How Do LLMs Work?
To understand LLM cyber security, it’s first important to understand their core mechanisms. At the most basic level, an LLM is a neural network that transforms text – like training manuals and books – into tokens, and then establishes links between each individual word and letter. These tokens are placed into vector format – essentially a multi-dimensional graph, where similar words get plotted nearby one another.
By analyzing many different texts, the LLM’s encoder essentially forms a map of how words are used. The decoder then re-traces this map whenever the model needs to generate its own text in response to a prompt. Text is generated step-by-step, with each word choice considering the preceding ones.
The final core component of an LLM is its self-attention mechanisms. This is how a model cross-references the intended importance of different words in an input. For instance, when interpreting the sentence “The animal didn’t cross the street because it was too tired,” the self-attention mechanism helps the model understand that “it” refers to “the animal,” ensuring accurate comprehension and generation. Self-attention mechanisms can be stacked, in order to ensure the LLM understands each nuance of an input prompt.
Collectively, this group of processes allows for a user to interact with an LLM, and have it generate text on-command. While impressive, it’s vital to keep security in mind throughout an LLM’s development and ongoing use.
Data Security for LLMs
The first component of LLM security is the datasets that the model is trained on.
Personal Data
LLMs are only as good as the information they provide access to. As a result, most corporate use cases add an extra layer of training, where the LLM communicates HR, sales, or email information upon request. This means that the data this LLM accesses and communicates about needs to be kept safe.
LLM data security begins at the training stage: data anonymization helps prevent the finished LLM from regurgitating PII, either accidentally or in response to prompt injection attacks. Older anonymization would rely on suppression, which effectively just removes all PPI from the dataset before a model is trained. This isn’t the best solution, as it risks reducing an LLM’s utility once it’s finished. More advanced approaches protect individual PPI by adding randomness and noise to the data itself. Its main goal is to make it challenging to identify whether a particular individual’s information is part of a dataset, whille retaining the entire dataset’s functionality at the aggregate level.
Training Data
The core data that powers an LLM’s real-world use cannot be overlooked: these datasets represent a large attack surface if not adequately verified ahead of time. The resultant risk is of attackers deliberately injecting malicious or misleading information into a training set. In the case of this being leveraged against a sentiment analysis or customer-facing LLM, this risks severe damage to a brand’s reputation.
User-Level LLM Security
With backend data secured, the second component of LLM security is its user-facing operations.
User Prompts
Prompts are the main way that users interact with an LLM: it’s the public face of the tool, and therefore represents the largest attack surface. Attacks like prompt injection aim to take advantage of this surface by forcing an LLM to regurgitate harmful or private information. Even worse, because of the unpredictable nature of LLM responses, security and tech teams are often unable to anticipate specific responses to prompts.
Resource Management
It’s public knowledge that LLMs and other neural networks use a great deal of electricity and, in turn, compute resources. An organization with an LLM deployed on their own cloud or physical appliance needs to be keenly aware of just how much is being used – failure to do so could lead to user inputs pulling abnormal amounts of resources. This can be due to simple errors, or malicious intent. The latter describes the deliberate use of resource-heavy queries against an LLM, in order to deliberately degrade other users’ service, or to incur high costs on the side of the operator.
User Visibility
Almost 90% of LLM usage is invisible to IT and security teams. And given that roughly 15% of employees use public LLMs on a daily basis, many organizations are unaware of the information being shared with third-party LLM providers. The use cases of highest concern include employees copying and pasting corporate data into part of their prompts.
The concerns around user visibility is twofold: for one, it represents a significant gap in the current security deployments. Traditional data loss prevention tools were designed for an era of fixed keywords, making them far less effective at detecting the unstructured, conversational format of GenAI prompts.
The other concern is what happens when that data is given to third-party GenAI providers. Since publicly available tools are constantly using data to refine and train models, users risk unintentionally exposing sensitive or copyrighted information to third-party LLM models.
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