What is LLM Security?
LLM security involves protecting large language models and their systems from unauthorized access, misuse, and attacks by implementing protective measures throughout development, deployment, and operation. Mitigation strategies focus on input validation, data sanitization, access controls, and monitoring for both the model itself and its infrastructure.
Key security threats to LLMs include:
- Prompt injection: Attackers provide malicious inputs to override the LLM's original instructions and force it to generate unauthorized responses.
- Data leakage: Sensitive information from the training data can be revealed to users, or external data sources, like databases, can be compromised.
- Model denial-of-service: Attacks that overload the LLM, leading to a denial of service.
- Poisoning: Malicious data is intentionally fed into the model during training or fine-tuning to compromise its behavior.
- Jailbreaking: A type of prompt injection where attackers craft prompts that bypass built-in safety measures to generate harmful or inappropriate content.
This is part of a series of articles about AI security.
In this article:
As LLMs are integrated into production systems and customer-facing applications, ensuring their security becomes critical to maintaining trust, privacy, and system reliability. Security failures can expose sensitive data, mislead users, or allow adversarial control over application logic.
Key reasons why LLM security is essential:
- Safeguarding sensitive data: LLMs often process user data, internal documents, or confidential inputs. Without strong input/output controls, there's a risk of exposing this data in outputs or logs.
- Maintaining model and application integrity: Secure deployment ensures the model isn't tampered with or replaced. This protects against supply chain attacks or model substitution that could introduce backdoors or biased behavior.
- Preventing unauthorized access: LLM APIs and interfaces must be protected to avoid misuse. Without proper access controls, malicious users can overuse resources, exploit integrations, or extract model capabilities for abuse.
- Compliance with regulations: In regulated environments (e.g., healthcare, finance), improper handling of data or lack of auditability can result in violations. Security measures help meet legal and policy requirements.
- Ensuring trustworthy outputs: Filters and guardrails are needed to prevent toxic, misleading, or harmful content generation. This is essential to preserve user safety and brand reputation.
- Resilience against emerging threats: As attack techniques evolve (e.g., jailbreaks, model inversion), proactive security helps organizations adapt quickly and stay protected.
The Open Web Application Security Project (OWASP) released a list of top 10 vulnerabilities facing LLM and generative AI applications. Here are the top 10 vulnerabilities according to OWASP, in order of severity, updated as of 2025.
LLM01: Prompt Injection
Prompt injection exploits occur when attackers manipulate model inputs, called “prompts,” to make LLMs perform unintended actions or leak confidential data. These attacks are possible because LLMs lack a way to distinguish between benign and malicious instructions encoded in natural language. For example, attackers might craft a prompt that appears legitimate but subtly instructs the model to reveal internal configuration, bypass content filters, or even modify application logic in systems with downstream automation.
Prompt injection risk is heightened in applications relying on user-generated input, especially when such input is combined with sensitive retrieval operations or downstream API calls. Failures to sanitize or contextually filter user prompts create opportunities for attackers to undermine business logic or extract protected information. Security measures must include strict input validation, context isolation, and, when feasible, the separation of user prompts from privileged model instructions.
LLM02: Sensitive Information Disclosure
Sensitive information disclosure occurs when LLMs surface proprietary, private, or regulated data in their outputs, whether accidentally or via crafted queries. Since LLMs are trained or fine-tuned on large datasets that may contain confidential elements, poorly controlled systems can inadvertently “recall” and output training data, internal configurations, or context from prior model sessions. Disclosure can result from direct compromise or indirect influence, such as creative, multi-turn prompting.
This risk is compounded when models are accessed via public-facing interfaces or multi-tenant environments, where one user’s activity could affect downstream users. Data minimization during training, enforced context separation, and careful output filtering are all necessary. Robust privacy auditing and the exclusion of sensitive data from training and inference pipelines help minimize the likelihood of dangerous leaks via LLM-generated responses.
LLM03: Supply Chain Vulnerabilities
LLMs depend on complex supply chains, including open-source libraries, pretrained model checkpoints, cloud infrastructure, and third-party plugins. Each component introduces potential risks, such as malicious payloads in dependencies or compromise of model weights by upstream threats. Threat actors can target the supply chain to inject backdoors, insert vulnerabilities during model development, or alter the software environment hosting the LLM.
