Authors:
(1) Constantinos Patsakis, Department of Informatics, University of Piraeus, 80 Karaoli & Dimitriou str., 18534 Piraeus, Greece and Information Management Systems Institute of Athena Research Centre, Greece;
(2) Fran Casino, Information Management Systems Institute of Athena Research Centre, Greece and Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili;
(3) Nikolaos Lykousas, Data Centric, Romania. Authors: Authors: (1) Constantinos Patsakis, Department of Informatics, University of Piraeus, 80 Karaoli & Dimitriou str., 18534 Piraeus, Greece and Information Management Systems Institute of Athena Research Centre, Greece; (2) Fran Casino, Information Management Systems Institute of Athena Research Centre, Greece and Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili; (3) Nikolaos Lykousas, Data Centric, Romania. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related work 2.1 Malware analysis and countermeasures 2.1 Malware analysis and countermeasures 2.2 LLMs in cybersecurity 2.2 LLMs in cybersecurity 3 Problem setting 3 Problem setting 4 Setting up the experiment and the dataset 4 Setting up the experiment and the dataset 5 Experimental results and discussion 5 Experimental results and discussion 6 Integration with existing pipelines 6 Integration with existing pipelines 7 Conclusions, Acknowledgements, and References 7 Conclusions, Acknowledgements, and References 6 Integration with existing pipelines As discussed, while LLMs are not mature enough to fully replace traditional deobfuscators, they can efficiently compliment them whenever they fail. This is very often during malware campaigns where threat actors may push changes on their droppers and payloads. As such, important information for, e.g., takedowns or isolation of the threat can be missed. In the case of Emotet that we use for reference, some of the compromised domains that were used to download Emotet’s executable were missed after changes in the dropper and the deobfuscator required manual patches. To address such issues, one could consider a pipeline as the one illustrated in Figure 7. More precisely, we consider that the input is a malicious MS Office document which, depending on the analysis environment, is either sent to a sandbox or an emulator. Either of them opens the file and logs the obfuscated malicious payload through its usual mechanisms. The payload is then sent to the traditional deobfuscators and to an LLM. Each of them tries to extract the configuration, e.g., dropper URLs, C2 servers, but the LLM tries to provide a summary of the code and from that derive the MITRE ATT&CK methods[6]. This way, the LLM can fill in the gaps of the deobfuscator in case it fails, but also provide a brief analysis of what the malicious script does and an easy to digest output for further correlations (MITRE ATT&CK methods). In our experiments, the prompt in Figure 8 managed to do the job quite efficiently in OpenAI’s GPT-4. For instance, the script of Figure 2 returns the JSON of Figure 9 which is quite accurate. 7 Conclusions According to our outcomes, LLMs can effectively automate a substantial portion of the deobfuscation process. The latter implies that, even though the advent of LLMs is still in its infancy, they exhibit a remarkable potential to improve malware analysis and to be integrated into real-world pipelines, as we discuss in the following paragraphs. First, cutting-edge LLMs do not simply generate code or superficially understand its context. Our extensive results clearly show their ability to process it, identify the relevant parts, and operate on them. Notably, this capability is showcased in code that is deliberately written in a form to prevent this from happening. While this is very relevant for LLMs provided as cloud-based services, the same does not apply to local LLMs. Indeed, the disparity among the two flavours of LLMs is so grave that they could be considered inefficient for this task. Yet, despite having fewer parameters than proprietary models, local LLMs can be fine-tuned to optimise their performance in specific tasks as their weights are made publicly available, which we will explore in future work. The latter includes exploring smaller LLMs to provide resource-efficient solutions related to code deobfuscation and analysis, fostering the adoption of LLMs in constrained environments. The above implies that we do not foresee that LLMs will replace traditional unpackers but operate in existing pipelines to enrich traditional malware analysis and threat intelligence platforms. To this end, in deobfuscating malicious code, the pressing needs can be summarised in three key areas: minimising the hallucinations, expanding the input for queries, and enhancing training methodologies. More concretely, our experiments have uncovered that even in the case of the best-performing LLM, there are numerous hallucinated domains. This raises a significant concern as such processes are used to automatically create rules, making the risk of raising false flags very high. Since the hallucinated domains are most likely to originate from the training dataset due to, e.g., high representation and reputation, they would be benign. Thus, the automatically generated rules for the hallucinated domains may not only permit malicious traffic but inadvertently block legitimate ones. Furthermore, we should consider that if the LLMs hallucinate domains, they may also hallucinate functionality once they do not understand some code snippet, leading to false claims and possibly false attribution. Moreover, the occasional misinterpretation of the prompts due to the alignment of some LLMs can also impede such tasks in automated pipelines. Finally, hallucinations are a way for LLMs to fill in the gaps in their responses. Therefore, in the context of this task, and possibly for other cybersecurity tasks, LLMs should provide different responses to hallucinations. For instance, responding that the task cannot be performed would be preferable to returning a wrong result. While the scripts in our current dataset are relatively short, malicious code, in general, is significantly longer and would not fit within a single prompt. Hence, expanding the input size becomes an absolute necessity. However, the length of the code is not the sole challenge. Since this code is purposefully obfuscated to evade analysis, even by humans, training LLMs with adequately labelled and properly annotated malicious code is crucial [18]. By fine-tuning LLMs, we anticipate a substantial improvement in the models’ accuracy, as well as their ability to handle more complex tasks, especially in understanding malicious code and artefacts. To achieve this, we plan to use sets of obfuscated and gradually deobfuscated code to train LLMs on how to deobfuscate code. We prioritise doing this for VBA, PHP, Python, and Javascript since malware analysts often find such obfuscated codes in malicious documents and webshells. Other languages would most likely use executables, which cannot be directly and accurately reversed to code. In this regard, and as stated in Section 2, there is a need for greater transparency and ethical considerations in AI development, which are, in turn, necessary to comply with regulations such as the EU Artificial Intelligence Act. One of the main concerns is the unclear origins of training data in most models and the opacity surrounding the refinement of these models, a step that usually requires human interaction. The latter is particularly relevant for evaluating the capabilities of LLMs since biased outcomes and overfitting can only be avoided if a sound methodology is used to define the training and testing procedures, highlighting the relevance of the corresponding datasets. Acknowledgements This work was supported by the European Commission under the Horizon Europe Programme as part of the projects LAZARUS (https://lazarus-he.eu/) (Grant Agreement no. 101070303), CyberSecPro (https://www.cybersecpro-project.eu/) (Grant Agreement no 101083594), and CYMEDSEC. This research is supported by supported by Ministerio de Ciencia, Innovaci´on y Universidades, Gobierno de Espa˜na (Agencia Estatal de Investigaci´on, Fondo Europeo de Desarrollo Regional -FEDER-, European Union) under the research grant PID2021-127409OB-C33 CONDOR. Fran Casino was supported by the Government of Catalonia with the Beatriu de Pin´os programme (Grant No. 2020 BP 00035), and by AGAUR with the project ASCLEPIUS (2021-SGR-00111). The content of this article does not reflect the official opinion of the European Union. Responsibility for the information and views expressed therein lies entirely with the authors. References [1] Ehab Alkhateeb, Ali Ghorbani, and Arash Habibi Lashkari. A survey on run-time packers and mitigation techniques. International Journal of Information Security, 23(2):887–913, 2024. [2] Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, and Percy Liang. 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Best Practices for Integrating LLMs with Malware Analysis Tools

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