OWASP Top 10 for LLM Applications 2025.

Jul 14, 2025

OWASP Top 10 for LLM Applications 2025: The Essential Security Framework for AI Engineers and DevSecOps Teams

Introduction

Large Language Model applications have rapidly evolved from experimental tools to production-critical systems handling sensitive data and executing business logic. However, the unique characteristics of LLMs — their probabilistic nature, natural language interfaces, and complex training pipelines — introduce attack vectors that traditional security frameworks fail to address adequately.

The OWASP Top 10 for LLM Applications 2025 represents a significant evolution from the 2023 baseline, incorporating lessons learned from production incidents, emerging threat patterns, and the maturation of LLM deployment architectures. This framework addresses critical gaps in traditional security approaches, providing actionable guidance for securing systems that process unstructured natural language inputs and generate dynamic outputs.

Unlike conventional web applications where input validation follows predictable patterns, LLM applications must handle adversarial inputs designed to exploit the model’s training and inference mechanisms. The probabilistic nature of neural networks makes deterministic security controls challenging to implement, requiring new approaches that balance security with functional requirements.

The 2025 update reflects significant architectural shifts in LLM deployments, including the widespread adoption of Retrieval-Augmented Generation (RAG) systems, multi-agent architectures, and edge deployment scenarios. These developments have expanded the attack surface considerably, necessitating specialized security controls for vector databases, embedding systems, and distributed AI inference.

https://genai.owasp.org/llm-top-10/
https://owasp.org/www-project-top-10-for-large-language-model-applications/

Architectural Context and Threat Landscape

LLM Application Architecture Overview

Modern LLM applications typically implement a multi-layered architecture comprising user interfaces, API gateways, orchestration layers, model inference engines, and data retrieval systems. Each layer introduces specific security considerations that must be addressed holistically.

The orchestration layer often manages complex workflows involving multiple model calls, tool invocations, and data retrieval operations. This layer presents unique challenges for access control and audit logging, particularly when implementing agentic systems that make autonomous decisions about tool usage and data access.

Vector databases and embedding systems have become critical components in RAG architectures, creating new attack surfaces related to similarity search algorithms, embedding space manipulation, and cross-tenant data isolation. These systems require specialized security controls that differ significantly from traditional database security approaches.

Emerging Threat Patterns

The threat landscape for LLM applications has evolved to include sophisticated prompt injection campaigns, model extraction attacks using API access patterns, and supply chain compromises targeting fine-tuning datasets and model repositories. Adversaries have developed techniques that exploit the semantic understanding capabilities of LLMs to bypass traditional input validation mechanisms.

Advanced persistent threats (APTs) have begun incorporating LLM-specific attack techniques into their playbooks, including the use of steganographic prompt injections and adversarial examples designed to trigger specific model behaviors. These attacks often combine multiple vulnerability classes to achieve their objectives.

The 2025 Top 10 Vulnerabilities according to OWASP

  • LLM01: Prompt Injection - Advanced Attack Vectors and Defenses
  • LLM02: Sensitive Information Disclosure - Data Leakage Prevention
  • LLM03: Supply Chain Vulnerabilities - Securing the AI Pipeline
  • LLM04: Data and Model Poisoning - Integrity Assurance
  • LLM05: Improper Output Handling - Securing AI-Generated Content
  • LLM06: Excessive Agency - Controlling Autonomous AI Systems
  • LLM07: System Prompt Leakage - Protecting Configuration Data
  • LLM08: Vector and Embedding Weaknesses - Securing RAG Systems
  • LLM09: Misinformation - Technical Approaches to AI Reliability
  • LLM10: Unbounded Consumption - Resource Management and Rate Limiting

LLM01: Prompt Injection - Advanced Attack Vectors and Defenses

Prompt injection represents the most fundamental security challenge in LLM applications, exploiting the models’ inability to reliably distinguish between instructions and data. The vulnerability manifests in multiple forms, each requiring specific defensive measures.

Direct Prompt Injection Techniques: Advanced attackers employ techniques such as payload splitting, where malicious instructions are distributed across multiple inputs to evade detection systems. Token-level attacks exploit the model’s tokenization process, using carefully crafted character sequences that alter semantic meaning during tokenization.

Context window manipulation attacks leverage the model’s attention mechanisms to prioritize malicious instructions over legitimate system prompts. These attacks often use techniques borrowed from adversarial machine learning research, including gradient-based optimization to find effective prompt modifications.