Tight controls and systematic vetting of third-party components are required to manage these risks. Adopting “zero trust” principles, maintaining hashes and provenance for model files, and using Software Bill of Materials (SBOMs) for dependencies help to ensure integrity. Continuous monitoring and patching are essential, as attacks may not immediately trigger visible failures but can be leveraged later through subtle misbehavior of LLM-powered systems.
LLM04: Data and Model Poisoning
Data poisoning involves feeding malicious or crafted samples into an LLM’s training or fine-tuning process with the goal of degrading accuracy, introducing bias, or enabling hidden behaviors upon specific input triggers. Model poisoning, similarly, targets the deployed model artifacts—at rest or in version control—to corrupt outputs or enable attacker-controlled backdoors. Both attack vectors can be subtle and hard to detect, leaving systems vulnerable to future exploitation.
Preventing data and model poisoning requires stringent validation and provenance checks at every stage of the machine learning pipeline. This includes authenticated source access, integrity verification, and continuous monitoring for anomalous model behavior. Defensive measures like differential privacy and secure data curation mitigate the risk, while incident response plans must be ready to rollback and safely recover from identified corruption or poisoning attempts.
LLM05: Improper Output Handling
Improper output handling arises when LLM responses are accepted and acted upon without scrutiny, interpretation, or filtering. Since LLMs can generate plausible but incorrect, biased, or offensive responses, unfiltered outputs can undermine applications, drive regulatory compliance failures, or trigger unsafe downstream actions. The danger escalates when LLMs are used as part of automated workflows—like customer service bots or software agents—where unchecked outputs influence real-world decisions.
Applications must implement robust validation and contextual review of all LLM outputs before they are exposed to users or other systems. This can involve automated content moderation, output reasonableness checks, and, where required, human-in-the-loop verification. Output guardrails not only protect end-users but also limit the chances of business logic corruption and reputational harm from unpredictable content generation.
LLM06: Excessive Agency
Excessive agency occurs when LLMs are given broad or unrestricted autonomy to interact with external systems, perform transactions, or make operational decisions. When LLMs have the authority to initiate actions beyond content generation—such as executing scripts, modifying databases, or invoking privileged APIs—they create new attack vectors. If attackers manipulate model prompts or contexts, they can exploit excessive privileges to compromise critical systems.
Mitigating this risk involves applying the principle of least privilege to all LLM interactions. Restricting capabilities via tightly-controlled action interfaces, explicit allow-listing of permitted operations, and continuous auditing all help to ensure LLMs operate within safe boundaries. Workflow segmentation and layered approval processes further reduce the risk of unintended or harmful model-driven actions, protecting both systems and data integrity.
LLM07: System Prompt Leakage
System prompt leakage refers to the unintended disclosure of hidden prompts or instructions used to steer LLM behavior. System prompts typically provide operating rules, constraints, or safety boundaries, and leaking them can enable attackers to reverse-engineer restrictions, bypass safety measures, or find vectors for further exploitation. Leakage may happen through direct query manipulation, prompt chaining, or configuration exposure via verbose outputs.
Defending against leakage requires keeping system instructions confidential and isolated from user-supplied contexts. Techniques include strict input/output filtering, session-based context separation, and limiting the granularity of model disclosures. Regular audits of LLM responses and simulated prompt attacks help detect and mitigate leakage before it can be weaponized by adversaries targeting embedded model logic.
LLM08: Vector and Embedding Weaknesses
Vector and embedding weaknesses involve vulnerabilities that stem from how LLMs process and represent text or data as mathematical vectors. Attackers may craft inputs that exploit model representation artifacts, leading to semantic manipulation, adversarial confusion, or information leakage from embedding spaces. Improper handling of embeddings can also introduce risks when used for search, classification, or downstream ML workflows.
Mitigating these vulnerabilities requires monitoring for model drift and embedding anomalies, and carefully curating embedding APIs exposed to users or services. Security-aware design of vector stores, including access control and audit trails, further limits abuse. Defenses like adversarial training and regular testing of embedding spaces help fortify applications against manipulation of model internals via deceptive vector inputs.
