Large language model (LLM) security refers to the policies, controls, and technologies used to protect LLMs, their training and inference data, and the applications that rely on them. While conventional application security focuses on fixed code paths and known vulnerabilities, LLM security must consider how models adapt to context and interpret natural language.
LLMs are rapidly becoming a foundational part of enterprise digital transformation. LLM-powered applications such as chatbots, virtual assistants, document summarization, code generation, and decision support are accelerating productivity and innovation at scale. However, this rapid adoption has also introduced a new and complex security domain: LLM security.
Unlike traditional software, LLMs don’t follow fixed rules—they interpret natural language and adjust their responses based on context. This 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.
LLM security focuses on protecting data, models, and AI-enabled applications across their entire lifecycle. As businesses move from experimenting to production-scale deployments, understanding LLM security risks and adopting proven mitigation strategies is essential to safely realizing the value of generative AI.
LLM security refers to the practices, policies, and technologies used to protect LLMs and the applications that rely on them. LLMs respond to unstructured language inputs and generate outputs that can vary even for the same prompt, a property known as non-determinism, making them susceptible to manipulation and misuse.
In addition to added governance measures such as monitoring, auditing, and human oversight, LLM security spans four primary areas:
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, ensuring 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.
Enterprises are adopting LLMs at an unprecedented pace to improve efficiency, customer experience, and innovation. These systems often process sensitive data, generate externally visible content, or trigger downstream actions.
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. Many LLM security risks emerge not from traditional exploits, but from subtle misuse of prompts, context, or overly trusted responses.
For executives and security leaders, LLM safety is essential to managing enterprise risk. Strong LLM security practices enable organizations to scale AI adoption responsibly while protecting intellectual property, meeting regulatory obligations, and maintaining customer trust.
Large language models and applications have a very different risk profile from traditional applications. LLMs interpret natural language, generate probabilistic outputs, and often act as intermediaries between users, data, and systems, thereby exposing new attack surfaces that legacy security controls do not adequately address.
Frameworks like OWASP Top 10 for Large Language Model Applications emphasize that LLMs pose unique security risks when scaled. OWASP highlights vulnerabilities in how LLMs process language, integrate with external data sources, and interact with downstream systems, beyond traditional software exposure. 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.
Prompt injection occurs when an attacker crafts an input designed to override system instructions or manipulate a model's behavior. Attacks may be direct, through user prompts, or indirect, embedded in external content such as documents or web pages.
Because LLMs prioritize language understanding, prompt injection can bypass safeguards, expose sensitive data, or trigger unintended actions. Adversaries frequently use automation and bots (https://www.f5.com/company/blog/how-bots-attack-large-language-models-the-owasp-llm-top-10) to probe these weaknesses at scale, issuing high volumes of crafted prompts or requests to discover exploitable behaviors.
LLMs generate text, code, and commands that are often trusted too readily by downstream consuming systems. Insecure output handling occurs when applications directly execute or display model outputs without validation or sanitization.
This oversight can lead to injection vulnerabilities, cross-site scripting (XSS), unauthorized system commands, or the spread of inaccurate or unsafe information. Robust LLM application security requires treating model outputs as untrusted until verified.
LLMs rely on 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. Similarly, compromised open-source models or plugins introduce supply chain risk.
Ensuring large language model security means validating data sources, maintaining provenance, and continuously assessing model dependencies.
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.
Granting LLMs excessive independence, such as triggering workflows or decision-making without oversight, risks errors and abuse. Overreliance can spread poor information and flawed reasoning. In these cases, attackers could misuse LLMs for phishing, social engineering, or malicious code, heightening threats.
LLM endpoints are resource-intensive and expensive assets. Attackers can abuse them with heavy prompt loads that flood them with traffic or increase costs. Weak access controls may also enable model theft or unauthorized replication.
LLM use, from design and development through deployment and ongoing operations. 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.
The following mitigations represent a practical, enterprise-oriented approach to securing LLM-powered applications and align with emerging AI risk management frameworks, such as theNational Institute of Standards and Technology Artificial Intelligence Risk Management Framework (AI RMF 1.0), which emphasizes
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. These controls are foundational to reducing one of the most common LLM security risks.
LLM outputs should never be blindly trusted. Generated text, code, queries, or commands must be validated before being executed, stored, or shown to users. Without safeguards, outputs can create injection vulnerabilities, expose sensitive data, or cause unsafe actions.
Enterprises should apply content filtering, schema validation, and context-aware checks to ensure outputs meet safety and policy requirements. Treating model responses as untrusted data is a core principle of LLM application security.
Training and fine-tuning data, including data vetting, source control, and auditing, 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.
LLM applications often rely on third-party models, open-source libraries, plugins, and external APIs. Each dependency introduces potential supply chain risk. 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.
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. This limits the probability of compromised prompts or unintended model behavior.
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.
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, thereby providing a foundation for long-term 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.
LLMs should use well-defined APIs protected with the same security as high-value services. F5 enhances application delivery and security controls for LLM workloads, ensuring consistent enforcement across hybrid and multicloud environments.
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 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, indicating 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.
LLMs deliver significant business value, but they also introduce unfamiliar and evolving risks. Addressing LLM security requires more than point solutions; it demands a holistic strategy combining technical safeguards, governance, and human oversight.
By understanding common vulnerabilities and applying LLM security best practices, enterprises can confidently scale AI initiatives while protecting data, models, and applications. Organizations that prioritize the security of large language models will be better positioned to innovate safely and sustainably.
Prompt injection, data leakage, insecure output handling, training data poisoning, and excessive autonomy are common risks.
It is an attack in which crafted language inputs manipulate an LLM into bypassing safeguards or revealing sensitive information.
By applying layered controls such as input filtering, output validation, least-privilege access, and monitoring
