LLM Security Challenges and Security Compliance Governance Framework
Why is It Important to secure Large Language Models (LLM)?
LLMs like OpenAIs GPT (Generative Pre-trained Transformer), Google Gemini and Meta LLaMA have revolutionized the way we interact with AI, enabling applications in translation, content creation, and coding assistance.
However, as LLMs enter mainstream use, securing them becomes much more important, especially in sensitive applications like finance, healthcare, and legal services. Vulnerabilities in LLMs can lead to misinformation, privacy breaches, and manipulation of information, posing significant risks to individuals and organizations.
With the increasing reliance on LLMs, the exposure to cyber threats also escalates. Cyber attackers can exploit vulnerabilities to perform attacks such as data poisoning, model theft, or unauthorized access. Implementing robust security measures is essential to protect the integrity of the models and the data they process.
The trends and Statistics of LLM Security Risks
As Large Language Models (LLMs) like OpenAI’s GPT, Meta’s LLaMA, and Google’s BERT become integral to more applications, their security vulnerabilities and the landscape of related cyber threats have come under increasing scrutiny.
A recent study by Cybersecurity Ventures predicts that by 2025, cybercrime will cost the world $10.5 trillion annually, a huge increase from $3 trillion in 2015, with much of the rise attributed to the use of advanced technologies like LLMs.
Adversarial attacks against LLMs are becoming more sophisticated. In 2023 alone, several high-profile incidents demonstrated that even well-secured models like GPT-4 could be susceptible when faced with novel attack vectors. These attacks not only manipulate model outputs but also seek to steal sensitive data processed by the models.
With the increasing deployment of LLMs in sensitive areas, regulatory bodies are beginning to step in. For instance, the European Union’s Artificial Intelligence Act is set to introduce stringent requirements for AI systems, including LLMs, focusing on transparency, accountability, and data protection.
Who is responsible for LLM Security?
Many organizations and end-users consume LLMs through websites or managed services, such as ChatGPT, Claude and Google’s Gemini. In these cases, the responsibility for model security and infrastructure security rests primarily with the service provider.
However, when organizations deploy LLMs on-premises, for example, via open source options like LLaMA, Qwen or commercial on-premises solutions like Tabnine, they have additional security responsibilities. In these cases, the organization deploying and operating the model shares responsibility for securing its integrity and the underlying infrastructure.
The following figure shows Microsoft's AI shared responsibility model,
Microsoft recommends organizations start with SaaS based approaches like the Copilot model for their initial adoption of AI and for all subsequent AI workloads. This minimizes the level of responsibility and expertise your organization has to provide to design, operate, and secure these highly complex capabilities.
If the current "off the shelf" capabilities don't meet the specific needs for a workload, you can adopt a PaaS model by using AI services, such as Azure OpenAI Service, to meet those specific requirements.
Custom model building should only be adopted by organizations with deep expertise in data science and the security, privacy, and ethical considerations of AI.
The significance of Governance in LLM Security
Governance structures play a crucial role in establishing transparency, accountability, and adherence to ethical standards throughout the entire lifecycle of LLM applications. By effectively managing risks associated with bias, misinformation, and unintended consequences, governance mechanisms provide a clear framework for ownership and responsibilities, enabling organizations to navigate potential challenges and mitigate adverse events.
According to OWASP, to establish robust governance in LLM security, organizations should
create an AI RACI chart, define roles, and assign responsibilities
document and assign AI risk assessments and governance responsibilities for a structured approach to risk management
implement data management policies, with a focus on data classification and usage limitations.
Crafting an overarching AI Policy aligned with established principles ensures comprehensive governance.
publish an acceptable use matrix for various generative AI tools, providing employee guidelines.
documenting the sources and management of data from generative LLM models ensures transparency and accountability in their utilization.
Management for enhanced LLM Security
Step1：Identifying LLM Security Challenges
The definition and scope of LLM security are discussed and defined in detail in ”Introduction to Framework of LLM(Large Language Model) Security“, which includes:
LLM security compliance governance
LLM risk control and content security governance
LLM infrastructure and application security
LLM ethical considerations, LLM security compliance governance
Different from traditional cybersecurity practices, LLMs face challenges like prompt injection, training data poisoning or breach of Personally Identifiable Information. Identifying such threats is crucial for safeguarding against attacks.
