Large language models are advanced AI systems designed to process and generate human-like text with remarkable accuracy. Trained on extensive datasets, these models power a wide range of applications – from enhancing customer support to driving innovation in content creation and data analysis. However, as LLMs enter mainstream use, securing them is more critical than ever. Vulnerabilities within these systems can lead to misinformation, privacy breaches, and manipulation of information, posing significant risks to individuals and organisations.
This guide will explore LLM security and risks and threats associated with its implementations while providing practical strategies to ensure your systems remain robust and secure.
What is large language model (LLM) security?
LLM security refers to a comprehensive set of policies, practices, and technologies designed to protect large language models from exploitation, ensuring data integrity and privacy. It addresses challenges such as adversarial attacks, data poisoning, and misuse of generated content, aiming to uphold trust and reliability in AI-driven systems.
Securing an LLM differs significantly from securing a traditional software system. They process vast amounts of diverse and sensitive training data, which can introduce unique vulnerabilities andunlike traditional systems, which are logically structured and can be audited for security, LLMs are not easily understandable or explainable due to their complexity. This makes it challenging for companies to identify potential security risks and defend against them.
Moreover, while conventional systems behave deterministically – produce predictable outputs – LLMs generate results probabilistically. This inherent unpredictability can lead to unintended or unforeseen behaviours, further complicating their security landscape. As a result, securing LLMs demands specialised approaches and continuous vigilance to address these evolving challenges.
Essential read: What is AI security?
Why security in LLM usage matters?
A recent report by Cybersecurity Ventures forecasts that global cybercrime will cost $10.5 trillion annually by 2025, a sharp rise from $3 trillion in 2015, with much of the rise due to the use of advanced technologies like LLMs. This alarming figure highlights the critical need for strong security measures in LLM usage as it empowers organisations to mitigate the following risks:
Financial risks
Security breaches involving a LLM can have serious financial consequences for businesses. When sensitive data is exposed or misused via exploited AI systems, businesses may face substantial costs, including fines, litigation fees, and operational disruption. For example, a data leak caused by insufficient LLM security may lead to immediate losses due to discontinued services or downtime, jeopardising revenue streams.
Furthermore, recovering from a breach often requires substantial investment in system audits, improved infrastructure, and workforce retraining – diverting resources away from growth and innovation. Beyond direct financial losses, productivity is also hampered as teams handle crisis management instead of focusing on strategic objectives. LLM security is, therefore, necessary to safeguard a company’s assets and ensure smooth business operations.
Compliance risks
Organisations using LLMs must align with stringent regulatory frameworks, such as the EU’s AI Act, GDPR, or sector-specific compliance mandates. Failure to comply with these regulations can result in breaches where sensitive customer or business data is exposed, triggering severe penalties for non-compliance. For instance, under GDPR regulations, companies can be fined up to €20 million or 4% of their annual global turnover, whichever is higher, for non-compliance. LLM security, adhering to regulatory frameworks, not only protects customer data but also avoids costly legal consequences. With proper encryption, access controls, security measures and policies in place, it ensures that companies remain compliant in their LLM usage and operations.
Reputation damage
Implementing strong security measures in LLM use plays a crucial role in safeguarding your company’s reputation. Data breaches and security incidents can severely damage customer trust. When sensitive customer data is exposed due to inadequate security, businesses risk being perceived as negligent or untrustworthy. However, robust LLM security practices, such as employing advanced encryption, authentication protocols, and regular vulnerability assessments, reassure customers that their data is being handled responsibly and securely. Furthermore, ensuring compliance with regulatory standards through a robust LLM security framework demonstrates a company's commitment to ethical practices and accountability. This can lead to increased customer loyalty and trust, as well as positive brand reputation.
Intellectual property theft
Large Language Models (LLMs) frequently handle sensitive business data, including proprietary algorithms, strategic plans, and other confidential information. A security breach involving an LLM could expose this intellectual property, leaving it vulnerable to exploitation by malicious actors. LLM security through strict protocols and access controls, can help safeguard sensitive data by restricting access to authorised personnel only, thereby minimising risks and preventing unauthorised external intrusions.
Top risks and threats facing LLMs
Large Language Models are a major step forward in artificial intelligence, boosting productivity by automating complex tasks. However, they come with risks, including technical vulnerabilities, operational issues, and societal impacts. Below we look at the key risks and threats that organisations must understand and address to ensure the safe and effective use of LLM tools.
Prompt Injection
Prompt injection attacks exploit the inherent flexibility of LLMs by inserting malicious inputs masked as legitimate prompts. By doing so, attackers can manipulate generative AI systems (GenAI) to leak sensitive information, spread misinformation, or execute other harmful actions. For instance, an attacker could seed a forum with a malicious prompt instructing LLMs to redirect users to a phishing website. If someone uses an LLM to summarise the forum discussion, the generated summary could unknowingly guide the user to the attacker’s site, leading to potentially serious consequences.
