Conceitos Básicos
Large language models (LLMs) offer transformative potential in enhancing various cybersecurity practices, from threat
intelligence to vulnerability detection and secure code generation.
Resumo
This paper provides a comprehensive overview of the state-of-the-art research on applying large language models (LLMs) in the field of cybersecurity. It addresses three key research questions:
How to construct cybersecurity-oriented domain LLMs?
Key technologies like continual pre-training (CPT) and supervised fine-tuning (SFT) can be used to adapt general LLMs to the cybersecurity domain.
Evaluation of LLM cybersecurity capabilities, such as cybersecurity knowledge, secure code generation, and IT operations, can guide the selection of appropriate base models.
Existing works have fine-tuned LLMs like Llama, CodeLlama, and GPT for tasks like vulnerability detection, secure code generation, program repair, and IT operations.
What are the potential applications of LLMs in cybersecurity?
Threat intelligence: LLMs can assist in generating, analyzing, and summarizing cyber threat intelligence from unstructured data.
Fuzzing: LLMs can generate high-quality test cases for API, deep learning libraries, protocols, and embedded systems, improving the efficiency of traditional fuzzing techniques.
Vulnerability detection: LLMs show promise in identifying software vulnerabilities, with strategies like code sequence embedding, in-context learning, and multi-role code review.
Secure code generation: LLMs can be leveraged to generate code that adheres to security best practices and minimizes vulnerabilities.
Program repair: LLMs can automate the process of fixing software bugs and vulnerabilities.
Anomaly detection: LLMs can be applied to detect security anomalies in logs, web content, and digital forensics.
LLM-assisted attacks: Researchers have also explored the potential misuse of LLMs in launching various cyber attacks.
What are the existing challenges and further research directions about the application of LLMs in cybersecurity?
LLM vulnerabilities and susceptibility to attacks, such as LLM-oriented attacks and jailbreaking, pose significant challenges.
Improving the robustness, reliability, and interpretability of LLMs in cybersecurity applications is crucial.
Developing specialized datasets and benchmarks for evaluating LLM cybersecurity capabilities is an important research direction.
Exploring the integration of LLMs with traditional cybersecurity tools and techniques can lead to synergistic advancements.
Overall, this review highlights the extensive potential of LLMs in enhancing cybersecurity practices and serves as a valuable resource for researchers and practitioners in this domain.
Estatísticas
LLMs have demonstrated promising results in detecting software vulnerabilities, with precision rates up to 50% in real-world scenarios.
LLMs can automatically produce a significant number of effective fuzz drivers with less manual intervention, outperforming traditional program analysis methods.
LLMs can achieve a vulnerability detection precision rate of up to 96% on Java functions when fine-tuned with appropriate datasets.
LLMs can identify 13 previously unknown Use-Before-Initialization (UBI) bugs in the Linux kernel through the LLift framework.
Citações
"LLMs hold good promise for the issue of vulnerability detection, though the false positive rate is still high and performance degrades on more challenging real-world datasets."
"By carefully designed prompt, desirable results can be obtained on synthetic datasets, but performance degrades on more challenging real-world datasets."
"LLMs can be effectively utilized to enhance the efficiency and quality of code reviews, particularly in detecting security issues within software code."