Security audit for LLM and GenAI applications using OWASP Top 10 for LLM Apps 2025. Assess prompt injection, data leakage, supply chain, and 7 more critical vulnerabilities.

OWASP Top 10 for LLM Applications Security Audit This skill enables AI agents to perform a comprehensive security assessment of Large Language Model (LLM) and Generative AI applications using the OWASP Top 10 for LLM Applications 2025 , published by the OWASP GenAI Security Project. The OWASP Top 10 for LLM Applications identifies the most critical security risks in systems that integrate large language models, covering vulnerabilities from prompt injection to unbounded resource consumption. This is the authoritative industry standard for LLM application security. Use this skill to identify security vulnerabilities, assess risk exposure, prioritize remediation, and establish secure development practices for AI powered applications. Combine with "NIST AI RMF" for comprehensive risk management or "ISO 42001 AI Governance" for governance compliance. When to Use This Skill Invoke this skill when: Auditing security of LLM powered applications before deployment Reviewing GenAI integrations for security vulnerabilities Assessing RAG (Retrieval Augmented Generation) systems Evaluating chatbot or AI assistant security Conducting penetration testing of AI features Building secure AI application architectures Reviewing third party AI API integrations Preparing for security compliance reviews Responding to AI related security incidents Inputs Required When executing this audit, gather: application description : Description of the AI application (purpose, LLM used, architecture, features, user base) [REQUIRED] architecture details : System architecture (APIs, databases, vector stores, plugins, integrations) [OPTIONAL but recommended] llm provider : LLM provider and model (OpenAI GPT 4, Anthropic Claude, self hosted, etc.) [OPTIONAL] deployment context : Deployment environment (cloud, on premise, hybrid, edge) [OPTIONAL] data sensitivity : Types of data processed (PII, financial, health, proprietary) [OPTIONAL] existing controls : Current security measures (auth, rate limiting, content filtering) [OPTIONAL] specific concerns : Known vulnerabilities or areas of focus [OPTIONAL] testing authorization : Explicit authorization, environment, and boundaries for active testing [REQUIRED for live or active tests] safe testing mode : documentation only, staging, production readonly, or production approved [OPTIONAL, defaults to documentation only unless authorization is clear] Authorized Testing Boundary Only run active security tests on systems the user owns or is explicitly authorized to test. If authorization is absent or unclear, perform a documentation and architecture review only, using safe hypothetical examples instead of live payload execution. For production systems: Prefer staging or read only validation Avoid denial of service, destructive, persistence, or real exfiltration tests unless written scope explicitly permits them Redact credentials, secrets, PII, and sensitive prompt or model outputs in reports Stop and report if testing crosses the approved scope The OWASP Top 10 for LLM Applications (2025) LLM01: Prompt Injection Severity : Critical Description : Attackers manipulate LLM operations through crafted inputs, either directly or indirectly, to bypass intended functionality, access unauthorized data, or trigger unintended actions. Attack Vectors: Direct injection : Malicious user prompts containing override commands Indirect injection : Hidden instructions in external content (web pages, documents, emails) processed by the LLM Jailbreaks : Techniques to bypass safety constraints and content policies Impact: Unauthorized data access and exfiltration Bypass of content safety filters Manipulation of downstream system actions Social engineering of users through manipulated outputs Assessment Checklist: [ ] Input sanitization and validation implemented [ ] System prompts separated from user inputs with clear delimiters [ ] Least privilege applied to LLM backend access [ ] Output validation before downstream actions [ ] Human in the loop for critical operations [ ] Adversarial testing conducted with known injection techniques [ ] Content filtering layers applied pre and post LLM Mitigation Strategies: 1. Enforce privilege controls on LLM backend access 2. Segregate external content from user prompts 3. Maintain human oversight for critical functions 4. Implement input/output validation pipelines 5. Conduct regular adversarial testing LLM02: Sensitive Information Disclosure Severity : Critical Description : LLMs inadvertently expose confidential data including PII, proprietary algorithms, credentials, intellectual property, or internal system information through their outputs. Attack Vectors: Crafted prompts designed to extract training data Legitimate queries that trigger memorized sensitive content Model outputs revealing internal system architecture Embedding leakage from vector databases Impact: Privacy violations and regulatory non compliance (GDPR, CCPA) Intellectual property theft Credential exposure enabling further attacks Reputational damage Assessment Checklist: [ ] PII and sensitive data removed from training/fine tuning data [ ] Data masking and tokenization in logs and outputs [ ] System instructions forbidding sensitive disclosures [ ] Output filtering for known sensitive patterns (SSN, credit cards, API keys) [ ] Model access restricted to necessary information via middleware [ ] User education against pasting confidential content [ ] Output monitoring for anomalous data exposure Mitigation Strategies: 1. Sanitize training data to remove sensitive information 2. Implement data loss prevention (DLP) on outputs 3. Apply access controls limiting model's data reach 4. Monitor outputs for sensitive