What is LLM security?
Large language model (LLM) security refers to the policies, controls, and technologies used to protect foundational models, their underlying training and inference data, and the enterprise applications that rely on them. Large language model security covers the full lifecycle from how models are trained and fine-tuned to how they handle inputs and generate outputs in production.
Understanding LLM security
LLMs respond to unstructured natural language inputs and generate outputs that can vary even for the same prompt; this a property known as non-determinism. This non-deterministic property makes LLM security a critical concern for organizations that handle sensitive data, operate in regulated industries, or rely on AI-driven automation. Without proper safeguards, LLM applications can expose companies to data leakage, compliance failures, intellectual property loss, and novel attack techniques such as prompt injection.
Enterprises are adopting LLMs at an unprecedented pace to directly handle sensitive customer data, draft externally visible brand communications, and more. Without proper controls, LLMs can be exploited through language-based attacks, leak proprietary information, or produce unsafe outputs that lead to operational or reputational harm. Enterprises are deploying integrated tools built on base models into production faster than they're building controls around them. Additionally, the rise of agentic AI that executes actions, tool calls, and communication without explicit human direction has introduced a new, higher risk profile.
Why does LLM security matter?
Security is critical because LLMs influence decisions, automate tasks, and interact directly with users. LLM application security treats models as high-risk components. Safeguarding them requires continuous assessment rather than one-time testing; teams need to ensure models behave safely and predictably as usage evolves. Emerging regulations, such as the EU Artificial Intelligence Act, require organizations to implement risk management, logging, and human oversight for AI systems, making LLM security a compliance requirement rather than just a technical best practice.
In addition to added governance measures such as monitoring, auditing, and human oversight, LLM security spans four primary areas:
- Protecting training and inference data
- Preserving model integrity
- Controlling inputs and outputs
- Securing deployment environments and APIs
Protecting training and inference data
Training data poisoning and secure pipelines
LLMs learn from vast datasets and third-party components. If training or fine-tuning data is maliciously altered, models may learn biased behaviors, hidden backdoors, or unsafe responses. Training and fine-tuning data directly shape model behavior. Using unvetted or poorly governed datasets increases the risk of data poisoning, bias, and unintended memorization of sensitive information.
Best practices include sourcing data from trusted repositories, maintaining version control, documenting data lineage, and auditing changes over time. Strong data governance supports both LLM security and regulatory compliance. Ensuring large language model security means validating data sources, maintaining provenance, and continuously assessing model dependencies.
Sensitive information disclosure and data leakage
LLMs may unintentionally reveal sensitive or proprietary information learned during training or provided via prompts and context windows, including personally identifiable information (PII), credentials, or confidential business data. Preventing data leakage is central to LLM safety, especially in regulated environments where compliance violations carry severe consequences.
Preserving model integrity
Supply chain security, trusted components, and SBOM
LLM applications often rely on third-party models, open-source libraries, plugins, and external APIs. Each dependency brings in more potential supply chain risk. Compromised open-source models or plugins also introduce supply chain risk. Every external component used is another potential entry point for bad actors to get in.
Maintaining an inventory of components through a software bill of materials (SBOM), performing regular security reviews, and monitoring for vulnerabilities help organizations reduce exposure to compromised or outdated dependencies.
Controlling inputs and outputs: Direct and indirect prompt injection
Direct prompt injection attacks involve attackers explicitly crafting an input designed to override system instructions or manipulate a model's behavior through user prompts.
Indirect prompt injection occurs when an attacker embeds adversarial instructions within external content such as documents or web pages. LLMs are designed to follow instructions, and prompt injection exploits this directly by smuggling hard to distinguish instructions into the input system. Because LLMs prioritize language understanding, prompt injection can bypass safeguards, expose sensitive data, or trigger unintended actions.
To mitigate this, all inputs to LLMs, including user prompts, retrieved documents, system context, and metadata, should be carefully constrained and sanitized. Input surface control reduces exposure to prompt injection, indirect prompt attacks, and malicious instructions embedded in external content. Typical recommended practices include allow-listing trusted data sources, stripping hidden or non-printable instructions, limiting prompt length and complexity, and isolating user input from system prompts.
Insecure output handling and validation
Insecure output handling occurs when applications directly execute or display model outputs without validation or sanitization. If a model generates malicious code, bad commands, or misleading content, and the application blindly executes it, organizations can encounter XSS (cross-site scripting) attacks, unauthorized system commands, or users acting on false information.
LLM outputs should never be blindly trusted. Generated text, code, queries, or commands must be validated before being executed, stored, or shown to users. Enterprises should apply content filtering, schema validation, and context-aware checks to ensure outputs meet safety and policy requirements.
Oversight, output checks, and reliance limits
Despite advances in AI, human judgment remains essential. Overreliance on LLM outputs can increase errors, fabrication, or unsafe decisions. Enterprises should enforce human-in-the-loop reviews for sensitive use cases, provide training on appropriate LLM usage, and clearly define where people must validate automated decisions.
Securing deployment environments and APIs
Autonomy, overreliance, and least-privilege usage
Granting LLMs excessive independence, such as the ability to trigger workflows or make decisions without oversight, expands the risk of errors and abuse. LLM independence without a human in the loop can trickle into a series of unintended real-world consequences. Both LLM autonomy and overreliance are amplified with agentic AI. In these cases, attackers could misuse LLMs for phishing, social engineering, or malicious code, heightening threats.
