Large language models (LLMs) have become a mature and valuable tool in the modern enterprise, powering AI-driven applications, content generation, intelligent assistants, and more.

However, LLM security concerns have also increased as more organizations embrace AI in the workplace. This means LLM data security must be addressed at a grassroots level. Private or sensitive data needs to be protected for regulatory compliance and to maintain employees’ trust in the systems they use every day, and customers’ trust in how the business handles data.

Without robust cybersecurity, LLM systems can fall foul of a number of vulnerabilities. But, with the right strategy and approach, these common LLM security threats can be mitigated.

What is LLM security?

When we say LLM security, we mean the entire ecosystem of best practices, strategies, methods, and technologies used to protect LLMs from exploitation—either accidental or malicious.

These AI security needs can include ensuring the ongoing integrity of models, the data they process, and the outputs they generate. The process involves not only technical fixes, but also operational practices and workflow considerations to ensure security standards are met.

Securing LLMs can help organizations prevent unauthorized access, stop data leaks, and prevent the malicious manipulation of models. Addressing these AI security risks is a vital element of fostering trust with stakeholders and users. It shows the business is taking steps to mitigate the risk of harm.

Capable LLM security tools and robust security practices also align with the question of the responsible use of AI. And, of course, in industries where compliance is a big issue, LLM security is a requirement of doing business.

Why is LLM security important?

Ensuring the privacy and security of LLMs is crucial as these models handle vast amounts of data—often including sensitive information. This is especially true for industries dealing with confidential data, such as healthcare records, financial transactions, or proprietary business information.

LLMs are increasingly used to process sensitive or private data, perform tasks that aid decision-making, and produce critical content such as summarizing research data. Neglecting LLM security risks could have far-reaching consequences, such as:

Loss of trust. In some cases, stakeholders and users may suffer a loss of confidence in systems and workflows driven by LLMs. Trust can be eroded if users find themselves facing security issues, or they start receiving unreliable responses from an LLM. For an enterprise that has invested in a project to deploy LLM applications safely, this loss of trust could be a significant and expensive setback.
Access or exploitation by malicious actors or cybercriminals. There are some well-documented security vulnerabilities that hackers or cybercriminals have already used to exploit LLMs—for example, prompt injection being used to manipulate the behavior of a model. These kinds of vulnerabilities could result in the bypassing of model parameters to generate malicious code or spread disinformation.
Incorrect or plainly harmful responses. If trained, fine-tuned, or configured incorrectly, LLMs can produce incorrect responses such as biased or harmful content, or presenting misleading information. And when businesses are relying on the LLM to securely and reliably present data to inform strategic decisions, this can have wide-reaching consequences. Ensuring the integrity of a model is a vital part of the LLM security process.
Actual data breaches. LLMs can contain sensitive information as part of their corpus of knowledge, or they may have access to personal data such as customer information or patient records. It’s vital to ensure tight control of both access to the model and also the way that responses are used. Unauthorized access or dissemination of sensitive data could result in privacy violations, and could see the organization failing in its compliance efforts— risking the regulatory fines or legal challenges.

Every organization must consider LLM security training—not only from a technical standpoint, but also a process and workflow point of view—to ensure that AI is used in a safe and responsible manner.

Comprehensive AI security measures show an organization is not only mitigating potential LLM security concerns, but also that it is committed to ensuring AI systems are used safely. This helps to build trust, and can be a step towards getting the most out of AI in the enterprise.

LLM security risks: OWASP’s 10 top LLM security vulnerabilities

To help enterprises to keep AI secure, the Open Web Application Security Project (OWASP) has published a list of 10 potential security vulnerabilities that organizations must consider when deploying LLMs. Be sure to consider how you might mitigate these potential vulnerabilities when designing and deploying your own enterprise AI.

Prompt injection

A prompt injection attack can take place when an attacker manages to manipulate the input prompt in a way that directly impacts how the LLM behaves. For example, a malicious agent may be able to find a way to embed instructions within a prompt that can bypass security measures, resulting in undesirable responses.

Mitigate the risk of prompt injection by:

Implementing a capable and robust method for sanitizing input and validating prompts.
Setting up standard prompt formats that can be restricted through a user-based permission system.
Proactively monitoring responses to make sure they are accurate and unbiased.

Sensitive information disclosure

Any LLM that was trained or fine-tuned using confidential datasets could inadvertently expose this information. Mitigate for sensitive information disclosure by:

Avoiding training or fine-tuning a model using sensitive or private data. Where applicable, synthetic data can be used instead.
Safeguarding training data by implementing differential privacy techniques, which consist in adding small tweaks to data in order to protect personal information.
Monitoring and auditing the LLM’s responses to make sure that no sensitive or private information is being disclosed.

Supply chain vulnerabilities

Organizations that have incorporated any third-party models, libraries, or datasets are at risk of vulnerabilities in the supplied products and services. Mitigate supply chain vulnerabilities by:

Vetting any third-party vendor, only selecting those that have demonstrated a suitable level of trustability.
Not taking external components at face value. Perform internal proving and code reviews of all external elements.
Keeping a finger on the pulse of third-party platform updates to make sure they do not introduce any security vulnerabilities.

Data and model poisoning

Data poisoning can take place when an attacker manages to insert malicious data into a training dataset; this modified dataset will have a direct impact on the integrity of the model. Model poisoning takes place when an attacker is able to tamper with the parameters of a model, altering its behavior.