Indirect Prompt Injection Vectors: Indirect injections through external data sources represent a particularly insidious attack vector. Attackers can embed malicious instructions in documents, web pages, or other content that the LLM processes as part of RAG operations. These instructions may use techniques such as:

  • Markdown injection with hidden formatting that affects model interpretation
  • Unicode manipulation to hide instructions from human reviewers
  • Semantic camouflage using contextually appropriate language that contains hidden commands

Technical Mitigation Strategies:

  • Implement input sanitization using semantic analysis rather than pattern matching.
  • Deploy multiple model architectures in a dual-LLM pattern where one model validates the outputs of another.
  • Use constitutional AI techniques to train models that resist instruction-following when inputs appear to contain prompt injections.

Implement runtime monitoring using embedding similarity analysis to detect inputs that deviate significantly from expected usage patterns. Deploy canary tokens within system prompts to detect extraction attempts and automatically trigger security responses.

LLM02: Sensitive Information Disclosure - Data Leakage Prevention

Sensitive information disclosure in LLM contexts extends beyond traditional data exfiltration to include model inversion attacks, training data extraction, and inadvertent exposure of system architecture details through model responses.

Technical Attack Mechanisms: Model inversion attacks use carefully crafted queries to extract specific information from training data. These attacks exploit the model’s tendency to memorize rather than generalize certain types of information, particularly personal identifiers and structured data formats.

Adversaries may employ techniques such as:

  • Iterative querying with increasing specificity to extract personal information
  • Template-based attacks that exploit the model’s familiarity with common data formats
  • Gradient-based extraction methods when model internals are accessible

Advanced Prevention Techniques:

  • Implement differential privacy mechanisms during training to limit memorization of individual data points.
  • Use techniques such as DP-SGD (Differentially Private Stochastic Gradient Descent) with carefully tuned noise parameters to maintain utility while protecting privacy.

Deploy post-processing filters using named entity recognition (NER) and regular expressions to detect and redact sensitive information in model outputs. Implement semantic analysis to identify potential information leakage patterns that may not match traditional regex patterns.

LLM03: Supply Chain Vulnerabilities - Securing the AI Pipeline

LLM supply chains present unique security challenges due to the complexity of model development pipelines, the prevalence of pre-trained models, and the emergence of collaborative model development platforms.

Supply Chain Attack Vectors: Model poisoning attacks target the training pipeline by introducing malicious data or compromising the training process itself. These attacks may involve:

  • Dataset poisoning through contributed training data
  • Backdoor insertion during fine-tuning processes
  • Compromise of model repositories and distribution channels
  • Malicious LoRA adapters that modify model behavior when loaded

Technical Security Controls:

  • Implement cryptographic verification for all model artifacts using digital signatures and hash verification.
  • Establish a model bill of materials (MBOM) tracking system that maintains provenance information for all model components, including base models, fine-tuning data, and adapter modules.

Deploy automated scanning systems for model repositories that can detect potential backdoors or anomalous model behaviors. Use techniques such as neural cleanse and other backdoor detection algorithms to identify compromised models before deployment.

LLM04: Data and Model Poisoning - Integrity Assurance

Data and model poisoning attacks target the fundamental integrity of LLM systems by corrupting training processes or introducing malicious behaviors that activate under specific conditions.

Advanced Poisoning Techniques: Modern poisoning attacks use sophisticated techniques such as:

  • Gradient matching to ensure poisoned samples integrate seamlessly with legitimate training data
  • Trigger optimization to identify effective backdoor activation patterns
  • Clean-label attacks that don’t require label modification but still achieve targeted behaviors
  • Distributed poisoning across multiple data sources to evade detection

Technical Detection and Prevention:

  • Implement gradient-based detection methods that analyze training dynamics to identify potential poisoning attempts.
  • Use spectral signatures and other statistical techniques to detect anomalous patterns in training data.

Deploy federated learning security mechanisms when training involves multiple data sources, including Byzantine-resilient aggregation methods and differential privacy techniques to limit the impact of malicious participants.

LLM05: Improper Output Handling - Securing AI-Generated Content

LLM output handling requires specialized approaches due to the dynamic and potentially adversarial nature of AI-generated content. Traditional output encoding may be insufficient for content that can contain semantically meaningful but syntactically dangerous constructs.

Technical Implementation Challenges: LLM outputs may contain code snippets, markup, or structured data that appears legitimate but contains security vulnerabilities. The challenge lies in implementing validation that maintains the semantic richness of LLM outputs while preventing injection attacks.

Context-dependent vulnerabilities arise when LLM outputs are processed by different downstream systems with varying security requirements. A single output may be safe for display but dangerous when passed to a code execution environment.