LLMs also face ethical issues for generating content. Stemming from pretraining on extensive datasets, LLMs are known to exhibit concerning behaviors such as generating misinformation, biased outputs, etc. While GPT-4 exhibits reduced hallucination and harmful content generation (according to OpenAI) it still reinforces social biases and may introduce emergent risks like social engineering and interactions with other systems. LLM-integrated applications, for example by Bing Chat (Microsoft), have faced public concerns due to unsettling outputs, prompting limitations on the chatbot's interactions. Instances of factual errors, blurred source credibility, and automated misinformation have occurred in search-augmented chatbots, emphasizing the need for vigilant risk mitigation strategies in LLM applications.
Step2：Determine basic risk management strategies
Regular Audits
Developing LLM auditing procedures is an important and timely task for two reasons.
First, LLMs pose many ethical and social challenges, including the perpetuation of harmful stereotypes, the leakage of personal data protected by privacy regulations, the spread of misinformation, plagiarism, and the misuse of copyrighted material. Recently, the scope of impact from these harms has been dramatically scaled by unprecedented public visibility and growing user bases of LLMs. For example, ChatGPT attracted over 100 million users just two months after its launch.
Second, LLMs can be considered proxies for other foundation models. Consider CLIP, a vision-language model trained to predict which text caption accompanied an image, as an example. CLIP too displays emergent capabilities, can be adapted for multiple downstream applications, and faces similar governance challenges as LLMs. The same holds of text2image models such as DALL·E 2. Developing feasible and effective procedures for how to audit LLMs is therefore likely to offer transferable lessons on how to audit other foundation models and even more powerful generative systems in the future.
Third, Unlike static models, LLMs evolve over time, refining their language capabilities and adapting to new information. This constant evolution poses challenges for traditional security measures, requiring strategies that can dynamically respond to emerging threats and vulnerabilities. The need for real-time monitoring, rapid updates, and flexible security protocols becomes crucial in safeguarding LLMs against evolving risks.
Incident Response Planning
Encouraging the development of a robust incident response plan is crucial to effectively address security breaches or issues that may arise during LLM deployment.
This strategy involves creating a detailed guidebook outlining the steps to be taken in the event of a security breach.
Drawing from the proactive risk management information, incident response plans should be updated to specifically address LLM incidents. For example, the plan could include steps to counteract potential model exploitation or mitigate risks associated with adversarial attacks on LLM-generated content.
Adopting Security Best Practices
Suggesting the alignment of LLM operations with established security best practices is a foundational strategy.
Reference can be made to the information provided on AI Asset Inventory, emphasizing the importance of cataloging AI components and applying the Software Bill of Material (SBOM) to ensure comprehensive visibility and control over all software components. Aligning with OWASP’s guidelines on security best practices ensures that the organization incorporates industry-recognized measures into their LLM deployment, enhancing security posture and reducing the risk of potential threats.
Step3：Integrating Security Practices
In essence, comprehending and effectively managing the risks associated with Large Language Models (LLMs) is imperative for maintaining secure operations. Whether addressing adversarial risks, ensuring ethical considerations, or navigating legal and regulatory landscapes, a proactive stance toward risk management is key.
While there are several challenges that are inherent to LLM security, adopting established security practices that align with the problem at hand can create a robust defense to known problems.
Safe model development
Adversarial training
Adversarial risks pose a threat to LLMs, and encompass a myriad of malicious activities that seek to compromise the integrity of these advanced language systems. The different adversarial risks are as follows:
Injection Attacks：Adversaries may attempt to inject malicious data into the LLM to manipulate its outputs or compromise its functionality.
Model Manipulation：Manipulating the model's outputs to produce biased or harmful results, influencing the generated content to serve malicious purposes.
Poisoning Attacks：Injecting tainted data into the training dataset to distort the model's learning process, leading to biased or compromised outputs.
Evasion Techniques：Adversaries may employ evasion tactics to bypass security measures, exploiting vulnerabilities in the model's understanding capabilities.
