The Security Dimensions of Adopting LLMs
The incredible capabilities of LLM (Large Language Models) enable organizations to engage in various useful activities such as generating branding content, localizing content to transform customer experiences, precise demand forecasting, writing code, enhanced supplier management, spam detection, sentiment analysis, and much more.
As a result, LLMs are being leveraged across a multitude of industries and use cases.
On the flip side, they are also being leveraged by cybercriminals and hackers for malicious activities.
Types of LLMs in Business
There are two main categories of LLMs: open-source and proprietary.
Proprietary LLMs are developed and owned by businesses. To utilize them, individuals or organizations must purchase a license from the company, which outlines the permissible uses of the LLM, often restricting redistribution or modification.
Notable proprietary LLMs include PaLM by Google, GPT by OpenAI, and Megatron-Turing NLG by Microsoft and NVIDIA.
Open-source LLMs, in contrast, are communal resources freely available for use, modification, and distribution. This open nature fosters creativity and collaboration.
Notable examples of open-source LLMs include CodeGen by Salesforce and LLama 2 by Meta AI.
Excessive Dependence on LLMs
In a recent CISO panel discussion, security leaders discussed the dangers of relying too much on LLMs and stressed the importance of finding a responsible balance to minimize potential risks. So, what are the impact of mass LLM adoptions:
- Unprecedented speed in source code creation
- Emergence of more intelligent AI applications
- Increased adoption for apps thanks to the ease of instructing LLMs using plain language
- A significant surge in data from more nuanced activity in LLMs
- A substantial shift in how information is harnessed and applied in various contexts
4 Key Risks Associated with LLMs
Sensitive Data Exposure
Implementing LLMs like ChatGPT carries a notable risk of inadvertently revealing sensitive information. These models learn from user interactions, potentially including unintentionally disclosing confidential details.
ChatGPT’s default practice of saving users’ chat history for model training raises the possibility of data exposure to other users. Those relying on external model providers should inquire about the usage, storage, and training processes involving prompts and replies.
Major corporations like Samsung have reacted to privacy concerns by restricting ChatGPT usage to prevent leaks of sensitive business information. Industry leaders like Amazon, JP Morgan Chase, and Verizon also limit the use of AI tools to maintain corporate data security.
If the information used to train the model gets compromised or tainted, it can result in biased or manipulated outputs.
Using LLMs for malicious intent, such as evading security measures or capitalizing on vulnerabilities, is an additional example of potential risks.
OpenAI has defined specific usage policies to ensure that ChatGPT is not misused or used maliciously by attackers. There are several restrictions on what the chatbot can and cannot do.
For instance, if you ask ChatGPT to write an exploit for an RCE vulnerability in a CMD parameter, ChatGPT will deny the request. The chatbot will tell you that it is an AI language model that does not support or participate in unethical or illegal activities.
However, attackers can strategically insert keywords or phrases into prompts or conversations to bypass the OpenAI policies and obtain required responses.
Unauthorized Access to LLMs
The unauthorized access to LLMs represents a critical security concern, as it opens the door to potential misuse and poses various risks.
If these models are accessed illegitimately, there is a risk of extracting confidential data or insights, potentially leading to privacy breaches and unauthorized disclosure of sensitive information.
Much like DDoS attacks target network infrastructure, LLMs are a prime focus for threat actors due to their resource-intensive nature. When attacked, these models can experience service interruptions and increased operational costs. The persistent reliance on AI tools across diverse domains, from business operations to cybersecurity, intensifies the challenge.
Best Practices to Balance Risks When Working with LLMs
Input Validation for Enhanced Security
An integral step in the defence strategy involves the implementation of proper input validation. Organizations can significantly limit the risk of potential attacks by selectively restricting characters and words. For instance, blocking specific phrases can be a robust defense mechanism against unforeseen and undesirable behaviors.
API Rate Limits
To prevent overload and potential denial of service, organizations can manipulate the power of API rate controls. Platforms like ChatGPT exemplify this by restricting the number of API calls for free memberships, ensuring responsible usage, and protecting against attempts to replicate the model through spamming or model distillation.
Proactive Risk Management
Anticipating future challenges requires a multifaceted approach:
- Advanced Threat Detection Systems: Deploy cutting-edge systems that detect breaches and provide instant notifications.
- Regular Vulnerability Assessments: Conduct regular vulnerability assessments of the entire tech stack and vendor relationships to identify and rectify potential vulnerabilities.
- Community Engagement: Participate in industry forums and communities to stay abreast of emerging threats and share valuable insights with peers.