How to mitigate data and model poisoning:

Be sure to curate training datasets and then vet them to ensure they contain no suspicious-looking data.
Implement a model version control system so that unauthorized changes can be spotted much more easily.
Put a system in place for reverting a model to a previous state if it’s found to have been compromised by data or model poisoning.

Improper output handling

Without some sort of LLM security scanner in place, there is a risk that an LLM could produce responses that are misleading, harmful, inaccurate, offensive, or that breach compliance protocols.

How to mitigate improper output handling:

LLM security testing needs to take place after responses have been generated to validate outputs and filter out anything inappropriate.
Implement some sort of human-in-the-loop system that will proactively review and evaluate specific high-stakes responses.
LLM prompts can be iteratively refined to help minimize the chance of harmful responses.

Excessive agency

Granting an LLM too much autonomy could result in unintended outcomes and consequences—it could, for example, make inappropriate automated decisions or execute actions that may result in harm. This is an especially dangerous vulnerability in sectors where lack of human oversight could result in catastrophic damage—military use of AI, for example.

Mitigate excessive agency by:

Ensuring that the scope of tasks assigned to an LLM are limited to just those that are needed.
Setting up a workflow that includes approval for critical actions and introduces general safeguards on actions the LLM can take.
Setting up regular reviews or auditing of LLM interactions to make sure they always comply with the intended outcome and use case.

System prompt leakage

System prompts pass operational instructions to LLMs. If these are accidentally exposed to actual users or maliciously targeted by attackers, they put the model at risk of exploitation.

How to mitigate system prompt leaks:

Hide system prompts by embedding them directly into the application logic.
Monitor user-facing interfaces to the LLM and make sure to redact prompt disclosures where found.
Encrypt system prompts where possible to protect them from unauthorized access.

Vector and embedding weaknesses

Some adversarial attacks specifically target the vectors and embeddings components of an AI-driven application. Mitigate for vector and embedding weaknesses by:

Using comprehensive and robust adversarial training methods and techniques.
Validating any embeddings against all known attack vectors.
Monitoring and auditing the behavior of embeddings to spot potential manipulations.

Misinformation

In some cases, an LLM may generate false or misleading information. This is a particularly relevant AI safety risk when a model is asked for factual answers to a very specific question.

How to mitigate misinformation:

Put limits in place so that an LLM can only produce responses based on knowledge taken from trusted and validated sources.
Implement a process for monitoring and fact-checking responses.
Where a model’s invalid responses may be harmful, provide users with a disclaimer telling them to check responses for accuracy.
Optimize the LLM with retrieval-augmented generation (RAG) and inline citations, which can improve accuracy and make it easier to check the sources of outputs.

Unbounded consumption

When LLMs are allowed to consume resources freely—including memory and computing power—there can be some serious ramifications. At extremes, this could result in something similar to a denial of service (DoS) attack, although produced internally. Whenever an LLM takes too many resources, it could lead to degraded system performance, and, of course, increased costs.

How to mitigate unbounded consumption:

Decide and enact on acceptable resource quotas for the LLM.
Monitor how resources are used and the impact on system performance in real-time.
Consider implementing a secure and scalable enterprise AI solution that will ensure adequate resources are available when needed, automatically scaling these back when not needed.

Understanding these 10 key security vulnerabilities identified by OWASP is critical for organizations looking to deploy enterprise AI. The smart combination of LLM security tools and comprehensive security-focused AI working practices can help to reduce LLM security risks, build trust, and help an organization get the best from AI.

LLM security best practices

From these LLM security tips and best practices, enterprise security teams can build out a comprehensive set of AI security processes and protocols.

Limit access: As part of hardening an LLM for cybersecurity purposes, or to prevent accidental unauthorized use, consider which job roles need to perform which LLM-driven tasks. The findings can then form part of a rules-based or role-based access system.
Audit regularly: It’s vital to monitor LLM configurations, outputs, system prompts, and security measures. In some cases, this will mean real-time monitoring; in others, simply a regular review. This helps to ensure that current LLM security practices are working while highlighting any potential emerging vulnerabilities.
Be transparent: LLM security processes should be transparent, including documentation. Known risks and the steps taken to mitigate them need to be readily available to stakeholders; this keeps them informed and empowers them to take a more proactive role in LLM security.
Consider automating, where it’s safe to do so: Real-time tools such as LLM security scanners can take some of the weight when it comes to monitoring security. Proactive monitoring—even automated monitoring—can help to highlight new threats as they occur, and also help to lessen the harm caused by a potentially damaging security breach.
Promote ethical AI usage: When it comes to data privacy, protecting sensitive data, and remaining compliant with legal requirements, a good first step is to promote ethical AI usage within the organization. Each business unit should highlight the way it wishes to use AI, plus any pain points, and put in place robust processes to remove those challenges. Users should be trained on, or at least informed of, what the company's ethical use policy is; the company should audit adherence to this policy where appropriate.
Invest in workforce skills: Technical staff within the organization may need additional LLM security training to be able to continue fulfilling their roles. Similarly, actual users may require additional training so that they know how to use the AI tools safely. In the case of a skills gap, consider buying-in external knowledge.

A robust LLM security system needs both technological solutions and softer solutions based around staff awareness and training.

Working to ensure LLM security is not just about mitigating risks; it’s also about unlocking the full potential of these transformative technologies. By addressing vulnerabilities proactively, organizations can build confidence in their AI deployments and drive innovation responsibly.

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LLM security risks and how to mitigate them