07 OWASP Top 10 for LLM Applications

Large Language Model applications introduce security risks that traditional web and application security frameworks do not always fully describe.

Modern AI applications may combine:

Large Language Models
Enterprise data
Retrieval-Augmented Generation
Vector databases
AI agents
External tools
APIs
Cloud infrastructure
Third-party models
Model supply chains

Security teams therefore need a structured way to identify and communicate common risks affecting these systems.

One of the most useful references is the:

OWASP GenAI LLM Top 10

The OWASP GenAI Security Project maintains guidance covering important risks affecting applications powered by Large Language Models and Generative AI.

For an AI Security Engineer, the important objective is not to memorize ten names.

The objective is to understand:

Where does each risk appear in an architecture, how could it create security impact, and what controls should reduce that risk?

Learning Objectives

By the end of this lesson, you should be able to:

Explain the purpose of the OWASP GenAI LLM Top 10.
Understand the major LLM security risk categories.
Recognize these risks inside enterprise AI architectures.
Distinguish between model-level and application-level weaknesses.
Understand how LLM risks connect with traditional cybersecurity.
Identify appropriate preventive and detective controls.
Use OWASP during AI architecture reviews.
Use OWASP during threat modeling.
Use OWASP during AI security assessments.
Map security findings to OWASP categories.
Avoid treating the OWASP Top 10 as a simple compliance checklist.

Current OWASP GenAI LLM Top 10

The current 2026 structure is:

ID	Risk
LLM01	Prompt Injection
LLM02	Sensitive Information Disclosure
LLM03	Excessive Agency
LLM04	Supply Chain
LLM05	Data and Model Poisoning
LLM06	Unbounded Consumption
LLM07	Misinformation
LLM08	Hidden Context Exposure
LLM09	Vector and Embedding Weaknesses
LLM10	Improper Output Handling

These categories help security engineers organize common security problems across the LLM application lifecycle.

Important Perspective

The OWASP LLM Top 10 does not mean:

Secure These 10 Things
        ↓
AI System Is Secure

Instead, think:

OWASP Top 10
      ↓
Security Awareness
      ↓
Architecture Review
      ↓
Threat Identification
      ↓
Security Testing
      ↓
Control Design

It is a starting point for security analysis.

It does not replace:

Threat modeling
Application security
Cloud security
IAM
Data security
API security
Security monitoring
Risk management

The 2026 OWASP LLM Top 10 at a Glance

A useful mental model is:

INPUT
 │
 └── LLM01 Prompt Injection

DATA
 │
 ├── LLM02 Sensitive Information Disclosure
 └── LLM05 Data and Model Poisoning

AGENTS
 │
 └── LLM03 Excessive Agency

SUPPLY CHAIN
 │
 └── LLM04 Supply Chain

RESOURCES
 │
 └── LLM06 Unbounded Consumption

TRUST
 │
 ├── LLM07 Misinformation
 └── LLM08 Hidden Context Exposure

RAG
 │
 └── LLM09 Vector and Embedding Weaknesses

OUTPUT
 │
 └── LLM10 Improper Output Handling

Now let’s understand each category.

LLM01 — Prompt Injection

Prompt Injection occurs when input influences an LLM in unintended ways.

The input may attempt to override, manipulate or redirect expected behavior.

A simplified attack path is:

Trusted Application Instructions
            +
Untrusted Input
            ↓
           LLM
            ↓
Unexpected Behavior

Prompt injection can be:

Direct
Indirect

Direct Prompt Injection

A user directly provides manipulative instructions.

Conceptually:

User
 │
 ▼
Malicious Prompt
 │
 ▼
LLM
 │
 ▼
Unexpected Behavior

For example, an attacker may attempt to cause an AI application to ignore its intended task.

The security problem becomes more serious when the LLM has access to:

Sensitive data
Enterprise APIs
AI agents
External tools

Indirect Prompt Injection

The malicious instruction arrives through another data source.

For example:

Malicious Document
        ↓
RAG
        ↓
LLM
        ↓
Unexpected Behavior

Possible sources include:

Documents
Websites
Emails
Search results
Database content
Tool responses

The user may never see the malicious instruction.

Enterprise Example

Imagine an AI assistant summarizes external webpages.

User
 │
 ▼
AI Assistant
 │
 ▼
External Website
 │
 ▼
LLM

A website contains instructions intended for the AI rather than the human reader.

If the model follows those instructions, the external site has influenced application behavior.

Potential Impact

Prompt Injection may contribute to:

Sensitive data exposure
Unauthorized actions
Security control bypass
Manipulated output
Agent tool abuse

Security Controls

Defenses may include:

Treat external content as untrusted.
Separate instructions from data where possible.
Minimize privileges.
Restrict agent capabilities.
Apply authorization outside the model.
Validate high-impact actions.
Monitor suspicious behavior.

