Crafty inputs can trick LLMs into unintended behaviors. This includes overwriting system prompts or manipulating external source inputs.
When LLM outputs are blindly trusted, it risks system exposure with potential threats like XSS, CSRF, and privilege escalation.
LLMs are at risk if their training data is altered, which might introduce security risks, biases, or affect their effectiveness.
LLMs are susceptible to attacks that strain resources, amplified by their resource-heavy nature and unpredictable user inputs.
LLM systems can be undermined by vulnerabilities from third-party datasets, plugins, or pre-trained models.
LLMs might unintentionally disclose sensitive information such as PII.
Insecure plugin designs, especially with poor input and access controls, can be easily exploited.
Excessive functionalities or permissions given to LLMs might lead to unexpected outcomes.
Over-relying on LLMs without proper checks may cause misinformation, legal challenges, and other vulnerabilities.
Unauthorized access to LLM models can result in financial losses, competitive disadvantages, and exposure of confidential info.
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