Emerging Security Challenges of Large Language Models (2412.17614v1)

Abstract: LLMs have achieved record adoption in a short period of time across many different sectors including high importance areas such as education [4] and healthcare [23]. LLMs are open-ended models trained on diverse data without being tailored for specific downstream tasks, enabling broad applicability across various domains. They are commonly used for text generation, but also widely used to assist with code generation [3], and even analysis of security information, as Microsoft Security Copilot demonstrates [18]. Traditional Machine Learning (ML) models are vulnerable to adversarial attacks [9]. So the concerns on the potential security implications of such wide scale adoption of LLMs have led to the creation of this working group on the security of LLMs. During the Dagstuhl seminar on "Network Attack Detection and Defense - AI-Powered Threats and Responses", the working group discussions focused on the vulnerability of LLMs to adversarial attacks, rather than their potential use in generating malware or enabling cyberattacks. Although we note the potential threat represented by the latter, the role of the LLMs in such uses is mostly as an accelerator for development, similar to what it is in benign use. To make the analysis more specific, the working group employed ChatGPT as a concrete example of an LLM and addressed the following points, which also form the structure of this report: 1. How do LLMs differ in vulnerabilities from traditional ML models? 2. What are the attack objectives in LLMs? 3. How complex it is to assess the risks posed by the vulnerabilities of LLMs? 4. What is the supply chain in LLMs, how data flow in and out of systems and what are the security implications? We conclude with an overview of open challenges and outlook.

An Analysis of Emerging Security Challenges in LLMs

The paper by Debar et al. presents an extensive examination of the security vulnerabilities inherent in LLMs, such as ChatGPT, considering their unprecedented adoption across sectors like education and healthcare. Given that LLMs are increasingly utilized for tasks ranging from text generation to code assistance, understanding their security implications becomes imperative. The authors focus on the vulnerabilities of LLMs to adversarial attacks, analyzing how these models differ from traditional machine learning approaches.

Adversarial Vulnerabilities

LLMs exhibit unique adversarial vulnerabilities due to their probabilistic nature, which leads to phenomena like hallucinations. These hallucinations can potentially be exploited by attackers to manipulate outputs for malicious purposes. While traditional machine learning models also face adversarial attacks, LLMs' tendency to hallucinate nonsensical or irrelevant content adds layers of complexity. Notably, the transformer architecture, which underpins LLMs, remains a frontier for further research into security, given its intricate components such as multi-head attention mechanisms.

Attack Objectives and Impacts

Potential attack objectives on LLMs include model theft, denial of service, privacy breaches, systematic bias, model degeneration, and falsified outputs. These objectives underline the diverse avenues through which adversaries might attempt to compromise LLM integrity. Particularly concerning is the prospect of LLMs generating backdoored or systematically biased outputs, which could influence critical decisions in domains that leverage these models for sensitive tasks.

Complexity of Risk Assessment

Assessing the security risks of LLMs is inherently complex due to factors such as the opacity of content models, the task-agnostic nature of LLMs, and the diversity of their applications. The use of diverse, large-scale training datasets raises concerns about the inclusion of biased or poisoned data, exacerbating the difficulty of ensuring robust security postures. Furthermore, the rapid advancements in LLM technologies challenge the ability to maintain up-to-date security defenses.

Supply Chain Vulnerabilities

The LLM supply chain, encompassing model training, fine-tuning, and user interactions, presents multiple vulnerability points. Data poisoning is a significant threat, enabled through malicious data introduced during training or fine-tuning phases. The authors identify that both training data and user feedback loops can be exploited for attack purposes, posing risks of permanent or ephemeral damage to model functionality.

Challenges and Future Directions

The paper highlights challenges in securing LLMs while also positing questions aimed at guiding future research. The trade-offs between LLM functionality and vulnerability avoidance are particularly emphasized; this delineates a pressing need for novel defense strategies that can address the sophisticated nature of attacks on these models. The authors advocate for more comprehensive research into systemic vulnerabilities and effective mitigation techniques.

Conclusion

While LLMs hold the promise of fundamentally transforming multiple sectors, their deployment must be tempered with careful consideration of security challenges. The work of Debar et al. underscores the urgency of addressing these concerns, presenting both a precise analysis of current threats and a clarion call for the development of resilient, secure AI systems. As LLMs continue to proliferate, their impact on society will necessitate a balanced approach that upholds security without hindering innovation.