Advanced Mitigation Techniques:

  • Implement semantic analysis using secondary LLMs trained specifically for security validation.
  • Deploy multi-stage validation pipelines that analyze outputs at syntactic, semantic, and pragmatic levels.

Use sandboxing techniques for any LLM outputs that may be interpreted as code or commands. Implement capability-based security models that restrict the actions available to LLM-generated content based on the original user’s permissions.

LLM06: Excessive Agency - Controlling Autonomous AI Systems

Excessive agency vulnerabilities become critical as organizations deploy increasingly autonomous AI systems capable of making decisions and taking actions with minimal human oversight.

Agentic Architecture Security: Modern agentic systems often implement complex decision trees involving multiple tool calls, API interactions, and data retrievals. Each decision point represents a potential security control point that must be carefully designed to prevent abuse.

Tool selection and parameter passing in agentic systems require careful validation to prevent attackers from manipulating the agent into accessing unauthorized resources or performing unintended actions.

Technical Control Implementation:

  • Implement capability-based security models where agents receive only the minimum necessary permissions for their intended functions.
  • Use formal verification techniques where possible to prove that agent behaviors remain within acceptable bounds.

Deploy runtime monitoring systems that track agent decision paths and flag anomalous behavior patterns. Implement circuit breakers that automatically restrict agent capabilities when suspicious activity is detected.

LLM07: System Prompt Leakage - Protecting Configuration Data

System prompt leakage vulnerabilities require careful architectural design to ensure that sensitive configuration information cannot be extracted through model interactions.

Technical Root Causes: System prompt leakage often occurs due to insufficient separation between system instructions and user context, inadequate prompt injection defenses, or design patterns that rely on prompts for security controls rather than architectural enforcement.

The vulnerability is exacerbated by the attention mechanisms in transformer architectures, which may inadvertently give prominence to system instructions when processing adversarial inputs.

Architectural Mitigations:

  • Implement system-level controls that operate independently of the model’s prompt processing.
  • Use external validation and authorization systems rather than relying on prompt-based instructions for security enforcement.

Design prompt architectures that minimize the inclusion of sensitive information in system prompts. Implement dynamic prompt generation that adapts system instructions based on user context and authorization levels.

LLM08: Vector and Embedding Weaknesses - Securing RAG Systems

Vector and embedding security requires specialized approaches due to the mathematical properties of embedding spaces and the unique characteristics of similarity-based retrieval systems.

Technical Attack Vectors: Embedding inversion attacks use mathematical techniques to reconstruct original text from embedding vectors. These attacks exploit the fact that high-dimensional embeddings often retain more information about the original text than intended.

Cross-context contamination in multi-tenant vector databases can occur when embedding spaces overlap, allowing queries from one tenant to retrieve semantically similar content from another tenant’s data.

Advanced Security Techniques:

  • Implement embedding space partitioning using techniques such as random projection or adversarial training to create tenant-specific embedding subspaces.
  • Use homomorphic encryption for embedding storage when complete isolation is required.

Deploy query monitoring systems that analyze retrieval patterns to detect potential information leakage or unauthorized access attempts. Implement differential privacy mechanisms for embedding generation to limit information leakage.

LLM09: Misinformation - Technical Approaches to AI Reliability

Misinformation mitigation requires technical approaches that can assess the accuracy and reliability of AI-generated content in real-time.

Technical Detection Methods: Implement fact-checking pipelines using multiple information sources and consistency checking algorithms. Use uncertainty quantification techniques to assess model confidence and flag low-confidence outputs for human review.

Deploy ensemble methods that combine multiple models or information sources to improve accuracy and detect potential misinformation through disagreement analysis.

Implementation Strategies:

  • Integrate external knowledge bases and real-time fact-checking APIs into the generation pipeline.
  • Implement semantic consistency checks that validate generated content against authoritative sources.

Use calibration techniques to improve model confidence estimates and implement threshold-based filtering for uncertain outputs.

LLM10: Unbounded Consumption - Resource Management and Rate Limiting

Unbounded consumption attacks require sophisticated rate limiting and resource management approaches that account for the variable computational costs of different LLM operations.

Technical Implementation Challenges: LLM inference costs vary significantly based on input length, complexity, and model architecture. Traditional rate limiting based on request counts may be inadequate for preventing resource exhaustion attacks.

Model extraction attacks use carefully crafted query patterns to extract model behavior while staying within apparent usage limits. These attacks require detection systems that analyze query patterns rather than just volume.

Advanced Protection Mechanisms:

  • Implement dynamic rate limiting based on computational cost estimation rather than simple request counting.
  • Use machine learning models trained to detect extraction attempts based on query pattern analysis.