Data Interference：Deliberate introduction of deceptive or misleading information into the input data to manipulate the model and influence generated outputs.
Privacy Breaches：Extraction of sensitive information from the model's responses, leading to privacy violations or unauthorized access to confidential data used during training.
Semantic Attacks：Crafting inputs with subtle changes to manipulate the semantic meaning of the generated content, potentially leading to misinformation or miscommunication.
Transfer Learning Attacks：Exploiting vulnerabilities in the model's transfer learning capabilities to transfer biases or manipulate outputs from a source domain to a target domain.
Adversarial Training Set Attacks：Deliberate inclusion of adversarial examples in the model's training set to influence its behavior and compromise its generalization capabilities.
Syntactic Attacks：Introducing alterations to the syntax of input data to deceive the model and generate outputs with unintended or harmful implications.
False Positive/Negative Generation：Adversaries may target the model's decision-making process, influencing it to produce false positives or negatives, potentially leading to erroneous actions based on generated content.
Adversarial training involves exposing the LLM to adversarial examples during its training phase, enhancing its resilience against attacks. This method teaches the model to recognize and respond to manipulation attempts, improving its robustness and security.
By integrating adversarial training into LLM development and deployment, organizations can build more secure AI systems capable of withstanding sophisticated cyber threats.
Adopting federated learning
Federated learning allows LLMs to be trained across multiple devices or servers without centralizing data, reducing privacy risks and data exposure. This collaborative approach enhances model security by distributing the learning process while keeping sensitive information localized.
Implementing federated learning strategies boosts security and respects user privacy, making it useful for developing secure and privacy-preserving LLM applications.
An example of such strategic deployment consideration is data decentralization, as shown in this paper, where the authors motivate the problem from a medical/clinical point of view. Although LLMs, such as GPT-4, have shown potential in improving chatbot-based systems in healthcare, their adoption in clinical practice faces challenges, including reliability, the need for clinical trials, and patient data privacy concerns.
The authors provide a general architecture (shown above) in which they identify the key components for building such a decentralized system concerning data protection legislation, independently of the specific technologies adopted and the specific health conditions it will be applied to. The system enables the acquisition, storing, and analysis of users’ data, but also mechanisms for user empowerment and engagement on top of profiling evaluation.
Incorporating differential privacy mechanisms
Differential privacy introduces randomness into data or model outputs, preventing the identification of individual data points within aggregated datasets. This approach protects user privacy while allowing the model to learn from broad data insights.
Adopting differential privacy mechanisms in LLM development ensures that sensitive information remains confidential, enhancing data security and user trust in AI systems.
Implementing bias mitigation techniques
Bias mitigation techniques address and reduce existing biases within LLMs, ensuring fair and equitable outcomes.
Approaches can include algorithmic adjustments, re-balancing training datasets, and continuous monitoring for bias in outputs. By actively working to mitigate bias, developers can enhance the ethical and social responsibility of LLM applications.
Proactively updating models with security patches
An critical measure is the proactive updating of models with security patches. This ensures that the deployed LLMs stay resilient against emerging threats by addressing known vulnerabilities promptly.
For example, ZeroLeak is a LLM-based patching framework for mitigating side-channel information leak in LLM-based code generation. ZeroLeak’s goal is to make use of the massive recent advances in LLMs such as OpenAI GPT, Google PaLM, and Meta LLaMA to generate patches automatically. The overview of the ZeroLeak framework is shown below.
Secure model deployment
Input validation mechanisms
Input validation mechanisms prevent malicious or inappropriate inputs from affecting LLM operations. These checks ensure that only valid data is processed, protecting the model from prompt injection and other input-based attacks.
Implementing thorough input validation helps maintain the security and functionality of LLMs against exploitation attempts that could lead to unauthorized access or misinformation.
Access controls
Access controls limit interactions with the LLM to authorized users and applications, protecting against unauthorized use and data breaches.
These mechanisms can include authentication, authorization, and auditing features, ensuring that access to the model is closely monitored and controlled.
