LLM security protects large language models from cyber threats, data breaches, and malicious attacks throughout their entire lifecycle. This discipline combines traditional cybersecurity practices with AI-specific protections to address unique vulnerabilities. The NIST AI Risk Management Framework notes that common security concerns relate to adversarial examples, data poisoning, and the exfiltration of models or training data—often referred to as model theft and data poisoning. For enterprises deploying LLMs at scale, implementing comprehensive security measures ensures AI systems deliver competitive advantages without compromising sensitive data or business operations.
LLM models, like GPT and other foundation models, come with significant risks if not properly secured. From prompt injection attacks to training data poisoning, the potential vulnerabilities are manifold and far-reaching.
That said, there are steps to take to make the most of LLMs without sacrificing security. In this article, we'll dive into the top risks posed by LLMs, the best practices for securing their deployment, and how tools like AI-SPM can help manage AI security at scale.
LLM Security Best Practices [Cheat Sheet]
This 7-page checklist offers practical, implementation-ready steps to guide you in securing LLMs across their lifecycle, mapped to real-world threats.
Top risks for LLM enterprise applications
LLM security challenges stem from the unique nature of AI systems that process vast amounts of data from diverse, often unknown sources. Unlike traditional applications, LLMs interact dynamically with users and external systems, creating an expansive attack surface.
The rapid pace of AI innovation means new vulnerabilities emerge faster than traditional security frameworks can adapt. Attackers continuously develop novel techniques like prompt injection and model extraction, while security teams struggle with limited AI-specific expertise. This creates a constant race between threat actors and defenders in an evolving landscape where conventional security measures often fall short.
OWASP Top 10 for LLM Applications provides the industry's authoritative framework for understanding and mitigating AI security risks. Developed by a global community with over 600 contributing experts, this framework identifies the most critical vulnerabilities threatening enterprise LLM deployments.
These risks represent real-world attack vectors that security teams encounter daily. Understanding each threat helps organizations prioritize their security investments and build comprehensive defense strategies tailored to AI-specific vulnerabilities.
Your Guide to Protecting Against OWASP's Top 10 LLM Risks
Watch on-demand as Wiz and guest Forrester share the latest AI research, why organizations are adopting AI-SPM (AI-Security Posture Management), and how you can secure AI workloads in the cloud today and protect against the top 10 LLM risks.
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1. Prompt injection
Prompt injection occurs when attackers craft malicious inputs designed to override an LLM's safety instructions or intended behavior. These attacks manipulate the model into ignoring its original programming, potentially causing it to leak sensitive information, execute unauthorized actions, or generate harmful content.
For enterprises, prompt injection can bypass security controls built into LLM applications. Attackers might trick a customer service chatbot into revealing confidential data or cause an AI assistant to provide instructions for illegal activities, creating compliance violations and reputational damage.
Example: An attacker might feed a chatbot a prompt that overrides its security logic, leading to leaked data or unauthorized actions.
2. Training data poisoning
The quality and trustworthiness of training data are foundational for LLM security. If attackers can insert malicious data into the training datasets, they can affect the entire model, leading to poor performance and compromised reliability.
Example: A recommendation engine trained on poisoned data could start promoting harmful or unethical products, undermining the integrity of the service and creating distrust among users.
3. Model theft
The competitive advantage of many enterprises lies in the proprietary models they build or fine-tune. If adversaries manage to steal these models, the company risks losing intellectual property, and in the worst-case scenario, facing competitive disadvantages.
Example: A cybercriminal exploits a vulnerability in your cloud service to steal your foundation model, which they then use to create a counterfeit AI application that undermines your business.
4. Insecure output
LLMs generate text outputs, which could expose sensitive information or enable security exploits like cross-site scripting (XSS) or even remote code execution.
Example: An LLM integrated with a customer support platform could use human-like malicious inputs to generate responses containing malicious scripts, which are then passed to a web application, enabling an attacker to exploit that system.
5. Adversarial attacks
Adversarial attacks involve tricking an LLM by feeding it specially crafted inputs that cause it to behave in unexpected ways. These attacks can compromise decision-making and system integrity, leading to unpredictable consequences in mission-critical applications.
Example: Manipulated inputs could cause a fraud-detection model to falsely classify fraudulent transactions as legitimate, resulting in financial losses.
6. Compliance violations
Whether dealing with GDPR or other privacy standards, violations can lead to significant legal and financial consequences. Ensuring LLM outputs don’t inadvertently breach data protection laws is a critical security concern.