Important principle:

Prompt defenses alone should never be responsible for protecting critical assets.

LLM02 — Sensitive Information Disclosure

Sensitive Information Disclosure occurs when an LLM application exposes information that should remain protected.

Sensitive information may include:

Personal information
Financial information
Customer records
Source code
Security architecture
Internal documents
API credentials
Proprietary information

Example

Employee
   │
   ▼
Enterprise AI Assistant
   │
   ▼
RAG
   │
   ▼
Confidential HR Document
   │
   ▼
Unauthorized Disclosure

The model may only be the final component delivering the information.

The actual weakness might exist in authorization, retrieval or data handling.

Common Causes

Possible causes include:

Excessive model context
Weak RAG authorization
Poor data classification
Cross-user data exposure
Sensitive information inside prompts
Insecure logging
Improper access control

Security Controls

Important controls include:

Data Minimization

Only provide information required for the task.

Authorization

Prevent unauthorized data from reaching the model.

Data Classification

Understand which information is:

Public
Internal
Confidential
Restricted

Logging Controls

Avoid unnecessarily recording sensitive prompts and responses.

Output Controls

Validate whether sensitive information may be returned.

The strongest principle is:

If a user is not authorized to access information, avoid giving that information to the model in the first place.

LLM03 — Excessive Agency

Modern AI systems increasingly use agents that can perform actions.

Examples include:

Read Email
Create Ticket
Query Database
Modify Cloud Resource
Execute Code
Send Message

Excessive Agency exists when the system gives an LLM-based application too much:

Functionality
Permission
Autonomy

Example

Requirement:

AI Assistant should read cloud security alerts.

Actual permission:

Cloud Administrator

This creates excessive agency.

Why This Is Dangerous

Consider:

Prompt Injection
      ↓
AI Agent
      ↓
Administrator Permission
      ↓
Production Environment

A model behavior problem has now become an infrastructure security problem.

Three Important Questions

Excessive Functionality

Does the agent have tools it does not need?

Excessive Permissions

Do those tools have more permissions than necessary?

Excessive Autonomy

Can the system perform high-impact actions without approval?

Better Architecture

User
 │
 ▼
AI Agent
 │
 ▼
Restricted Tool
 │
 ▼
Least Privilege
 │
 ▼
Human Approval for High-Risk Actions

Security Controls

Apply:

Least privilege
Tool allowlists
Restricted functions
Scoped service identities
Human approval
Strong logging
Transaction limits
Action validation

One of the most important AI Security Engineer principles is:

The model should never have more authority than its business function requires.

LLM04 — Supply Chain

AI applications depend on complex supply chains.

An enterprise AI system may include:

Models
+
Datasets
+
Python Packages
+
Containers
+
Libraries
+
Frameworks
+
APIs
+
Plugins
+
Cloud Services

Compromise of any dependency can affect the final application.

Example Supply Chain

External Model
      ↓
Model Repository
      ↓
AI Application
      ↓
Production

If the model is malicious or tampered with, the organization may introduce the problem into production itself.

Possible Supply-Chain Risks

Malicious models
Vulnerable libraries
Compromised packages
Unsafe model adapters
Compromised containers
Untrusted datasets
Third-party AI providers
CI/CD compromise

Security Questions

Before adopting a component, ask:

Where did it come from?

Who maintains it?

What version are we using?

Has it been reviewed?

Can integrity be verified?

What permissions does it require?

How will vulnerabilities be tracked?

Security Controls

Organizations should consider:

Trusted repositories
Dependency scanning
Model provenance
Artifact integrity
Software inventories
AI Bills of Materials where appropriate
Version control
Secure CI/CD
Vendor assessment

The security principle is:

Do not automatically trust an AI component because it is popular or publicly available.

LLM05 — Data and Model Poisoning

AI systems depend heavily on data.

Attackers may attempt to manipulate:

Training data
Fine-tuning data
Embedding data
RAG documents
Models

This can change how the AI behaves.

Basic Poisoning Scenario

Trusted Dataset
      +
Malicious Data
      ↓
Training
      ↓
Manipulated Model

RAG Poisoning Example

Attacker
   ↓
Malicious Document
   ↓
Enterprise Knowledge Base
   ↓
Embedding
   ↓
Vector Database
   ↓
RAG
   ↓
LLM

The attacker may influence model responses without directly attacking the model itself.

Potential Impact

Poisoning may cause:

Incorrect responses
Hidden backdoors
Manipulated decisions
Biased output
Unsafe model behavior
Security control bypass

Security Controls

Important controls include:

Trusted data sources
Data provenance
Restricted ingestion
Integrity validation
Dataset versioning
Model versioning
Change approval
Monitoring for unexpected behavior

AI Security Engineers should ask:

Who can change what the AI learns from or retrieves from?