By enforcing strict access controls, organizations can mitigate the risks associated with unauthorized access to LLMs, safeguarding valuable data and intellectual property.
Secure execution environments
Secure execution environments isolate LLMs from potentially harmful external influences, providing a controlled setting for AI operations.
Techniques such as containerization and the use of trusted execution environments (TEEs) enhance security by restricting access to the model’s runtime environment.
Creating secure execution environments for LLMs is crucial for protecting the integrity of AI processes and preventing the exploitation of vulnerabilities within the operational infrastructure.
Network security protection
Data Protection：The fundamental need to protect sensitive data is a shared principle between LLM security and traditional cybersecurity. For instance, just as in traditional cybersecurity, encrypting data inputs and outputs is crucial to prevent unauthorized access and ensure confidentiality.
Network Security：Ensuring the integrity and security of network connections is a commonality. LLMs, like any other system, benefit from robust network security practices. For instance, implementing firewalls and intrusion detection systems helps mitigate potential threats in the communication channels.
User Access Controls：Managing and controlling user access is a universal aspect. Standard practices, such as role-based access control (RBAC), play a vital role in both traditional cybersecurity and LLM security. Proper access controls prevent unauthorized users from manipulating or exploiting the language model.
Monitoring and response
Adopt a proactive stance through continuous monitoring, employing real-time monitoring tools to track system performance and detect anomalies around security risks. This includes monitoring for signs of misuse, such as unexpected model outputs or unusual access patterns.
Develop, maintain, and periodically update incident response plans tailored to LLM applications. The plans must center around the dangers of LLMs’ inherent bias, lack of explainability, and potential to cause individual/societal harm through data breaches and misinformation. Outline clear action plans for each threat scenario.
Continuous dynamic testing(RedTeam for LLMs)
Use automated tools for security testing and vulnerability scanning in LLM development and deployment pipelines.
Conduct LLM-based red teaming exercises to uncover vulnerabilities and develop adversarial robustness.
Staff training and awareness
Secure Coding Practices：Adhering to secure coding practices is not exclusive to traditional applications, it is equally crucial in the LLM landscape. Proper coding practices help mitigate vulnerabilities that could be exploited, reinforcing the overall security posture of language models.
Personnel training：Upskill and educate developers on LLM security threats and protocols. The OWASP Top 10 for LLMs is a great starting point.
Conclusion
Integrating LLMs into business applications offers significant benefits, but minimizing LLM security risks is critical to their successful deployment and profitability. Securing your LLM applications is a continuous and iterative process.
In conclusion, navigating the landscape of Large Language Model (LLM) security requires a dual approach, embracing both theoretical knowledge and real-world insights. From foundational principles to advanced tools and real-world insights, the journey through LLM security underscores its pivotal role in responsible technological advancement.
As we navigate the evolving landscape of LLMs, a proactive and adaptive approach to security becomes paramount. By integrating established cybersecurity practices, understanding legal and regulatory frameworks, and leveraging cutting-edge tools like LLMVSpy stakeholders can fortify the reliability and ethical use of LLMs.
Additional Resources
The OWASP Top 10 provides a checklist of recommendations for LLM implementation and security post-deployment. MITRE ATT&CK is another global knowledge base, providing insights into adversary tactics and techniques from real-world observations. It serves as a foundation for developing specific threat models and methodologies across various sectors, promoting collaboration in the cybersecurity community. MITRE's commitment to a safer world is evident in the open accessibility of ATT&CK, freely available for use by individuals and organizations.
The AI Incident Database stands as a pivotal resource, meticulously cataloging real-world harms or potential risks stemming from AI systems. Modeled after analogous databases in fields like aviation and computer security, its primary objective is to facilitate learning from these incidents, allowing for the prevention or mitigation of similar issues in the future. By exploring the diverse cases within the database, you can gain valuable insights into the multifaceted challenges posed by AI.
LLM Security Net is a dedicated platform designed for the in-depth exploration of failure modes in LLMs, their underlying causes, and effective mitigations. The website serves as a comprehensive resource, featuring a compilation of LLM security content, including research papers and news. You can stay informed about the latest developments in LLM security by accessing detailed information on LLM Security Net official website.