attacker might craft a prompt disguised as a poem to trick the model into revealing sensitive information. Maihem’s automated red teaming solution generates a diverse set of prompt injections, identifies vulnerabilities, and offers actionable guidance on how to address them effectively.
LLM02: Insecure Output Handling
Invalid LLM outputs can result in downstream security exploits, such as unauthorized code execution that compromises systems and exposes sensitive data. Maihem’s automated red teaming module generates a diverse set of prompts designed to trigger invalid or insecure responses, including those intended to execute harmful or unauthorized code through the chatbot.
LLM03: Training Data Poisoning
LLMs can produce incorrect or harmful outputs if they have been trained (or fine-tuned) on poisoned data. Even OpenAI’s models have been exposed to such risks, as they are trained on vast, unvetted datasets from sources like Reddit. Maihem thoroughly assesses the quality of LLM responses by detecting biases, harmful content, and other potential risks. Based on the results, Maihem provides an actionable plan to mitigate data poisoning issues.
LLM04: Model Denial of Service
Attackers can overload LLMs by triggering resource-intensive operations, leading to service outages and increased costs. Maihem simulates denial-of-service (DoS) attacks to assess the vulnerability of LLM applications and provides guidance on how to defend against them.
LLM05: Supply Chain Vulnerabilities
Insecure input prompts or improper output handling can create supply chain vulnerabilities, such as leaking or deleting sensitive data (e.g., removing a shipment from a database). Maihem’s automated red teaming generates a diverse set of prompts to ensure that LLM application inputs and outputs are secure and free of vulnerabilities.
LLM06: Sensitive Information Disclosure
LLM applications can unintentionally disclose sensitive information or fall victim to prompt injection attacks, resulting in serious consequences. Key examples include personally identifiable information (PII) and role-based access controls. Maihem’s automated red teaming uses targeted prompts to identify potential information leaks, detecting vulnerabilities before they affect customers and ensuring robust protection against data disclosure risks.
LLM07: Insecure Plugin Design
LLM plugins with insufficient access controls are vulnerable to processing untrusted inputs, potentially leading to severe exploits such as remote code execution. Maihem’s automated red teaming simulates attacks targeting insecure plugins and offers guidance on how to make LLM applications secure.
LLM08: Excessive Agency
LLM applications with unchecked autonomy may present excessive agency, which presents the risk of overstepping safety boundaries. For example, a chatbot might give a medical diagnosis when it is not supposed to. Through automated red teaming, Maihem simulates various user interactions and evaluates the responses of LLMs to detect unauthorized actions like deleting invoices or giving illegal financial advice.
LLM09: Overreliance on Model Outputs
LLM applications with unchecked autonomy can overstep safety boundaries, posing risks of taking actions beyond their intended scope. For example, a chatbot might provide a medical diagnosis when it shouldn’t. Maihem’s automated red teaming simulates diverse user interactions and evaluates LLM responses to detect unauthorized behaviors, such as deleting invoices or offering illegal financial advice.
LLM10: Model Theft
Hackers can use jailbreak attacks to extract and steal an LLM’s instruction prompt, creating a security risk since these prompts often contain sensitive information. Maihem’s automated red teaming solution uses state-of-the-art techniques to test whether an LLM is vulnerable to such leaks and detects if its instruction prompt has been compromised.
Conclusion
Maihem’s platform automatically tests the OWASP Top 10 vulnerabilities for LLMs using its advanced red teaming solution, simulating various types of attacks and failures to evaluate the resilience of your LLM applications. With Maihem’s automated red teaming and quality testing tools, you can proactively mitigate security risks and protect your LLM-powered applications from potential exploits. Reach out to learn how Maihem can help you on your AI journey.