LLM09: Misinformation
LLMs can generate highly plausible, but entirely false or misleading statements, intentionally or otherwise. Misinformation output may lead users to make faulty decisions, propagate false narratives, or inadvertently share unverified data. This risk is especially significant in public-facing applications, news aggregation systems, and customer support bots, where trust in LLM responses may be high.
To counteract misinformation, it’s critical to implement strong controls like confidence scoring, cross-referencing with trusted sources, and contextual disclaimers. User education about LLM limitations, regular content accuracy audits, and escalation paths for correction improve reliability and help maintain user trust. Monitoring model outputs for trending or repeated errors also enables proactive containment of misinformation spread.
LLM10: Unbounded Consumption
Unbounded consumption describes scenarios where LLMs are exposed to requests that cause excessive resource usage, such as unregulated compute, storage, or bandwidth. Abuse of this nature can be accidental (e.g., repeated large queries) or intentional (e.g., denial-of-service attacks). Excessive consumption impacts availability, drives up operational costs, and may degrade service for legitimate users.
Defending against unbounded consumption requires rate limiting, resource quotas, and monitoring of usage patterns. Automated alerts for unusual spikes, together with graceful degradation or request rejection mechanisms, help ensure LLM operations remain sustainable. Resource usage contracts and capacity planning also play a key role in preventing abuse, aligning consumption to organizational priorities and service-level agreements.
Uri Dorot
Uri Dorot is a senior product marketing manager at Radware, specializing in application protection solutions, service and trends. With a deep understanding of the cyberthreat landscape, Uri helps bridge the gap between complex cybersecurity concepts and real-world outcomes.
Tips from the Expert:
In my experience, here are tips that can help you better secure LLM applications in 2026:
Apply “split-brain” design for high-value prompts: Architect LLM applications to use separate prompt channels for privileged instructions vs. user input. Physically and logically isolate internal model directives (e.g., system prompts) from user-facing query flows, reducing the chance of leakage or prompt injection through shared memory or context bleed.
Tokenize and detokenize PII before LLM exposure: Instead of relying solely on redaction or post-processing, tokenize sensitive information (e.g., names, IDs, financial data) before LLM access, and only detokenize output if needed downstream. This neutralizes data leakage risks even if the LLM misbehaves or is compromised.
Use honeypot prompts to detect manipulation attempts: Embed decoy instructions or trap prompts within LLM workflows that have no legitimate function but would only be triggered during prompt injection or jailbreaking attempts. Triggering these prompts should generate high-fidelity alerts for SOC teams to investigate.
Adopt zero-trust plugin architecture for LLM extensions: Many LLM systems interface with plugins or tools (e.g., databases, APIs). Apply zero-trust principles to each plugin: enforce identity, define tight scopes, sandbox interactions, and validate every response. Never assume plugin integrity; treat them as potentially hostile.
Perform latent threat simulation during fine-tuning: Before deploying any fine-tuned model, run adversarial simulations to test for latent behaviors or poisoning artifacts. Inject synthetic “trigger phrases” and monitor for anomalous completions, bias reactivation, or command injection—this is akin to penetration testing the model’s memory.
Many organizations are using dedicated AI / LLM security solutions that can protect against threats like the OWASP top 10. Here are common features of LLM security solutions.
1. Adversarial Input Protection
Defending LLMs against adversarial input means detecting and neutralizing attempts to exploit input prompts for malicious purposes. This includes both overt manipulations such as prompt injection, and more subtle adversarial attacks that try to bypass controls or trick the model into unsafe behavior. Techniques involve input normalization, intent analysis, and statistical anomaly detection to flag or block suspicious user-supplied instructions.
Automating this scrutiny across all entry points is crucial in preventing a wide range of attacks. Integrating adversarial input detection into the preprocessing pipeline ensures LLMs only engage with validated, safe content. Security layers at this stage also contribute to broader defenses against downstream vulnerabilities, making it harder for attackers to even reach core model logic.
2. Information Leak Protection
Information-leak protection features guard against the unintentional disclosure of sensitive or regulated data during LLM interactions. These can include preventing the output of personal, financial, proprietary, or training data artifacts, a risk amplified in multi-user and externally-facing settings. Automated redaction, real-time content scanning, and output filtering are foundational ways to limit leakage.
