AI-Security Essentials for Decision-makers: The Rising Significance of Large Language Models (LLM) Security

1. Introduction

The technological landscape has witnessed a transformative change in recent years with the advent of Large Language Models (LLMs). These sophisticated AI systems, capable of understanding, generating, and interacting with human language, are not just a scientific marvel but are reshaping industries. From enhancing customer service through chatbots to revolutionizing content creation and aiding in complex data analysis to driving innovation in healthcare, LLMs are proving to be a cornerstone in the digital era. Their versatility and growing adoption underscore a pivotal shift in how we interact with technology, making them a subject of immense interest and investment across various sectors.

However, as with any powerful technology, LLMs come with their own challenges and vulnerabilities, particularly in cybersecurity. The very features that make LLMs valuable – vast data processing capabilities, adaptability, and deep integration into business processes – also make them attractive targets for cyber threats. Issues such as data breaches, manipulation of model outputs, unauthorized access, and exploitation of inherent biases in AI models pose significant risks. The implications of such vulnerabilities are far-reaching, affecting not just the integrity and reliability of the models but also the security and privacy of user data. This intersection of AI and cybersecurity is a critical frontier that demands immediate attention and action.

This article specifically focuses on the risks associated with Large Language Models. By aligning our discussion with established cybersecurity frameworks – the OWASP Top 10 for Large Language Models applications [1], MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) [2], and the NIST AI Risk Management Framework (AI RMF 1.0) [3] – we aim to provide a comprehensive understanding of these risks. The objective is not only to highlight the potential cybersecurity threats posed by LLMs but also to offer insights into how these risks can be identified, assessed, and mitigated.

2. Understanding Large Language Models (LLMs)

LLMs are advanced artificial intelligence systems designed to understand, interpret, and generate human language in a coherent and contextually relevant way. At their core, these models are trained on vast text datasets, allowing them to learn language patterns, nuances, and structures. This training enables LLMs to perform various language-related tasks, from translation and summarization to question-answering and content creation.

The functionality of LLMs is rooted in Machine Learning algorithms, particularly Deep Learning. These models use neural networks with multiple layers (hence "large") to process and analyze text. The most significant feature of LLMs is their ability to generate human-like text based on the input they receive, making them incredibly versatile tools in processing and generating language.

The applications of LLMs span a diverse array of industries. In the tech sector, they're used to improve search engine results, enabling more accurate and relevant responses to user queries. In customer service, LLMs power chatbots and virtual assistants, providing efficient and human-like customer interactions.

In content creation, LLMs assist in generating reports and even creative writing, drastically reducing the time and effort required. Furthermore, in education, they're used to create personalized learning materials, and in healthcare for processing medical documentation and literature.

Despite their immense potential, LLMs carry inherent cybersecurity risks. One significant vulnerability is data poisoning, where malicious actors manipulate the training data to make the model produce incorrect or biased outputs. This can have far-reaching consequences, especially if the LLM is used in critical decision-making processes.

Another risk is model inversion attacks, where attackers use output data to infer sensitive information about the model's training data. This could potentially lead to privacy breaches, especially if the training data included personal information.

Furthermore, the complexity of LLMs can lead to unintended biases in their outputs. These biases could perpetuate stereotypes or result in unfair outcomes, particularly in sensitive applications like hiring or loan approvals.

In conclusion, while Large Language Models are transformative tools in the field of artificial intelligence, their widespread application across industries necessitates a thorough understanding of their inherent vulnerabilities to ensure they are used safely and ethically.

3. OWASP Top 10 for LLMs

The OWASP Top 10 for Large Language Model Applications project aims to educate developers, designers, architects, managers, and organizations about the potential security risks when deploying and managing LLMs. The project provides a list of the top 10 most critical vulnerabilities often seen in LLM applications, highlighting their potential impact, ease of exploitation, and prevalence in real-world applications:

?? LLM01. Prompt Injection - This manipulates a LLM through crafty inputs, causing unintended actions by the LLM. Direct injections overwrite system prompts, while indirect ones manipulate inputs from external sources.

?? LLM02. Insecure Output Handling - This vulnerability occurs when an LLM output is accepted without scrutiny, exposing backend systems. Misuse may lead to severe consequences like XSS, CSRF, SSRF, privilege escalation, or remote code execution.

