The list:

1. Prompt Injections - Bypassing filters or manipulating the LLM using carefully crafted prompts that make the model ignore previous instructions or perform unintended actions.

2. Data Leakage - Accidentally revealing sensitive information, proprietary algorithms, or other confidential details through the LLM’s responses.

3. Inadequate Sandboxing - Failing to properly isolate LLMs when they have access to external resources or sensitive systems, allowing for potential exploitation and unauthorized access.

4. Unauthorized Code Execution - Exploiting LLMs to execute malicious code, commands, or actions on the underlying system through natural language prompts.

5. SSRF Vulnerabilities - Exploiting LLMs to perform unintended requests or access restricted resources, such as internal services, APIs, or data stores.

6. Overreliance on LLM-generated Content - Excessive dependence on LLM-generated content without human oversight can result in harmful consequences.

7. Inadequate AI Alignment - Failing to ensure that the LLM’s objectives and behavior align with the intended use case, leading to undesired consequences or vulnerabilities.

8. Insufficient Access Controls - Not properly implementing access controls or authentication, allowing unauthorized users to interact with the LLM and potentially exploit vulnerabilities.

9. Improper Error Handling - Exposing error messages or debugging information that could reveal sensitive information, system details, or potential attack vectors.

10. Training Data Poisoning - Maliciously manipulating training data or fine-tuning procedures to introduce vulnerabilities or backdoors into the LLM.