What are Large Language Models and How Are They Used in Cyber Security?

LLMs are AI systems that are designed to process and analyze vast amounts of natural language data and then use that information to generate responses to user prompts. These systems are trained on massive data sets using advanced machine learning algorithms to learn the patterns and structures of human language.

In the world of cybersecurity solutions, this is very advantageous as LLMs can sort through massive amounts of data and threat intelligence to find attacker techniques, patterns, and behaviors more quickly and accurately. LLMs are becoming increasingly important in a variety of applications such as natural language processing, machine translations, code and pattern, and behavior analysis.

What Are Language Models and How do They Work?

Large language models are advanced artificial intelligence systems that take some input and generate humanlike text as a response. They work by first analyzing vast amounts of data and creating an internal structure that models the natural language data sets that they’re trained on. Once this internal structure has been developed, the models can then take input in the form of natural language and approximate a good response. Over time as more data is accumulated, and data sets are trained on this increasing data, the ability to discover and examine potential vulnerabilities, for example, becomes faster and more accurate.

Why are LLMs Such a Big Deal?

A few recent advancements have really brought the spotlight to generative AI and large language models:

• Technique Advancement: In recent years, there have been substantial improvements in the techniques used to train these models, leading to significant performance enhancements. A particularly notable improvement has been the incorporation of human feedback directly into the training process, resulting in one of the most significant leaps in performance.
• Expanded Access: The release of ChatGPT made it possible for anyone with internet access to interact with one of the most advanced LLMs through a simple web interface. This highlighted the impressive advancements of LLMs, which were previously accessible only to researchers with significant resources and deep technical expertise.
• Expanded Processing Capabilities: The availability of more powerful computing resources, such as graphics processing units (GPUs), along with improved data processing techniques, enables researchers to train much larger models, enhancing the performance of these language models.
• Enhanced Training Datasets: As our ability to collect and analyze large volumes of data improves, model performance has seen dramatic enhancements. BreachLock’s proprietary AI engine, which utilizes supervised NLP-based AI models, has demonstrated outstanding results by training our model with high-quality datasets derived from thousands of penetration tests, scans, and attack surface discovery scans, vulnerability classifications, and more. This threat intelligence, combined with our AI engine, has enabled more accurate identification of vulnerabilities that might otherwise have been overlooked by both automated and manual efforts.

How Are Organizations Using Large Language Models?

We have reviewed how LLM benefits cyber security but how do these models benefit enterprise. Here are just a few examples of common use cases for large language models:

• Chatbots and Virtual Assistants: One of the most common applications of LLMs is their use by organizations to assist with tasks such as customer support, troubleshooting, and engaging in open-ended conversations based on user-provided prompts.
• Code Generation and Troubleshooting: LLMs can be trained on extensive collections of code examples, allowing them to generate useful code snippets in response to natural language requests. With the right techniques, LLMs can also be designed to reference additional relevant data, such as a company’s documentation, to provide more accurate responses.
• Opinion Mining: Often a hard task to quantify, LLMs can help take a piece of text and gauge emotion and opinions. This can help organizations gather the data and feedback needed to improve customer satisfaction.
• Text Categorization and Segmentation: The ability to categorize and sort large volumes of data enables the identification of common themes and trends, supporting informed decision-making and more targeted strategies.
• Language Translation: Easily globalize all your content without extensive effort by processing your web pages through suitable LLMs to translate them into various languages. As more LLMs are trained in multiple languages, both the quality and accessibility of translations will further enhance over time.
• Simplifying and Rephrasing: Entire customer calls or meetings could be efficiently summarized so that others can more easily digest the content. LLMs can take large amounts of text and boil it down to just the most important bytes.
• Content Generation: Begin with a detailed prompt for an LLM to create an outline. Follow up with additional prompts for the LLM to generate an initial draft. Use these tools for brainstorming and asking questions to spark inspiration. Note: While LLMs excel in language use, they may lack factual knowledge (e.g., recent sports winners). Always fact-check and understand responses before using them as references.

Applying Large Language Models

When considering the application of large language models, there are several approaches to explore. Broadly speaking, these approaches can be categorized into two main types, with some overlap between them. Below are the advantages and disadvantages of each, as well as the scenarios where each type is most suitable.

Commercial Solutions

Introducing large language models (LLMs) to the mainstream can be challenging. The computational demands are immense; for instance, OpenAI invested over $100 million in developing GPT-4. Moreover, ongoing operational costs are substantial due to the resources required to handle user queries. As a result, access to these powerful models typically remains under the control of organizations, necessitating data transmission to their servers. This arrangement raises concerns about privacy, security, and the transparency of model operations, as users often interact with “black box” systems they cannot oversee.

Furthermore, beyond initial limited usage, these services are not free, which can impact affordability when deploying them at scale. In summary, proprietary LLM services are ideal for complex tasks but require users to share data with third parties and be prepared for associated costs when operating at scale.

Open-source Models

The other avenue for language models is to go to the open-source community, where there has been similarly explosive growth over the past few years. Communities like Hugging Face gather hundreds of thousands of models from contributors that can help solve tons of specific use cases such as text generation, summarization, and classification. The open-source community has been quickly catching up to the performance of the proprietary models but ultimately still hasn’t matched the performance of something like GPT-4.

An additional advantage of open-source models is the ability to fine-tune them with proprietary data. Unlike proprietary services where models are opaque, open-source models can be customized to enhance performance in specific domains. This flexibility supports the trend toward organizations seeking complete control and transparency over their language models.

Overall, the future of language models is increasingly likely to embrace these open-source approaches, empowering organizations to tailor models to their unique needs while maintaining control over data and costs.

Conclusion

Every organization faces unique challenges that require tailored solutions, especially when implementing LLMs. In our increasingly expanding threat landscape and data-driven world, the effectiveness of LLMs hinges on a robust foundation of quality data. While LLMs offer remarkable capabilities, their successful deployment depends on leveraging a solid data infrastructure.

BreachLock not only provides this foundational data strength, but we also integrate our attack surface management, penetration testing, and red teaming tools essential for utilizing and refining LLMs within specific domains.

How BreachLock Uses NLP-based AI Models

BreachLock has been conducting continuous security testing for over five years now, performing thousands of penetration tests. Through this extensive experience, we have accumulated comprehensive knowledge of potential attack paths, as well as Tactics, Techniques, and Procedures (TTPs) tailored to diverse technology stacks and contexts. Aligned with industry standards such as MITRE & ATT&CK, OWASP, NIST, and OSSTMM, our automated algorithms and supervised NLP-based AI models help to refine BreachLock’s proprietary pentesting framework. Integrated seamlessly into the BreachLock Platform, our framework serves as a safeguard for precision and quality, automating routine tasks like report formatting, proof of concept integration, and basic vulnerability identification. BreachLock ensures maximum ROI by directing certified pentesters towards uncovering complex security flaws that other vendors may overlook.

About BreachLock

BreachLock is a global leader in Continuous Attack Surface Discovery and Penetration Testing. Continuously discover, prioritize, and mitigate exposures with evidence-backed Attack Surface Management, Penetration Testing and Red Teaming.

Elevate your defense strategy with an attacker’s view that goes beyond common vulnerabilities and exposures. Each risk we uncover is backed by validated evidence. We test your entire attack surface and help you mitigate your next cyber breach before it occurs.

Know your risk. Contact BreachLock today!