What is a Large Language Model (LLM)?

What are LLMs (Large Language Models)?

Large language models (LLMs) are enabling businesses and consumers alike to transform operations and redefine productivity with the power of AI. Through their advanced natural language processing capabilities, LLMs can be productivity force multipliers. But their rise also poses unique challenges within cybersecurity, shaping adversary tradecraft and the day-to-day roles of modern defenders.

In this post, we’ll dive into what LLMs are, their impact on cybersecurity, and how SecOps teams can leverage LLMs for protection while guarding against new forms of AI-enabled threats. Let’s begin by unpacking the core concepts.

Generative AI and LLMs

Generative AI (GenAI) represents a subset of AI that’s focused on creating new content in various forms, such as text, code, images, audio, and video. Central to the advancements in GenAI are LLMs, which have demonstrated remarkable proficiency in understanding and generating human language.

Before the advent of LLMs, traditional language models were limited by smaller datasets and simpler algorithms. They were constrained in their ability to grasp nuance and complexity in language and in their available computational capacity. Recent breakthroughs in LLMs have equipped data scientists to operate on a vastly larger scale of data and complexity, enabling LLMs to understand context, generate coherent text, and even exhibit a form of “creativity” that was previously unattainable for AI.

LLMs depend on several critical technologies and processes:

• Massive datasets: LLMs are trained on extensive collections of data, encompassing a wide range of human knowledge and languages.
• Advanced algorithms: Techniques like transformer models enable LLMs to understand the context and relationships between words in a sentence far better than earlier models.
• Neural networks: LLMs are built on neural networks that mimic the way human brains operate, allowing for more sophisticated understanding and generation of content.
• Natural language processing (NLP): This foundational technology enables LLMs to interpret, understand, and generate human language in a way that is both meaningful and contextually relevant.
• Continuous learning: LLMs often undergo continuous training, incorporating new data over time to improve and update their capabilities.

As with most AI fields, the role of data in the effectiveness of LLMs cannot be overstated. To train LLMs effectively, it’s not just the quantity of data that matters but its quality and diversity. Training data covers a broad spectrum of topics, languages, and formats, enabling LLMs to perform a wide variety of tasks — from translation and content creation to question answering and conversation simulation — with remarkable accuracy and relevance.

LLMs (and GenAI) have revolutionized how businesses approach tasks like content creation, customer service, and even software development. However, the prevalence of LLM-based applications in fields such as healthcare, finance, government, and education emphasizes the critical need for effective security measures that foster safe and responsible adoption of generative AI.

Security considerations when using LLMs

As the adoption of LLMs across sectors continues to grow, so does the complexity of ensuring their security. Given their broad capabilities, the sensitive nature of the data they process, and the workflows they automate, securing LLM applications is crucial to preventing misuse, data breaches, and other vulnerabilities. Key security considerations include:

• Handling proprietary or sensitive data: Using sensitive data in LLM training without adequate safeguards can lead to privacy violations and data leakage.
• Securing your data pipeline and tooling: Ensuring the security of the data pipeline and the tools used in LLM development is essential to protect against unauthorized access and data tampering.
• Protecting your models: Attackers may attempt to manipulate the model’s training data to compromise its integrity, leading to malicious outputs.
• Understanding your data: Inherent biases in training data can perpetuate and amplify stereotypes or unfair representations, so it is necessary to implement measures to identify and mitigate such biases.

The OWASP Top 10 for Large Language Model Applications offers additional detailed guidance aimed at securing LLM applications.

LLMs in cybersecurity: the bright side

Generative AI poses enormous transformative potential for cybersecurity, promising significant gains in workflow effectiveness and efficiency — especially in an industry characterized by persistent shortages in skilled labor. Cybersecurity solutions like CrowdStrike® Charlotte AI™ enable organizations to become significantly more efficient in detecting and responding to threats. In concert with AI-native threat intelligence and detection capabilities, modern cybersecurity platforms can analyze and operationalize vast amounts of data while providing actionable insights to users of all skill levels. This assistance is especially crucial in a landscape where threats evolve rapidly and the speed of detection can make the difference between a minor incident and a major breach.

The ability of LLMs to understand and process natural language also enables organizations to  sift through unstructured data — like emails and social media posts — to identify subtle indicators of phishing attempts, malware, or other cyber threats.

LLMs and cyber threats: the dark side

Unfortunately, the capabilities that make LLMs valuable for modern defenders can also be weaponized by cybercriminals. The advent of dark AI has shown how LLMs can be used to craft more convincing phishing emails, automate the generation of malicious code, or even manipulate social media with disinformation campaigns. Tools like FraudGPT exemplify how LLMs can be adapted for malicious purposes, creating challenges for cybersecurity defenses.

LLMs have led to a new arms race in the digital domain: as cybercriminals leverage AI for malicious purposes, defenders must also harness the power of AI to stay one step ahead. This dynamic underscores the importance of developing and implementing cutting-edge AI solutions that can adapt to and counteract the evolving tactics of cyber adversaries.

Protecting your GenAI and LLM-based applications

In this post, we’ve looked at the impact and mechanics of LLMs, highlighting their transformative role as the foundation of GenAI applications across sectors. We’ve looked at how these models are reshaping the landscape of digital innovation. We’ve also discussed the security considerations for developing and deploying LLM-based applications, especially in light of ever-evolving cyber threats that grow in sophistication with each day.

In the face of these challenges, AI-native cybersecurity tools play a critical role. Specifically designed to combat AI-enabled threats, these tools represent the cutting edge of cybersecurity. The CrowdStrike Falcon® platform leverages solutions like AI-powered indicators of attack (IOAs) and provides AI-native protection in its threat intelligence and threat hunting. Bundling in advanced systems like Charlotte AI, the Falcon platform helps enterprises establish a proactive stance against today’s cyber threat actors.

To learn more about the Falcon platform, contact our team today.