Example: An LLM that generates responses without proper safeguards could leak personally identifiable information (PII) such as addresses or credit card details—and do so at a big scale.
Beyond these LLM-specific inherent risks, traditional threats like denial of service attacks, insecure plugins, and social engineering also pose significant challenges. Addressing these risks requires a comprehensive and forward-thinking security strategy for any enterprise deploying LLMs.
7. Supply chain vulnerabilities
LLM applications often rely on a complex web of third-party models, open-source libraries, and pre-trained components. A vulnerability in any part of this supply chain can introduce significant risk, allowing attackers to inject malicious code or compromise the integrity of the entire system.
Example: An attacker could publish a compromised version of a popular machine learning library that includes a backdoor, giving them access to any model that uses it.
Wiz AI-SPM extends supply chain visibility to AI models and dependencies, identifying risks in third-party frameworks and training datasets. By mapping the entire AI pipeline, Wiz helps you understand your exposure to vulnerabilities in the components you rely on.
8. Sensitive information disclosure
LLMs can inadvertently leak sensitive data, such as personally identifiable information (PII), intellectual property, or confidential business details, in their responses. This can happen if the model was trained on sensitive data without proper sanitization or if it is prompted to reveal information it has access to.
Example: A customer service chatbot could be tricked into revealing another user's account details or order history, leading to a major privacy breach and compliance violation.
Beyond these LLM-specific inherent risks, traditional threats like denial of service attacks, insecure plugins, and social engineering also pose significant challenges. Addressing these risks requires a comprehensive and forward-thinking security strategy for any enterprise deploying LLMs.
State of AI in the Cloud [2025]
LLM security is a top priority, but understanding the broader AI security landscape is key. Wiz’s State of AI Security Report 2025 provides insights into how organizations are securing LLMs and other AI models in the cloud.
Best practices for securing LLM deployments
Securing LLM deployments is not just about patching vulnerabilities as they arise—it requires a well-structured, organization-wide effort. LLM security should be part of a broader AI risk management strategy, integrating closely with a company’s existing security frameworks.
MITRE ATLAS serves as the authoritative knowledge base for understanding adversarial tactics against machine learning systems. This framework, developed by MITRE Corporation, maps real-world AI attack techniques to specific countermeasures. It documents over 130 techniques, 26 mitigations, and numerous case studies, providing security teams with actionable defense strategies.
Adversarial training/tuning
LLMs that are exposed to adversarial examples during training or tuning are better equipped to mitigate adversarial inputs and are more resilient to unexpected inputs.
Actionable steps
Regularly update the training sets with adversarial examples to ensure ongoing protection against new threats.
Deploy automated adversarial detection systems during model training to flag and handle harmful inputs in real time.
Test the model against novel attack strategies to ensure its defenses evolve alongside emerging adversarial techniques.
Use transfer learning to fine-tune models with adversarially robust datasets, allowing the LLM to generalize better in hostile environments.
Adversarial Robustness Toolbox (ART) and CleverHans are two interesting open-source projects to consider using to develop defenses against adversarial attacks.
Model evaluation
Conducting a thorough evaluation of your LLM in a wide variety of scenarios is the best way to uncover potential vulnerabilities and address security concerns before deployment.
Actionable steps
Conduct red team exercises (where security experts actively try to break the model) to simulate attacks.
Stress-test the LLM in operational environments, including edge cases and high-risk scenarios, to observe its real-world behavior.
Evaluate the LLM’s reaction to abnormal or borderline inputs, identifying any blind spots in the model’s response mechanisms.
Use benchmarking against standard adversarial attacks to compare your LLM's resilience with industry peers.
Input validation and sanitization
Validating and sanitizing all inputs reduces the risk of prompt injection attacks and feeding harmful data to the model.
Actionable steps
Enforce strict input validation mechanisms, ensuring that manipulated or harmful inputs are filtered before reaching the model.
Implement allowlists or blocklists to tightly control what types of inputs the model can process.
Set up dynamic input monitoring to detect anomalous input patterns that could signify an attack.
Use input fuzzing techniques to automatically test how the model reacts to unusual or unexpected inputs.
Content moderation and filtering
LLM outputs must be filtered to avoid generating harmful or inappropriate content and to ensure they comply with ethical standards and company values.
Actionable steps
Integrate content moderation tools that automatically scan for and block harmful or inappropriate outputs.
Define clear ethical guidelines and program them into the LLM’s decision-making process to ensure outputs align with your organization’s standards.
Audit generated outputs regularly to confirm they are not inadvertently harmful, biased, or in violation of compliance standards.