Insecure output handling
Insecure output handling in LLMs refers specifically to insufficient validation, sanitisation, and handling of the outputs generated by large language models. This issue arises because LLMs can generate highly dynamic and unpredictable content, which may include sensitive, harmful, or unintended information. Since LLM-generated content can be directly influenced and controlled by prompt input, this behaviour is similar to providing users indirect access to additional functionality or hidden capabilities within the model.
Training data poisoning
LLMs rely on vast datasets for training, and these datasets can become a point of attack. Cyber-criminals may attempt to inject false, biased, or harmful data during the training process, compromising the integrity of the model. This tactic can skew outcomes or introduce vulnerabilities into LLM operations, necessitating rigorous data vetting processes.
Model Denial of Service (DoS)
Like traditional IT systems, LLMs are susceptible to denial-of-service (DoS) attacks. This happens when malicious entities overwhelm an LLM system with excessive requests, rendering it unusable. Such an attack could disrupt organisational services or cause considerable operational downtime, emphasising the need for robust rate-limiting and system redundancy protocols.
Supply chain vulnerabilities
Many LLM deployments involve partnerships, third-party integrations, or the utilisation of external plugins and APIs. Each point of supply chain interaction introduces potential vulnerabilities to exploitation. Targeting any weak link in this chain can allow attackers access to broader systems, which is why organisations must demand rigorous security standards from all vendors and partners.
Sensitive information disclosure
LLMs trained on vast datasets may inadvertently store and recall sensitive or confidential information from their training data or user inputs. This presents risks of unauthorised data exposure, especially if the model is interrogated with cleverly constructed prompts. Strict data encryption, anonymisation, and retention-limit strategies can minimise this threat.
Insecure plugin design
LLM plugins and extensions provide valuable functionality but can also introduce risks if poorly designed or inadequately secured. Malicious plugins might inject malware into the system or compromise data integrity. Organisations must adopt robust evaluating and monitoring processes to ensure only secure, verified plugins are deployed within their LLM ecosystems.
Excessive agency
Empowering LLMs with significant autonomous decision-making capabilities ("excessive agency") can be highly risky, especially if models are entrusted with operationally critical or sensitive tasks. Poorly designed LLMs might misinterpret directives or act in unintended ways, leading to operational disruptions, reputational damage, or even safety concerns.
Overreliance on LLMs
While LLMs are undeniably powerful, over-dependence can lead to complacency or the erosion of human oversight, especially in contexts that require judgment and accountability. Blind reliance on LLM systems can amplify errors or lead to poor decision-making. Human review and intervention remain essential for high-stakes deployments.
Model theft
The theft of a proprietary LLM model or its intellectual property not only results in financial losses but also poses risks of the model being used for unethical purposes by competitors or attackers. Protecting LLMs through intellectual property rights, encryption, and system access controls is essential to safeguard this critical technological asset.
Find out more about how OneAdvanced AI ensures security and compliance from our CTO Andrew Henderson: Andrew Henderson Discusses OneAdvanced AI | Technology Driving Change
Key components of a strong LLM security strategy
Data encryption and secure storage
Data encryption and secure storage are paramount to safeguarding sensitive information in the context of Large Language Models (LLMs). Encrypting both data at rest and in transit ensures that unauthorised individuals cannot access or exploit valuable datasets. Organisations should adopt robust encryption standards, such as AES-256, and implement secure storage solutions to protect datasets used in training and operation. Additionally, implementing encryption key management practices is critical, ensuring that access to keys is limited to authorised personnel. This approach minimises the risks posed by data breaches or interception, particularly where proprietary or sensitive data is involved in LLM workflows.
Access controls and identity management
Access controls and identity management protect LLM infrastructure from external threats and insider risks. Establishing strict access permissions ensures that only authorised users, applications, and services can interact with LLM systems and their underlying data. Implementing multi-factor authentication (MFA) and role-based access control (RBAC) mechanisms creates an additional layer of security, making it harder for attackers to gain unwarranted access. Identity management solutions, such as single sign-on (SSO) and federated identity services, streamline authentication processes while maintaining system integrity. By enforcing stringent access controls, organisations can significantly reduce the risk of unauthorised interference with their LLM technology.
Secure development practices for LLMs
To ensure the reliability and safety of LLM technologies, secure development practices must be integral to the lifecycle of these systems. This includes adopting a shift-left security approach, where vulnerabilities are identified and addressed early in the development process. Practices such as regular code reviews, automated security scanning, and penetration testing help uncover potential weaknesses before deployment. Integrating secure coding standards and frameworks tailored to AI and machine learning development is equally important to mitigate common vulnerabilities. Furthermore, organisations should prioritise the use of trusted libraries, maintain supply chain security, and establish secure update mechanisms to address emerging threats post-deployment. By embedding security measures into every phase of the LLM development lifecycle, organisations can build resilient and trustworthy applications.