Security platforms may also embed data fingerprinting and context-aware privacy controls, which adapt to usage context and user privileges. Combining technical safeguards with policy enforcement ensures LLMs remain compliant with legal and ethical standards, reducing data breach and compliance exposure while still supporting productive model-based workflows.
3. Monitoring, Observability and Real-Time Anomaly Detection
Comprehensive monitoring and observability capabilities are necessary to track LLM activity, performance, and unexpected behaviors. Security solutions should provide real-time visibility into transaction patterns, output characteristics, and system health, with automated detection of anomalies that might indicate attacks or malfunction. This includes alerting for abnormal spikes, abnormal feedback loops, or atypical user interactions.
The feedback from continuous monitoring enables fast incident response, root cause analysis, and iterative system improvement. Integrating observability with operational dashboards and security event management systems ensures all stakeholders, from security teams to application owners, are equipped to identify, investigate, and mitigate emerging threats in LLM environments.
4. Access Control, Identity and Permissions for LLM Components
Granular access control for LLM systems enforces who can interact with models, adjust configurations, or retrieve outputs, often down to the API, function, or data level. Security solutions should integrate with organizational identity management to map user roles and define clear permission boundaries across all LLM components, including data sources, plugin integrations, and orchestration layers.
Robust key management, fine-grained access logs, and just-in-time authorization reduce risk of credential misuse or privilege escalation. These practices help ensure only authorized actions are permitted, limit the blast radius of compromised credentials, and create a clear audit trail to support investigations or compliance requirements.
5. Output Validation and Guardrails
Output validation and guardrails are mechanisms that review and constrain LLM-generated content before it is delivered to users or other systems. Typical controls involve content moderation, toxicity checks, length and format constraints, and logic validation (where outputs drive downstream automation). These guardrails are critical for maintaining safety, regulatory compliance, and user trust.
Effective solutions combine both automated and human-in-the-loop output review, adapting checks to the sensitivity and use-case of the LLM deployment. By systematically rejecting or flagging problematic outputs, organizations reduce liability and avoid harmful or misleading results entering critical workflows.
6. Model and Infrastructure Hardening
Model and infrastructure hardening includes all actions taken to make the underlying LLM and its operational environment more resilient to exploitation. This encompasses patch management, attack surface reduction, secure configuration, and hardware-level protections. Encryption, container isolation, and regular vulnerability scanning are baseline requirements.
Specialized hardening practices such as limiting model introspection, sandboxing execution environments, and enforcing secure API gateways, add further layers of isolation and defense. Continual hardening ensures that LLM platforms evolve alongside the threat landscape, proactively addressing both known and emergent risks in production environments.
Here are a few things your organization can do to improve LLM security.
1. Encrypt Data in Transit and at Rest
Encrypting data in transit and at rest protects sensitive information from interception, unauthorized access, or tampering as it moves between systems or is stored in persistent locations. This practice involves leveraging protocols such as TLS for network communications and strong cryptographic algorithms for storage. By making sure that all data input to or output from the LLM is encrypted, organizations mitigate the risk of eavesdropping and data theft across internal and external boundaries.
Security solutions should enforce encryption defaults across all LLM components, including application servers, databases, and external APIs. Automated key rotation, audit trails for cryptographic events, and regular compliance checks help maintain trust in the confidentiality of model-driven services. Consistent encryption practices are foundational for meeting regulatory obligations and upholding organizational privacy standards in AI-enabled workflows.
2. Output Filtering and Moderation
Output filtering and moderation ensure that LLM-generated responses align with safety, compliance, and quality standards before being presented to users or downstream systems. Since LLMs can produce harmful, biased, or otherwise inappropriate content, implementing a structured output review process is essential to mitigate reputational, legal, or operational risk.
Techniques include automated content scanning for toxicity, hate speech, personally identifiable information (PII), and regulatory violations (e.g., HIPAA, GDPR). Filters can be rule-based, ML-driven, or a hybrid approach, depending on the deployment’s sensitivity. For higher-risk use cases, human-in-the-loop moderation may be required to review flagged outputs before release.
