insight - ComputerSecurityandPrivacy - # LLM-Assisted Malware Analysis

Feasibility of Large Language Models for Supporting Static Malware Analysis: A Demonstration Experiment

Q: How can the confidentiality concerns related to sharing potentially sensitive information with external LLMs be effectively addressed to enable broader adoption in real-world security settings?

Confidentiality concerns regarding sensitive information sharing with external LLMs are a major roadblock to broader adoption in security settings. Here are some strategies to address this: On-Premise LLMs: Transitioning from external, cloud-based LLMs to on-premise deployments allows organizations to maintain complete control over their data. This eliminates the risk of data exposure during transit or storage with third-party providers. Federated Learning: This approach trains LLM models across multiple decentralized devices or servers holding local data, without exchanging the data itself. This allows for collaborative model improvement while preserving data privacy. Differential Privacy: This technique injects noise into the training data while ensuring the LLM learns general patterns without memorizing specific, potentially sensitive details. Homomorphic Encryption: This advanced cryptographic method allows computations on encrypted data without decryption. Applying this to LLMs is an active research area, but it holds the promise of analyzing malware without exposing sensitive code snippets. Data Sanitization: Before feeding code to an LLM, organizations can implement rigorous data sanitization techniques. This includes removing or replacing sensitive identifiers like API keys, hardcoded credentials, or internal domain names. Legal and Contractual Agreements: Clear contractual agreements with LLM providers that explicitly address data ownership, usage limitations, and security protocols are essential. In addition to these technical solutions, fostering a culture of security awareness among analysts is crucial. This includes training on data handling best practices and emphasizing the importance of minimizing sensitive data exposure.

Q: Could the integration of LLMs with dynamic analysis techniques further enhance malware analysis by providing a more comprehensive understanding of malware behavior?

Yes, integrating LLMs with dynamic analysis techniques holds significant potential for enhancing malware analysis and providing a more comprehensive understanding of malware behavior. Here's how: Correlating Static and Dynamic Findings: LLMs can bridge the gap between static and dynamic analysis. For instance, an LLM could analyze static code to identify potentially malicious functions and then guide the dynamic analysis environment to focus on those specific behaviors. This targeted approach can uncover evasive malware that only exhibits malicious behavior under certain conditions. Behavioral Pattern Recognition: LLMs can be trained on vast datasets of malware behavior logs obtained from dynamic analysis sandboxes. This enables them to identify complex behavioral patterns indicative of malicious intent, even in previously unseen malware samples. Real-Time Threat Intelligence: LLMs can continuously ingest and analyze threat intelligence feeds, correlating this information with dynamic analysis results. This allows for real-time identification of known malware families, attack campaigns, and indicators of compromise. Automated Report Generation: LLMs can automatically generate comprehensive malware analysis reports that combine insights from both static and dynamic analysis. This can free up human analysts to focus on more complex tasks, such as threat hunting and incident response. By combining the strengths of LLMs in natural language processing, code understanding, and pattern recognition with the insights gained from dynamic analysis, we can create a more robust and efficient malware analysis pipeline.

Core Concepts

Large language models (LLMs) show promise in supporting static malware analysis by generating accurate and informative explanations of malware functionality, potentially reducing the workload and expertise required for this task, although confidentiality concerns and integration challenges need to be addressed.

Abstract

Bibliographic Information:

Fujii, S., & Yamagishi, R. (2024). Feasibility Study for Supporting Static Malware Analysis Using LLM. In Workshop on Security and Artificial Intelligence (SECAI 2024).

Research Objective:

This research investigates the feasibility of utilizing large language models (LLMs), specifically ChatGPT (GPT-4), to assist security analysts in conducting static malware analysis. The study aims to determine if LLMs can generate accurate and helpful explanations of malware functionality, thereby potentially improving the efficiency and effectiveness of static analysis.