Deploy resource isolation techniques using containerization and resource quotas to limit the impact of resource exhaustion attacks on overall system availability.

Implementation Roadmap for Technical Teams

Phase 1: Assessment and Foundation (Months 1-2)

Begin with comprehensive threat modeling specific to your LLM architecture. Inventory all LLM touchpoints, data flows, and integration points. Assess current security controls against the OWASP Top 10 framework.

Implement basic monitoring and logging infrastructure to establish baseline visibility into LLM operations. Deploy initial input validation and output sanitization controls for the most critical attack vectors.

Phase 2: Core Security Controls (Months 3-6)

Deploy advanced prompt injection defenses using multi-model validation approaches. Implement comprehensive access controls and permission management for agentic systems.

Establish secure supply chain processes for model management, including cryptographic verification and automated security scanning. Deploy vector database security controls for RAG systems.

Phase 3: Advanced Defenses and Monitoring (Months 6-12)

Implement machine learning-based attack detection systems for sophisticated threats such as model extraction and poisoning attempts. Deploy differential privacy mechanisms for sensitive data protection.

Establish automated incident response capabilities for LLM-specific security events. Implement comprehensive security metrics and reporting for stakeholder visibility.

Integration with Existing Security Infrastructure

LLM security controls must integrate seamlessly with existing security information and event management (SIEM) systems, identity and access management (IAM) platforms, and security orchestration tools.

Develop custom detection rules and correlation logic for LLM-specific attack patterns. Implement automated response workflows that can isolate compromised LLM systems while maintaining operational continuity.

Monitoring and Detection Strategies

Technical Metrics and Alerting

Implement comprehensive monitoring covering prompt injection attempt rates, unusual output patterns, resource consumption anomalies, and access pattern deviations. Use statistical analysis and machine learning models to establish baselines and detect anomalies.

Deploy real-time alerting for critical security events such as suspected model extraction attempts, significant prompt injection campaigns, or unauthorized access to sensitive embeddings.

Incident Response Procedures

Develop LLM-specific incident response procedures that account for the unique characteristics of AI system compromises. Include procedures for model quarantine, training data investigation, and assessment of potential data exposure.

Establish communication protocols for coordinating with AI model vendors and cloud providers during security incidents. Develop forensic capabilities for analyzing LLM attack patterns and impact assessment.

Conclusion

The OWASP Top 10 for LLM Applications 2025 provides essential technical guidance for securing AI systems in production environments. The framework addresses the fundamental security challenges inherent in probabilistic AI systems while providing practical implementation guidance for engineering teams.

Success in LLM security requires a comprehensive approach that combines traditional security engineering principles with AI-specific techniques. Organizations must invest in specialized security capabilities, develop new monitoring and detection approaches, and establish incident response procedures tailored to AI system characteristics.

The rapidly evolving nature of LLM technology demands continuous adaptation of security approaches. Engineering teams must maintain awareness of emerging threats, participate in the security research community, and implement flexible architectures that can accommodate new defensive techniques as they become available.

As LLM systems become increasingly critical to business operations, the investment in comprehensive security controls becomes essential for maintaining operational resilience and protecting sensitive data. The OWASP framework provides the foundation for building secure, reliable AI systems that can operate safely in production environments.

FAQ

Q: How do we implement effective prompt injection detection in real-time systems?
A: Implement multi-layered detection using embedding similarity analysis, pattern matching for known attack vectors, and secondary LLM validation. Use streaming analytics platforms to process inputs in real-time with sub-100ms latency requirements. Consider implementing circuit breakers that automatically restrict functionality when attack patterns are detected.

Q: What are the performance implications of implementing comprehensive LLM security controls?
A: Security controls typically add 10-30% latency overhead depending on implementation complexity. Input validation and output sanitization have minimal impact, while multi-model validation and cryptographic operations require more significant resources. Implement caching strategies and asynchronous processing where possible to minimize user-facing impact.

Q: How should we handle security for fine-tuned models and LoRA adapters?
A: Implement cryptographic signing for all model artifacts, maintain detailed provenance tracking, and deploy automated security scanning for adapter modules. Use sandboxed environments for testing new adapters and implement rollback capabilities for compromised models. Consider implementing adapter validation using baseline model comparisons.

Q: What monitoring metrics are most critical for detecting LLM-specific attacks?
A: Monitor prompt injection attempt rates, output entropy changes, resource consumption per query, embedding retrieval pattern anomalies, and cross-tenant access attempts. Implement statistical baselines for normal operation and alert on significant deviations. Use machine learning models to detect subtle attack patterns that rule-based systems might miss.​​​​​​​​​​​​​​​​