## 2 Background and Literature Review
### Conclusions on Related Work and Research Gap Identification  
Static signature-based scanning (without the usage of LLMs), as elaborated in Section 2.1, represents a rapid and reliable approach for identifying known samples. However, it is also evident from the preceding section that this method is not entirely effective in detecting a significant number of malware threats. The static non-LLM-based method either blocks the file completely or allows it to run, which typically requires user consent, such as for the use of macros.  
The utilisation of machine learning algorithms has demonstrated the capacity to reliably identify malicious files of diverse typologies. It has also been demonstrated that large language models are capable of differentiating between malicious and benign indicators, thereby enabling them to make classifications in this context. However, these were either modified to enhance their ability to predict malware or deployed to evaluate collected logs and contextualise them.  
It has yet to be demonstrated that a potentially malicious file can be extracted and assessed by a general-purpose LLM, using the extracted data. As fine-tuned LLMs may achieve superior testing results on classified samples, they are susceptible to the same issues as the presented machine learning approaches. Specifically, they are vulnerable to the misselection of features or, respectively, the fine-tuning mechanisms that can be circumvented by future malware engineers, who will seek to navigate around those features. A general-purpose large language model may prove a superior means of detecting such malware, given its extensive knowledge base and the inherent difficulty of reverse-engineering the features upon which the LLM is classifying the samples.  
Thus, the aim of the paper is to provide a system for the assessment of OOXML office files, specifically the detection of malicious parts in them, using a general purpose LLM. We use Claude 3.5 Sonnet by Anthropic, as it gained the highest score in the Graduate-Level Google-Proof Q&A Benchmark (see Figure 10).  
In order to compare the generated output of a LLM-based detection approach to other currently employed malware scanners such as Microsoft Defender or Bitdefender, we not only let the language model generate a summary of its potentially malicious findings, but also a numerical assessment ranging from 0.0 to 10.0. With this numerical value, we can define a threshold, also called a decision boundary, for which to declare an Office document as malicious or not. The intuitive approach would be to simply define 5.0 as this threshold; however, empirical tests have shown, this is not the most fitting value. Details can be found in Section 4. Now that we have a final true/false rating, we can overview the efficiency by making use of common statistical measures, such as the Accuracy, Precision, Recall and F1-score and assess the quality of our proposed detection approach. The results can be found in Section 5.  
Now that we know how maldocs are currently assessed and what limitations this kind of assessment has, we can take a look into how this paper's approach performs the feature extraction, namely, how to extract all the available information from an Office document. For this, we show how to get to malware samples and how the script Office2 JSON extracts this information.