Large Language Models (LLMs) are increasingly used in businesses, especially in customer service, content classification, moderation, and text summarization. Given that LLMs often handle sensitive company or customer data, they must adhere to high-security standards. Many companies aim to integrate LLMs into their applications, allowing users to interact with them in natural language. This approach is often more user-friendly compared to traditional interfaces that rely on menus, checkboxes, and toolbars.
LLMs offer a unique, non-deterministic experience compared to conventional software, which is typically predictable. The OWASP Foundation has identified significant security risks associated with software, and with the rise of LLMs, they’ve quickly released a list of risks specific to AI language models. This list has been expanded with a document titled “LLM Cybersecurity and Governance Checklist,” aimed at managers and executives.
Many businesses wish to incorporate LLMs into their applications. Security officers and administrators must adjust their security measures accordingly. All models have vulnerabilities, such as hallucinations and biases, which are inherent issues that will occur regardless. To use language models securely, it is essential to have a clear, specific purpose that cannot be achieved with traditional software.
During operation, businesses must sanitize inputs to language models, monitor outputs, and strictly limit access to external sources and tools. This article will address the dangers from the OWASP top-10 list, aiming to provide an understanding of the risks and protective measures crucial for the safe use of LLMs in a business context. This includes vulnerabilities like prompt injections, risks from unchecked LLM outputs, and preventing the disclosure of sensitive information. Additionally, intrinsic phenomena such as hallucinations, biases, and sycophancy, along with their impacts, are described.
Prompt injections and jailbreaking are methods attackers use to manipulate LLMs by feeding them specific inputs that can cause the model to behave unexpectedly or divulge private data. Multi-shot attacks involve providing the model with several examples to influence its behavior subtly. Avoiding hallucinations and prompt injections is crucial, as these can lead to the model generating incorrect or misleading information.
Another significant concern is the potential for LLMs to inadvertently reveal sensitive information. As these models are trained on vast datasets, there is a risk they might output confidential data if not properly managed. Additionally, the inherent biases present in LLMs need to be addressed, as these can affect the fairness and accuracy of the model’s outputs.
Reward manipulation is another issue, where models might be trained to optimize for specific outcomes, potentially leading to unintended behaviors. To mitigate these risks, companies must implement strict controls and continuously monitor the performance and outputs of their LLMs.
In conclusion, while LLMs offer significant advantages in terms of user interaction and application functionality, they also present unique security challenges. Companies must be vigilant in managing these risks to ensure the safe and effective use of LLMs in their operations.