?? LLM03. Training Data Poisoning - This occurs when LLM training data is tampered, introducing vulnerabilities or biases that compromise security, effectiveness, or ethical behavior. Sources include Common Crawl, WebText, OpenWebText, & books.

?? LLM04. Model Denial of Service - Attackers cause resource-heavy operations on LLMs, leading to service degradation or high costs. The vulnerability is magnified due to the resource-intensive nature of LLMs and unpredictability of user inputs.

?? LLM05. Supply Chain Vulnerabilities - LLM application lifecycle can be compromised by vulnerable components or services, leading to security attacks. Using third-party datasets, pre-trained models, and plugins can add vulnerabilities.

?? LLM06. Sensitive Information Disclosure - LLMs may inadvertently reveal confidential data in its responses, leading to unauthorized data access, privacy violations, and security breaches. It’s crucial to implement data sanitization and strict user policies to mitigate this.

?? LLM07. Insecure Plugin Design - LLM plugins can have insecure inputs and insufficient access control. This lack of application control makes them easier to exploit and can result in consequences like remote code execution.

?? LLM08. Excessive Agency - LLM-based systems may undertake actions leading to unintended consequences. The issue arises from excessive functionality, permissions, or autonomy granted to the LLM-based systems.

?? LLM09. Overreliance - Systems or people overly depending on LLMs without oversight may face misinformation, miscommunication, legal issues, and security vulnerabilities due to incorrect or inappropriate content generated by LLMs.

?? LLM10. Model Theft - This involves unauthorized access, copying, or exfiltration of proprietary LLM models. The impact includes economic losses, compromised competitive advantage, and potential access to sensitive information.

4. MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems)

MITRE ATLAS is a detailed repository encompassing various adversarial strategies and methodologies specifically targeted at Artificial intelligence (AI) systems. This knowledge base includes an array of adversary tactics, techniques, and real-world case studies, making it an invaluable tool for those seeking to understand and protect AI technologies. Let’s have a look at them in more detail. This section explores its applications in AI and LLM security, considering their tactics:

?? 1. Reconnaissance?- The adversary is trying to gather information about the machine learning system they can use to plan future operations. Techniques: Search for Victim’s Publicly Available Research Materials; Search for Publicly Available Adversarial Vulnerability Analysis; Search Victim-Owned Websites; Search Application Repositories; and Active Scanning.

?? 2. Resource Development? - The adversary is trying to establish resources they can use to support operations. Techniques: Acquire Public ML Artifacts; Obtain Capabilities; Develop Capabilities; Acquire Infrastructure; Publish Poisoned Datasets; Poison Training Data; and Establish Accounts.

?? 3. Initial Access - The adversary is trying to gain access to the machine learning system. Techniques: ML Supply Chain Compromise; Valid Accounts; Evade ML Model; Exploit Public Facing Application; and LLM Prompt Injection; Phishing.

?? 4. ML Model Access - The adversary is attempting to gain some level of access to a machine learning model.? Techniques: ML Model Inference API access; ML-Enabled Product or Service; Physical Environment Access; and Full ML Model Access.

?? 5. Execution - The adversary is trying to run malicious code embedded in machine learning artifacts or software. Techniques: User Execution; Command and Scripting Interpreter; and LLM Plugin Compromise.

?? 6. Persistence - The adversary is trying to maintain their foothold via machine learning artifacts or software. Techniques: Poison Training data; Backdoor ML Model; and LLM Prompt Injection.

?? 7. Privilege Escalation -The adversary is trying to gain higher-level permissions. Techniques: LLM Prompt Injection; LLM Plugin Compromise; and LLM Jailbreak.

?? 8. Defense Evasion - The adversary is trying to avoid being detected by machine learning-enabled security software. Techniques: Evade ML Model; LLM Prompt Injection; and LLM Jailbreak.

?? 9. Credential Access - The adversary is trying to steal account names and passwords. Technique: Unsecured Credentials.

?? 10. Discovery - The adversary is trying to figure out your machine learning environment. Techniques: Discover ML Model Ontology; Discover ML Model Family; Discover ML Artifacts; and LLM Meta Prompt Extraction.

?? 11. Collection - The adversary is trying to gather machine learning artifacts and other related information relevant to their goal. Techniques: ML Artifact Collection; Data From Information Repositories; and Data from Local System.