Establish a feedback loop where users can report harmful outputs, allowing for continuous improvement of content moderation policies.
Data integrity and provenance
Ensuring the integrity and trustworthiness of the data used in training and real-time inputs is key to preventing data poisoning attacks and ensuring customer trust.
Actionable steps
Verify the source of all training data to ensure it hasn’t been tampered with or manipulated.
Utilize data provenance tools to monitor the origins and changes of data sources, promoting transparency and accountability.
Employ cryptographic hashing or watermarking on training datasets to ensure they remain unaltered.
Implement real-time data integrity monitoring to alert on any suspicious changes in data flow or access during training.
Access control and authentication
Strong access control measures can prevent unauthorized access and model theft, making sure that users can access only the data they have permissions for.
Actionable steps
Limit access to resources according to user roles to ensure that only authorized individuals can engage with sensitive components of the LLM.
Implement multi-factor authentication (MFA) for accessing the model and its APIs, adding an additional layer of security.
Audit and log all access attempts, tracking access patterns and detecting anomalies or unauthorized activity.
Encrypt both model data and outputs to prevent data leakage during transmission or inference.
Use access tokens with expiration policies for external integrations, limiting prolonged unauthorized access.
Secure model deployment
Proper deployment of LLMs can significantly reduce risks such as remote code execution and ensure the integrity and confidentiality of the model and data.
Actionable steps
Isolate the LLM environment using containerization or sandboxing to limit its interaction with other critical systems.
Regularly patch both the model and underlying infrastructure to make sure that vulnerabilities are addressed promptly.
Conduct regular penetration testing on the deployed model to identify and mitigate potential weaknesses in its security posture.
Leverage runtime security tools that monitor the model’s behavior in production and flag anomalies that may indicate exploitation.
While these best practices focus on prevention, it's equally important to maintain a robust incident response process to address any security issues as they arise. Also, regular audits and assessments will keep your security strategy proactive, ensuring compliance and mitigating risks before they escalate.
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Protecting your LLM enterprise applications with Wiz AI-SPM
AI Security Posture Management (AI-SPM) provides continuous visibility and risk assessment for enterprise AI deployments, including LLMs, training data, and inference pipelines. This approach extends traditional security posture management to address AI-specific vulnerabilities that conventional tools cannot detect, as research shows certain manipulations can be difficult to detect through general-purpose performance evaluations.
Wiz AI-SPM addresses the unique challenge of securing dynamic AI systems that process sensitive data and interact with external systems. The platform delivers three essential capabilities that transform how enterprises protect their LLM investments.
Visibility through AI-BOMs: Wiz AI-SPM gives you a comprehensive view of your LLM pipeline, providing a bill of materials (BOM) for all AI assets in use. This visibility helps identify any potential vulnerabilities or risks associated with specific LLM deployments.
Risk assessment: By continuously analyzing LLM pipelines, Wiz AI-SPM assesses risks like adversarial attacks, model theft, or training data poisoning. It flags issues that could compromise security and gives them the right priority, ensuring organizations are aware of their risk exposure.
Proactive risk mitigation: Wiz goes beyond just flagging risks; it offers context-driven recommendations for mitigating them. For example, if a prompt injection attack were identified, the platform would provide insights on how to tighten input validation and secure the model from future attacks.
Real-world AI security risks often involve seemingly unrelated infrastructure vulnerabilities that create pathways to AI systems. Consider a common scenario: a development team deploys a web application container that inadvertently contains hardcoded API credentials for your organization's LLM services.
This exposed container becomes a backdoor to your AI infrastructure. Attackers discovering these credentials can manipulate your LLMs, extract training data, or use your AI resources for malicious purposes. Traditional security tools might detect the container vulnerability but miss the AI-specific risk it creates.
When alerting you to the exposed API key, Wiz provides both immediate actions (e.g., rotating the API key) and long-term mitigation strategies (e.g., locking down the exposed endpoint) to secure your deploymentーkeeping your LLM environment safe from potential breaches and service disruptions.
Next steps
LLM security is a complex but critical part of enterprise risk management. By understanding the top risks—like prompt injection, model theft, and adversarial attacks—and applying best practices—such as adversarial training, input validation, and secure model deployment—enterprises can secure their GenAI investments in the long term.
Wiz AI-SPM helps fast-track this process, giving organizations the tools they need to monitor, assess, and mitigate LLM security risks. Wiz also offers a direct OpenAI connector for bootstrapped ChatGPT security.
To learn more about how Wiz can enhance your AI security, visit Wiz for AI or schedule a live demo.