Methodology:

The researchers selected a ransomware sample (Babuk) with a publicly available analysis article for evaluation. They decompiled and disassembled the malware using Ghidra, inputting the results into ChatGPT with various prompts to generate explanatory text for each function. The accuracy of the generated explanations was assessed based on function coverage, BLEU, and ROUGE scores, comparing them to the analysis article. Additionally, a user study involving six security analysts was conducted. The analysts were tasked with performing a simulated static analysis of the malware using ChatGPT-generated explanations alongside decompiled/disassembled results. Their feedback was collected through questionnaires and interviews to evaluate the practicality and usefulness of LLM assistance in a real-world setting.

Key Findings:

LLMs can generate explanations that cover malware functions with high accuracy (up to 90.9%) when provided with decompiled code.
The wording and instructions within the prompts significantly influence the accuracy and usefulness of the generated explanations. Prompts instructing the LLM to act as a malware analyst and focus on suspicious parts yielded the best results.
User study results indicate that LLM-generated explanations are perceived as fluent, relevant, and informative by security analysts.
While not a complete replacement for traditional analysis methods, LLM explanations show potential for practical use as a supplementary tool, potentially reducing analysis time and complexity.

Main Conclusions:

This study demonstrates the potential of LLMs as valuable assistants in static malware analysis. The findings suggest that LLMs can effectively generate accurate and helpful explanations of malware functionality, contributing to a more efficient analysis process. However, challenges such as confidentiality concerns regarding sensitive information and the need for seamless integration with existing analysis tools need to be addressed for wider practical adoption.

Significance:

This research contributes to the growing body of work exploring the applications of LLMs in cybersecurity. It provides valuable insights into the potential benefits and challenges of using LLMs for static malware analysis, paving the way for the development of more sophisticated and user-friendly LLM-based security tools.

Limitations and Future Research:

The study was limited to a single malware sample and a small number of participants. Future research should involve a wider range of malware families and a larger, more diverse group of analysts to validate the generalizability of the findings. Further investigation is needed to address the identified challenges, such as developing methods for handling sensitive information and improving the integration of LLM outputs with existing analysis workflows.

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Stats

The LLM achieved an accuracy of up to 90.9% in explaining malware functions.
6 security analysts from 4 different organizations participated in the user study.
The user study involved analyzing a ransomware sample with 107 functions, 62 of which were malware-specific.

Quotes

"The explanations forcefully linked unrelated items to malware behavior, which was confusing."
"It was necessary to check the results of decompile and disassemble together, and although the LLM output is useful, it was difficult to rely on it alone to analyze the results."
"There is a possibility that information hard-coded in the malware (organization name, DNS, etc. of the target organization, and authentication information) could be leaked".

Key Insights Distilled From

Feasibility Study for Supporting Static Malware Analysis Using LLM

by Shota Fujii,... at arxiv.org 11-25-2024

https://arxiv.org/pdf/2411.14905.pdf

Feasibility Study for Supporting Static Malware Analysis Using LLM

Deeper Inquiries

How can the confidentiality concerns related to sharing potentially sensitive information with external LLMs be effectively addressed to enable broader adoption in real-world security settings?

Confidentiality concerns regarding sensitive information sharing with external LLMs are a major roadblock to broader adoption in security settings. Here are some strategies to address this:

On-Premise LLMs: Transitioning from external, cloud-based LLMs to on-premise deployments allows organizations to maintain complete control over their data. This eliminates the risk of data exposure during transit or storage with third-party providers.
Federated Learning: This approach trains LLM models across multiple decentralized devices or servers holding local data, without exchanging the data itself. This allows for collaborative model improvement while preserving data privacy.
Differential Privacy:  This technique injects noise into the training data while ensuring the LLM learns general patterns without memorizing specific, potentially sensitive details.
Homomorphic Encryption: This advanced cryptographic method allows computations on encrypted data without decryption. Applying this to LLMs is an active research area, but it holds the promise of analyzing malware without exposing sensitive code snippets.
Data Sanitization: Before feeding code to an LLM, organizations can implement rigorous data sanitization techniques. This includes removing or replacing sensitive identifiers like API keys, hardcoded credentials, or internal domain names.
Legal and Contractual Agreements:  Clear contractual agreements with LLM providers that explicitly address data ownership, usage limitations, and security protocols are essential.
In addition to these technical solutions, fostering a culture of security awareness among analysts is crucial. This includes training on data handling best practices and emphasizing the importance of minimizing sensitive data exposure.