Context-aware filtering—tuned to user roles, application domains, or real-time usage context—further improves accuracy. Systems should also log moderated outputs and actions for auditability and continuous tuning of moderation criteria. These safeguards prevent unsafe outputs from reaching users and help build trust in LLM-powered services.
3. Least Privilege and Access Control
Applying the principle of least privilege limits what users, services, and LLM components can access or perform within a system. This reduces the attack surface by ensuring that no entity—human or automated—has more permissions than absolutely necessary for its function.
This applies to API access, data retrieval, plugin invocation, model configuration, and prompt injection points. Role-based access control (RBAC), attribute-based access control (ABAC), and integration with identity providers (e.g., SSO, OAuth) enable granular enforcement. LLM applications should validate permissions at every layer, from data sources to output consumers.
Least privilege also applies to internal service accounts and LLM agents. These should have scoped tokens or credentials, limited-duration access, and isolation between environments (e.g., dev, test, prod). Tight access boundaries help contain compromise, reduce misuse risk, and simplify audits.
4. Rate Limiting and DoS Protection
Rate limiting protects LLM services from abuse, whether intentional (e.g., denial-of-service attacks) or accidental (e.g., overuse by misconfigured clients). By restricting the number of allowed requests per user, IP, or application over time, systems remain available and responsive for legitimate users.
Security controls should include tiered rate limits based on user trust levels, API keys, or endpoint sensitivity. Adaptive throttling—where limits adjust dynamically based on system load or anomaly detection—can offer additional protection without harming user experience.
Defenses should also include input size limits, request timeouts, and memory/compute quotas to prevent resource exhaustion. Monitoring request patterns and triggering alerts for spikes or repeated failures adds an early warning system against emerging abuse patterns.
5. Logging, Monitoring, and Anomaly Detection
Logging and monitoring are critical for maintaining visibility into LLM operations, detecting attacks, and supporting incident response. All interactions with the LLM, including prompts, outputs, configuration changes, and system events, should be logged in a tamper-resistant and queryable format.
Logs should include context such as timestamps, user identity, session metadata, and response metadata (e.g., generation time, token count, confidence scores). Integration with SIEM tools and alerting platforms enables real-time detection of unusual behavior, such as prompt injection attempts, repeated error patterns, or unauthorized access.
Anomaly detection systems should flag deviations in usage patterns, model outputs, or performance metrics. These might include novel input structures, unexpectedly toxic responses, or unexplained shifts in API call volumes. Automated response workflows can isolate affected systems or escalate for investigation, allowing fast containment of threats.
As large language models move into production environments, security must address more than basic access control. Organizations must defend against prompt injection, data leakage, unsafe tool use, model abuse, and availability risks across the full AI workflow. Radware delivers purpose-built protection for both LLM deployments and emerging agentic AI systems.
Radware LLM Firewall protects LLM applications from prompt injection, system prompt leakage, sensitive information disclosure, and adversarial manipulation aligned with the OWASP Top 10 for LLM Applications. It inspects inbound prompts and outbound responses in real time, enforcing policy guardrails, detecting jailbreak attempts, and preventing unauthorized data exposure before it leaves the model boundary. This enables secure AI interactions without degrading usability.
For autonomous and tool-using AI systems, Radware Agentic AI Protection provides runtime governance over AI agents. It validates agent intent against defined policies, monitors API calls and chained workflows, enforces access controls, and prevents excessive agency or unauthorized actions. This is critical for mitigating risks such as unsafe plugin execution, improper output handling, and unbounded consumption.
To further secure the environments that expose LLM functionality, Radware Cloud Application Protection Service provides web application firewall, API security, bot management, and application-layer DDoS protection. This ensures that AI-enabled applications are protected against injection attempts, automated abuse, and API exploitation at the application edge.
For availability and resilience, Radware Cloud DDoS Protection Service safeguards LLM endpoints and AI-driven APIs against volumetric and multi-vector attacks that could otherwise disrupt model access or exhaust compute resources.
Together, these capabilities create a layered LLM security architecture that combines adversarial input detection, output validation, agent governance, application-layer protection, and DDoS resilience. This unified approach enables organizations to implement encryption, strict access controls, secure API integrations, and structured incident response, while safely scaling AI and agentic systems across production environments.