Incident response planning for AI systems
Incident response planning is an essential component for mitigating the impact of security incidents on LLM systems. Organisations must develop a comprehensive AI-specific incident response plan that outlines clear protocols for identifying, containing, and resolving potential security breaches. This plan should include guidelines for assessing the scope of the incident, isolating affected components, and determining the root cause. Real-time monitoring tools should be in place to detect anomalies in system behaviour, and organisations must establish communication channels for timely response coordination. Furthermore, regular incident response drills and post-incident reviews are critical for refining and improving response strategies. A well-prepared incident response plan enables organisations to address threats effectively, minimising downtime and preserving the reputation and functionality of their LLM operations.
Best practices for mitigating LLM security risks
Encrypt data in transit and at rest
Encryption is a foundational security measure for safeguarding sensitive data. When data related to your Large Language Model (LLM) operations are encrypted both in transit and at rest, it significantly reduces the risk of unauthorised access or data breaches. Encryption in transit ensures that data moving between systems, such as during API requests or communications with databases, remains secure against interception by malicious actors. Meanwhile, encrypting data at rest protects stored information, whether on servers, databases, or backups, from unauthorised access.
Manage and control training data sources
The quality and security of training data play a crucial role in determining the performance and resilience of LLMs. To mitigate the risk of introducing biases or vulnerabilities, organisations must carefully manage and control their data sources. It’s essential to verify data authenticity, ensuring no untrusted or malicious datasets are included. By strictly limiting access to the data pipeline, implementing authentication mechanisms, and regularly auditing the data used for training, organisations can maintain the integrity of their LLMs. Furthermore, incorporating diverse, unbiased data helps avoid generating outputs that may be harmful or discriminatory.
Anonymise sensitive data during training
Protecting sensitive or personal data within training datasets is a vital step in mitigating privacy risks. Anonymisation ensures that identifying information is removed or replaced with non-sensitive equivalents, ensuring regulatory compliance and safeguarding user privacy. Having strict policies in place for data pre-processing and leveraging anonymisation frameworks reduces the risk of exposing confidential information through model outputs.
Perform regular vulnerability testing on LLM APIs
APIs provide the backbone for interacting with LLM systems, making them a prime target for attackers. Regular vulnerability testing is vital to uncover and address potential weaknesses within your API infrastructure. This includes conducting penetration tests, scanning for common vulnerabilities such as injection attacks or misconfigurations, and ensuring secure authentication methods are implemented. By adopting a proactive approach to API testing, organisations can identify and remediate flaws before they are exploited, reducing their exposure to cyber risks.
Monitor LLM systems continuously
Continuous monitoring is pivotal for maintaining a secure and reliable LLM environment. Implement systems capable of tracking user interactions, access logs, and system behaviour to detect anomalies in real-time. Using advanced monitoring tools, organisations can flag suspicious activity such as unusual data requests, unexpected usage patterns, or attempts to exploit model outputs. This proactive surveillance enables timely intervention and ensures compliance with security standards. Pairing monitoring systems with automated alert mechanisms ensures that potential risks are addressed promptly, maintaining the stability and security of LLM operations.
OneAdvanced AI – The UK’s first LLM-based tool
At OneAdvanced, we’ve developed OneAdvanced AI – a safe, trusted, and secure platform that leverages open-source large language model (LLM) technology. Hosted exclusively on our private platform in the UK, it ensures the highest standards of security and reliability.
OneAdvanced AI has been meticulously fine-tuned to meet the unique needs and preferred tone of voice of our customers for their organisational use. This customised approach guarantees outputs that are relevant, reliable, and context-sensitive, while upholding the exceptional quality our customers expect.
By securely hosting the model within the UK, we prioritise data sovereignty, privacy, and exclusive access for our regional customer base, offering peace of mind in a highly secure environment. Committed to delivering the most advanced generative AI solutions, we continuously evaluate emerging open-source LLM developments. This ensures we remain at the forefront of innovation, providing UK organisations with the most effective and optimised AI tools available.
Want to explore more about OneAdvanced AI and take next step to empower your business. Visit today!
Frequently Asked Questions (FAQs)
What makes LLM security different from traditional cybersecurity?
LLM security focuses on safeguarding model-specific vulnerabilities, such as prompt manipulation, data extraction attacks, or adversarial inputs, which are not typically a concern in traditional cybersecurity. It requires specialised monitoring, understanding of AI behaviours, and tailored safeguards that address the capabilities and risks unique to language models.
How often should LLM security assessments be conducted?
Security assessments for LLMs should ideally be conducted regularly, such as quarterly or biannually. However, they must also be performed after significant updates to the model, new integrations, or when new threats emerge, ensuring that the system remains resilient and up-to-date against evolving risks.
Are standard cybersecurity tools enough for protecting LLMs?
Standard cybersecurity tools alone are not sufficient for protecting LLMs. While they address general threats, safeguarding LLMs also requires specialised solutions that target model misuse, prompt injection, and other AI-specific risks, combining traditional tools with advanced AI-focused security practices.
How can businesses ensure compliance while using LLMs?
Businesses can ensure compliance by adhering to data protection laws, implementing transparent AI governance policies, and regularly auditing their use of LLMs. Employing proper access controls, documenting usage practices, and aligning operations with industry standards also help demonstrate compliance and build trust.