?? 12. ML Attack Staging - The adversary is leveraging their knowledge of and access to the target system to tailor the attack. Techniques: Create Proxy ML Model; Backdoor ML Model; Verify Attack; and Craft Adversarial Data.

?? 13. Exfiltration - The adversary is trying to steal machine learning artifacts or other information about the machine learning system. Techniques: Exfiltration via ML Inference API; Exfiltration via Cyber Means; LLM Meta Prompt Extraction; and LLM Data Leakage.

?? 14. Impact - The adversary tries to manipulate, interrupt, erode confidence in, or destroy your machine learning systems and data. Techniques: Evade ML Model; Denial of ML Service; Spamming ML System with Chaff Data; Erode ML Model Integrity; Cost Harvesting; and External Harms.

5. NIST AI Risk Management Framework (AI RMF 1.0)

The NIST AI RMF Core outlines a framework for managing AI risks and developing trustworthy AI systems through dialogue and understanding. It consists of four main functions: GOVERN, MAP, MEASURE, and MANAGE, which are further broken down into categories and sub-categories. These divisions detail specific actions and outcomes to guide organizations, but they are not strictly a checklist or sequential steps. The framework facilitates a comprehensive approach to AI risk management.

?? GOVERN function - This approach promotes a risk management culture in organizations that deal with AI systems across various stages like design, development, deployment, evaluation, or acquisition. It emphasizes the need for processes and documentation that proactively identify and manage potential risks, impacting users and society. The framework integrates impact assessments, aligns risk management with organizational values and strategic priorities, and connects AI technicalities with organizational ethics. It also enhances skills for those handling AI systems and covers the entire product lifecycle, addressing legalities and third-party integrations.

?? MAP function - The framework sets the stage for understanding risks associated with AI systems, acknowledging the complex interplay of activities and actors in the AI lifecycle. Often, those responsible for one aspect of the process lack complete insight or control over other parts, leading to challenges in foreseeing the full impact of AI systems. For instance, initial decisions about the AI system's purpose can significantly influence its behavior and capabilities, while the deployment environment shapes its real-world effects. This interconnectedness means that subsequent decisions and conditions can compromise well-intentioned actions in one stage.

?? MEASURE function - This function involves using various tools and methods, both quantitative and qualitative, to analyze and monitor AI risks and their impacts. This process builds on the AI risks mapped out earlier and informs the management strategies. It emphasizes the importance of testing AI systems both pre-deployment and during operation. Measuring AI risks involves documenting system functionality and trustworthiness, tracking metrics for trustworthy AI characteristics, assessing social impact, and evaluating human-AI interaction. Rigorous testing, performance assessment, and independent reviews are critical for effective risk measurement and management.

?? MANAGE function - The MANAGE function involves allocating resources to address identified and evaluated risks, guided by the principles set by the GOVERN function. This includes planning for response, recovery, and communication regarding potential incidents. This function aims to reduce the likelihood of system failures and adverse impacts by using insights from experts and relevant AI stakeholders established in GOVERN and executed in MAP. Systematic documentation, part of GOVERN, MAP, and MEASURE, supports AI risk management and enhances transparency. This function also includes processes for identifying new risks and mechanisms for continuous improvement, ensuring ongoing effective management and resource allocation for AI system risks.

6. Conclusion

In conclusion, this article delved into the critical importance of cybersecurity in the realm of Large Language Models (LLMs), guided by frameworks like the OWASP Top 10 for LLMs, MITRE ATT&CK for ATLAS, and the NIST AI RMF 1.0. We explored how these frameworks provide comprehensive strategies for identifying and mitigating risks, emphasizing the need for ongoing vigilance and proactive measures in cybersecurity practices for LLMs. As the landscape of AI and cybersecurity evolves, I invite readers to share feedback and contribute to a collaborative approach.

References:

[1] OWASP. "Top 10 for LLM Applications VERSION 1.1". https://owasp.org/www-project-top-10-for-large-language-model-applications/assets/PDF/OWASP-Top-10-for-LLMs-2023-v1_1.pdf (2023).

[2] MITRE. MITRE ATLAS? (Adversarial Threat Landscape for Artificial-Intelligence Systems). https://atlas.mitre.org/ (2024).

[3] AI, NIST. "Artificial Intelligence Risk Management Framework (AI RMF 1.0)." https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf (2023).