Owasp Llm Top10

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]
• safetestingmode: 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

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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

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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

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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

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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

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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

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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

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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 unintentionally echoing system prompts

Impact:

• Security logic exposure enabling bypass attacks
• Credential compromise if secrets embedded in prompts
• Internal system knowledge revelation
• Facilitation of more targeted attacks

Assessment Checklist:

• [ ] No passwords, API keys, or secrets in system prompts
• [ ] Prompts treated as public information
• [ ] Models configured to refuse revealing system messages
• [ ] Clear message role delimiters (system/user/assistant)
• [ ] Security policies enforced at application level, not prompt level
• [ ] Output monitoring for prompt leakage patterns
• [ ] Regular testing with known extraction techniques

Mitigation Strategies:

1. Never embed sensitive data in system prompts
2. Implement application-level security enforcement
3. Configure models to refuse prompt disclosure
4. Monitor outputs for leakage patterns
5. Use structured message formats with role delimiters

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LLM08: Vector and Embedding Weaknesses

Severity: Medium

Description: Vulnerabilities in vector databases and embedding-based retrieval systems (RAG) allow poisoning, injection, or unauthorized access to stored data.

Attack Vectors:

• Poisoned embeddings retrieved during RAG operations
• Direct injection of malicious vectors into stores
• Retrieval of sensitive data from improperly secured databases
• Metadata-based attacks exploiting insufficient filtering
• Similarity-based retrieval returning harmful content

Impact:

• Output manipulation through poisoned context
• Sensitive data leakage from vector stores
• Misinformation injection
• Compromised RAG system integrity

Assessment Checklist:

• [ ] Data validated and sanitized before vectorization
• [ ] Access controls on vector store insertion and modification
• [ ] Metadata filtering restricts retrieval to appropriate categories
• [ ] Monitoring for suspicious bulk insertions
• [ ] Similarity thresholds ensuring relevant retrieval
• [ ] Sensitive and public vector stores separated
• [ ] Embedding source provenance tracked
• [ ] Anomaly detection for unusual retrieval patterns

Mitigation Strategies:

1. Validate data before storing in vector databases
2. Implement strict access controls on vector operations
3. Use metadata filtering and similarity thresholds
4. Separate sensitive and public data stores
5. Monitor for anomalous patterns

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LLM09: Misinformation

Severity: Medium

Description: LLMs generate plausible but false information (hallucinations/confabulations) that users may trust and act upon, causing harm.

Attack Vectors:

• Fabricated facts presented authoritatively
• Fake citations or references that don't exist
• Invented case law, medical advice, or technical solutions
• Adversarial prompts designed to trigger hallucinations
• Confident incorrect reasoning

Impact:

• Harmful decisions based on false information
• Legal liability from incorrect advice
• Erosion of trust in AI systems
• Regulatory violations in compliance contexts
• Reputational damage

Assessment Checklist:

• [ ] Confidence scores or uncertainty indicators provided
• [ ] Fact-checking against reliable databases implemented
• [ ] Citations with verifiable sources required for sensitive domains
• [ ] RAG grounding responses in validated data
• [ ] System instructions encourage admitting uncertainty
• [ ] Human review for critical outputs
• [ ] Model limitations clearly communicated to users

Mitigation Strategies:

1. Implement retrieval-augmented generation (RAG) for grounding
2. Provide confidence indicators to users
3. Require verifiable citations for critical domains
4. Add human review for high-stakes outputs
5. Clearly communicate model limitations

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LLM10: Unbounded Consumption

Severity: Medium

Description: Uncontrolled LLM usage causes denial-of-service, system crashes

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: mastepanoski
• Source: mastepanoski/claude-skills
• License: MIT

Install and usage instructions live in the source repository linked above.