data patterns 5. Use differential privacy techniques in training LLM03: Supply Chain Vulnerabilities Severity : High Description : Compromised third party components (models, datasets, libraries, plugins) introduce security risks including malware, backdoors, or biased behavior. Attack Vectors: Malicious pre trained models from public repositories Poisoned datasets with embedded triggers Vulnerable ML libraries and dependencies Compromised plugins with unauthorized access Trojanized fine tuning adapters Impact: System compromise and data theft Backdoor access to production systems Model corruption affecting all users Legal liability from unlicensed content Assessment Checklist: [ ] Models sourced from verified, reputable providers [ ] Digital signatures and checksums verified [ ] Model files scanned for suspicious code (picklescan, etc.) [ ] Third party models deployed in sandboxed environments [ ] Dependencies regularly updated and audited [ ] Plugin permissions restricted with allowlists [ ] Complete inventory of all models and components maintained [ ] SBOM (Software Bill of Materials) maintained for AI components Mitigation Strategies: 1. Source models only from trusted, verified providers 2. Scan model files for malicious code before deployment 3. Sandbox third party models with restricted permissions 4. Maintain updated dependency inventory 5. Implement model signing and integrity verification LLM04: Data and Model Poisoning Severity : High Description : Attackers manipulate training or fine tuning data to introduce vulnerabilities, backdoors, or biases that compromise model security and reliability. Attack Vectors: Crafted training examples with hidden trigger phrases Poisoned web scraped content absorbed during training Direct tampering with model weights or parameters Malicious fine tuning data Subtle label manipulation or data anomalies Impact: Biased or degraded model outputs Trigger activated backdoors in production Erosion of model trustworthiness Long term hidden threats difficult to detect Assessment Checklist: [ ] Training data validated, cleaned, and audited [ ] Data provenance tracked and documented [ ] Rate limiting and moderation for crowdsourced data [ ] Differential privacy techniques applied [ ] Models tested with known trigger phrases before deployment [ ] Deployed models monitored for behavioral drift [ ] Model file checksums verified against known good states Mitigation Strategies: 1. Validate and clean all training data sources 2. Implement data provenance tracking 3. Apply differential privacy to limit individual data influence 4. Test with adversarial inputs before deployment 5. Monitor production models for unexpected behavior LLM05: Improper Output Handling Severity : High Description : Applications blindly execute or render LLM outputs without validation, enabling code injection, XSS, SQL injection, SSRF, and other attacks. Attack Vectors: Unescaped HTML/JavaScript in outputs (XSS) Model generated shell commands executed without sanitization SQL queries constructed from model output Unsanitized API calls based on AI suggestions Direct execution via eval() or exec() Impact: Remote code execution Session hijacking Database manipulation Privilege escalation Full system compromise Assessment Checklist: [ ] All LLM output treated as untrusted input [ ] Strict output schema validation enforced (JSON, formats) [ ] Output sanitized and escaped based on context (HTML, SQL, shell) [ ] Parameterized queries used instead of raw SQL [ ] Allowlists for acceptable output patterns [ ] Generated code executed in sandboxed environments [ ] Human approval required for high impact actions [ ] Rendering libraries with built in escaping used Mitigation Strategies: 1. Never trust LLM output; validate and sanitize everything 2. Enforce strict output schemas 3. Use parameterized queries and safe ORM methods 4. Sandbox all code execution 5. Require human approval for privileged operations LLM06: Excessive Agency Severity : High Description : AI agents possess excessive permissions and autonomous capabilities, enabling significant harm through compromised prompts, hallucinations, or malicious manipulation. Attack Vectors: Prompt injection exploiting overly permissioned agents Hallucinations triggering unintended high impact actions Confused deputy attacks using AI's elevated privileges Malicious plugins with excessive access Unrestricted system control (email, API, database) Impact: Unauthorized data transmission Destructive actions (deletion, modification) Financial loss through unauthorized transactions Service disruptions Automated attack amplification Assessment Checklist: [ ] Principle of least privilege applied to all AI capabilities [ ] Granular permissions with limited scope OAuth tokens [ ] Functionality compartmentalized across narrow scope agents [ ] High risk actions restricted (deletion, transfers, device control) [ ] Explicit user approval for significant operations [ ] Rate limiting on AI actions and API calls [ ] Comprehensive audit logs of all agent activities [ ] Monitoring with alerts for anomalous behavior Mitigation Strategies: 1. Grant only essential capabilities (least privilege) 2. Compartmentalize agent functionality 3. Require human approval for high impact operations 4. Implement comprehensive audit logging 5. Set up real time monitoring and anomaly detection LLM07: System Prompt Leakage Severity : Medium Description : System instructions intended to guide AI behavior are exposed to users or attackers, revealing internal logic, security controls, or sensitive configurations. Attack Vectors: Prompt injection requesting instruction disclosure Sophisticated probing asking to repeat conversation context Tokenization quirks causing unintended disclosure Reverse engineering through behavioral observation Model unintentional

owasp-llm-top10