Could the integration of LLMs with dynamic analysis techniques further enhance malware analysis by providing a more comprehensive understanding of malware behavior?

Yes, integrating LLMs with dynamic analysis techniques holds significant potential for enhancing malware analysis and providing a more comprehensive understanding of malware behavior. Here's how:

Correlating Static and Dynamic Findings: LLMs can bridge the gap between static and dynamic analysis. For instance, an LLM could analyze static code to identify potentially malicious functions and then guide the dynamic analysis environment to focus on those specific behaviors. This targeted approach can uncover evasive malware that only exhibits malicious behavior under certain conditions.
Behavioral Pattern Recognition: LLMs can be trained on vast datasets of malware behavior logs obtained from dynamic analysis sandboxes. This enables them to identify complex behavioral patterns indicative of malicious intent, even in previously unseen malware samples.
Real-Time Threat Intelligence: LLMs can continuously ingest and analyze threat intelligence feeds, correlating this information with dynamic analysis results. This allows for real-time identification of known malware families, attack campaigns, and indicators of compromise.
Automated Report Generation: LLMs can automatically generate comprehensive malware analysis reports that combine insights from both static and dynamic analysis. This can free up human analysts to focus on more complex tasks, such as threat hunting and incident response.
By combining the strengths of LLMs in natural language processing, code understanding, and pattern recognition with the insights gained from dynamic analysis, we can create a more robust and efficient malware analysis pipeline.

What are the ethical implications of using LLMs in cybersecurity, particularly concerning potential biases in training data and the potential for misuse by malicious actors?

The use of LLMs in cybersecurity, while promising, raises significant ethical considerations:

Bias in Training Data: LLMs are trained on massive datasets, which may contain biases reflecting existing societal prejudices or imbalances in representation. If not addressed, these biases can be amplified in the LLM's output, leading to unfair or discriminatory outcomes. For example, an LLM trained on a dataset overrepresenting a specific type of malware might misclassify or overemphasize the threat from that category.

Malicious Use by Adversaries:  The same capabilities that make LLMs valuable for cybersecurity professionals can be exploited by malicious actors. For instance, attackers could use LLMs to:

Generate more sophisticated and evasive malware: LLMs can be used to automate the process of code obfuscation, polymorphism, and other techniques that make malware harder to detect and analyze.
Craft highly convincing phishing attacks: LLMs can generate grammatically flawless and contextually relevant phishing emails, increasing the likelihood of successful social engineering attacks.
Develop automated vulnerability discovery tools: LLMs can be used to analyze code for potential security weaknesses, potentially leading to the discovery of zero-day vulnerabilities.

Over-Reliance and Automation Bias:  Over-reliance on LLM-driven security tools without proper human oversight can lead to automation bias, where analysts may blindly trust the LLM's output without critical evaluation. This can result in missed threats or false positives.
To mitigate these ethical concerns, it's crucial to:

Develop and deploy LLMs with fairness and bias mitigation techniques: This includes carefully curating training datasets, implementing bias detection and mitigation algorithms, and continuously monitoring and evaluating LLM outputs for potential biases.
Establish clear ethical guidelines and regulations for LLM use in cybersecurity: This includes defining acceptable use cases, establishing accountability mechanisms, and promoting responsible development and deployment practices.
Invest in research and development of countermeasures against malicious LLM use: This includes developing techniques to detect and mitigate LLM-generated malware, phishing attacks, and other threats.
By proactively addressing these ethical implications, we can harness the power of LLMs for good while mitigating the risks associated with their misuse.