OWASP LLM Top 10: Manjesh ModelTheft

## Vulnerability Name

LLM Model Theft

**Author(s):**

Manjesh S

**Description:**

The LLM model theft refers to the unauthorized access to the Language Model models (LLMs). This arises when the proprietary LLM models, which are valuable intellectual property, are compromised or stolen by malicious actors. The impact of LLM model theft can include economic losses, erosion of competitive advantage, unauthorized usage of the model, or unauthorized access to sensitive information contained within the model.

**Labels/Tags:**

- Model protection
- Model Theft
- TMT
- Trained Model Theft

**Common Examples of Vulnerability:**

1. Example 1: A malicious actor gains unauthorized access to a company's LLM model repository and downloads proprietary LLM models.
2. Example 2: An insider threat scenario where a rogue employee copies trained LLM models into external storage media.
3. Example 3: An attacker compromises the server with LLM model due to misconfiguration in their network or application security settings.

**How to Prevent:**

1. Prevention Step 1: Implement strong access controls and strong authentication mechanisms to limit unauthorized access to LLM model repositories and training environments.
2. Prevention Step 2: Restrict the LLM's access to network resources, internal services, and APIs.
3. Prevention Step 3: Regularly monitor and audit access logs and activities related to LLM model repositories to detect and respond to any suspicious or unauthorized behavior promptly.

**Example Attack Scenarios:**

Scenario #1: A skilled attacker exploits a vulnerability in a company's infrastructure to gain unauthorized access to their LLM model repository. The attacker proceeds to download valuable LLM models and uses them to launch a competing language processing service or extract sensitive information, causing significant financial harm to the original company.

Scenario #2: An attacker operates a shared GPU service, offering cheap hosting or access to GPU resources for running Language Model models (LLMs). In this scenario, unsuspecting users utilize the shared GPU service to execute their LLM models due to cost-effectiveness or limited hardware availability. The attacker easily gains unauthorized access to the users' LLM models and then copies them to their controlled server, thereby compromising the proprietary LLM models.

**Reference Links:**

1. [Meta’s powerful AI language model has leaked online](https://www.theverge.com/2023/3/8/23629362/meta-ai-language-model-llama-leak-online-misuse): A news article highlighting a real-world incident where an AI language model leaked online, emphasizing the importance of protecting LLM models from unauthorized access and misuse.

**Author Commentary (Optional):**

The theft of LLM models represents a significant security concern as language models become increasingly powerful and prevalent. Organizations and researchers must prioritize robust security measures to protect their LLM models, ensuring the confidentiality and integrity of their intellectual property. Employing a comprehensive security framework that includes access controls, encryption, and continuous monitoring is crucial in mitigating the risks associated with LLM model theft and safeguarding the interests of both individuals and organizations relying on LLM.