To protect against this, LLM agents and plugins should operate under the principle of least privilege. Models should only have access to the data, tools, and actions necessary to perform their intended function. High-risk operations such as executing code, modifying records, or initiating transactions should require explicit approval or additional validation that’s defined and enforced by the infrastructure.
DOS attacks, resource exhaustion, and model theft
LLM endpoints are resource-intensive and expensive assets. Attackers can abuse them with heavy prompt loads that flood them with traffic and increase costs. Weak access controls may also enable model theft or unauthorized replication. Without structured and robust access controls/limits, an attacker can query a model and replicate its behavior without touching the backend. In denial-of-wallet attacks, malicious actors flood endpoints with token-heavy queries to rapidly consume an organization’s cloud or API budget without crashing the AI service.
Monitoring, logging, and incident response
Continuous monitoring of prompts, outputs, usage patterns, and performance enables early detection of abuse, anomalies, or emerging threats. Logging supports forensic analysis and compliance requirements. Regulations such as the EU AI Act mandate traceability and post-incident analysis for specific AI systems, making logging and monitoring essential for both compliance and security. Organizations should also establish incident response procedures specific to LLM-related events, ensuring rapid containment and remediation when issues arise.
Top LLM security and vulnerability risks
Large language models and applications have a very different risk profile from traditional applications. The OWASP Top 10 for LLM Applications focuses on AI-specific risks such as prompt injection, insecure output handling, training data poisoning, sensitive information disclosure, excessive agency, and model denial of service. Unlike the traditional OWASP Top 10, which focuses on web application vulnerabilities like broken access control and injection flaws, the LLM version addresses risks created by natural language inputs, model behavior, external tools, and AI autonomy. These threats include prompt injection, insecure output handling, training data poisoning, disclosure of sensitive information, excessive model autonomy, and denial of service via resource exhaustion. The framework emphasizes LLMs as high-impact components that require dedicated governance, ongoing monitoring, and multilayered protections aligned with modern application and API standards.
Recognizing these categories helps enterprises design targeted mitigations that align with modern application and API security practices and address the unique scope of LLM security.
LLM security best practices
Because LLMs interact through natural language, generate dynamic outputs, and often integrate with sensitive systems, traditional security controls alone are insufficient.
Effective LLM security best practices combine technical safeguards, governance processes, and human oversight. Organizations must assume that prompts can be manipulated, outputs can be misused, and dependencies can be compromised.
The goal is not to eliminate risk, but to reduce the likelihood and impact of failures while enabling safe innovation at scale.
Enterprises need continuous visibility into the risk composition of every AI model they adopt. This means continuously cross-referencing deployed models against up-to-date AI vulnerability libraries to understand what threats each model introduces into the environment. F5's Comprehensive AI Security Index (CASI) is one of the largest and most regularly updated AI vulnerability libraries available, giving organizations a consistent, up-to-date view of model-level risk.
How does F5 enable LLM security?
LLM-powered applications should be included in secure development practices, not treated as standalone systems. Like APIs and microservices, models should be versioned, access-controlled, tested, and deployed via governed continuous integration/continuous delivery (CI/CD) pipelines to keep changes traceable, auditable, and reversible. F5 enhances application delivery and security controls for LLM workloads, ensuring consistent enforcement across hybrid and multi-cloud environments.
The F5 AI Security Platform secures and governs AI models, apps, agents, and the APIs connecting them, with a continuous cycle of risk assessment and mitigation that keeps customers in command. From AI governance to AI runtime protection, the AI Security Platform supports AI observability from ingestion pipeline to output through F5 AI Guardrails, F5 AI Red Team, and more.
F5 BIG-IP and F5 Distributed Cloud Services help organizations secure LLM APIs with traffic management, access control, and inspection. Features such as API discovery, authentication, rate limiting, and bot mitigation protect LLM endpoints from abuse, DoS attacks, and automated attacks. These measures prevent resource exhaustion and block unauthorized access to costly inference services.
F5 Distributed Cloud Web App and API Protection (WAAP) improves LLM security by detecting injection attacks, malicious prompts, and unusual request patterns. It monitors traffic to spot abnormal activity, identifying prompt injection, data exfiltration, or misuse of LLM-powered system features.
F5 BIG-IP SSL Orchestrator allows decryption and inspection of encrypted traffic in environments with sensitive data or external systems, enabling security tools to detect hidden threats. This visibility is vital for monitoring LLM inputs and outputs in production without affecting performance.
Distributed Cloud Services offer centralized policy management, observability, and enforcement across hybrid and multicloud setups. Security teams gain consistent visibility into LLM traffic, anomalies, and metrics, allowing faster threat detection and response. Embedding LLMs into existing architectures prevents isolated AI silos. F5 enables scalable, resilient AI security that adapts to evolving business needs and threats, applying defense-in-depth principles to AI workloads.
LLM security: Frequently asked questions
What are the main risks associated with LLM security?
Prompt injection, data leakage, insecure output handling, training data poisoning, and excessive autonomy are common risks.
How can enterprises improve LLM security?
Enterprises can enhance LLM security by applying layered controls such as input filtering, output validation, least-privilege access, and monitoring.
How does the OWASP Top 10 for LLM Applications differ from the traditional OWASP Top 10 web risks?
The traditional OWASP Top 10 was designed for web application security and covers risks such as injection, broken authentication, and misconfiguration. The OWASP Top 10 for LLM Applications addresses natural language, non-deterministic, and non-traditional web risks (prompt injection, insecure output handling, training data poisoning, etc.).
Can LLMs be utilized for cyber defense?
Yes. LLMs are commonly used to automate threat analysis, analyze security logs, and expedite triage and response.