LLM06 — Unbounded Consumption

LLM applications can consume significant:

CPU
GPU
Memory
Tokens
API calls
Cloud resources
Financial budget

If usage is not controlled, attackers or accidental usage may exhaust resources.

Example

Attacker
   ↓
Thousands of Large Requests
   ↓
LLM API
   ↓
High Token Consumption
   ↓
Cost Increase / Service Degradation

Potential Impact

Unbounded consumption can lead to:

Denial of service
Unexpected cloud bills
API quota exhaustion
Performance degradation
Model extraction attempts
Reduced availability

Security Controls

Consider:

Rate limiting
User quotas
Token limits
Request size limits
Timeouts
Budget controls
Cost monitoring
Abuse detection

For AI systems:

Cost protection is also a security consideration.

LLM07 — Misinformation

LLMs can generate responses that appear confident but are:

Incorrect
Incomplete
Misleading
Fabricated

This becomes a security issue when users or automated systems rely on those responses for important decisions.

Example

Security Engineer
      ↓
AI Assistant
      ↓
Incorrect Firewall Recommendation
      ↓
Engineer Trusts Output
      ↓
Security Control Weakened

The problem is not simply that the AI produced an incorrect answer.

The security problem appears because the output influences a security-sensitive decision.

Higher-Risk Areas

Misinformation becomes particularly important when AI is used for:

Security configuration
Incident response
Healthcare
Financial decisions
Legal analysis
Code generation
Infrastructure changes

Security Controls

Use:

Human validation
Trusted source grounding
RAG where appropriate
Citation and provenance
Confidence and uncertainty handling
Independent validation
High-risk action approval

The key principle is:

AI output should be treated as information requiring appropriate verification, not unquestionable truth.

LLM08 — Hidden Context Exposure

Modern LLM applications often send significantly more information to the model than the user sees.

Hidden context may include:

System Instructions
+
Internal Policies
+
Conversation Memory
+
Retrieved Documents
+
Tool Definitions
+
Application Metadata
+
Intermediate Reasoning Data

This hidden context may contain information the organization does not intend to expose.

Example

Application
   │
   ├── System Instructions
   ├── Internal Workflow
   ├── Tool Metadata
   └── Sensitive Context
         ↓
        LLM
         ↓
       User

If information from the hidden context becomes observable through output or application behavior, the security boundary may fail.

Why This Matters

Organizations sometimes assume:

User Cannot See It
       =
It Is Secure

That is not a safe assumption.

Information provided to the model should be considered potentially influenceable or exposable depending on application architecture and model behavior.

Security Controls

Apply:

Context minimization
Avoid embedding secrets in hidden instructions
Separate security controls from prompts
Limit sensitive metadata
Enforce access outside the model
Carefully manage conversation memory
Review tool descriptions and internal context

A useful rule is:

Do not place information in LLM context that the model does not need to perform its task.

LLM09 — Vector and Embedding Weaknesses

RAG applications frequently use embeddings and vector databases.

Typical architecture:

Enterprise Documents
       ↓
Embedding Model
       ↓
Vector Database
       ↓
Retriever
       ↓
LLM

Security weaknesses in this layer may expose sensitive information or manipulate retrieval.

Important Risks

Missing Authorization

A user may retrieve documents they should not access.

Tenant Isolation Failure

One customer’s information may become visible to another.

Poisoned Embeddings

Malicious content may enter the retrieval system.

Sensitive Metadata Exposure

Vector databases may contain sensitive metadata.

Excessive Retrieval

Too much information may be passed to the LLM.

Example

Engineering User
       ↓
RAG Search
       ↓
Vector Database
       ↓
Executive HR Documents

The user’s authentication may work correctly.

The problem is that retrieval authorization failed.

Security Controls

Important controls include:

Document-level authorization
Tenant isolation
Secure ingestion
Data classification
Vector database authentication
Private network access
Retrieval filtering
Metadata protection
Monitoring

Important principle:

RAG must preserve the organization’s existing authorization model.

LLM10 — Improper Output Handling

LLM-generated output is not automatically safe.

An LLM may generate:

HTML
SQL
Source code
Shell commands
API requests
URLs
Configuration
Structured data

Problems arise when another system trusts or executes this content without validation.

Example

User Input
    ↓
LLM
    ↓
Generated SQL
    ↓
Application Executes SQL

The LLM has effectively become part of a command-generation pipeline.

Another Example

LLM
 ↓
Generated Shell Command
 ↓
Automation Platform
 ↓
Production Server

If output is trusted automatically, security impact can become significant.

Security Controls

Generated output should be treated similarly to other untrusted input.

Apply:

Output validation
Encoding
Sanitization
Parameterized operations
Allowlisted actions
Sandboxing
Least privilege
Human approval where appropriate

Important principle:

AI-generated content does not become trusted simply because your own application generated it.

Seeing the Risks Together

Individual categories often interact.

Consider this attack chain:

Malicious Document
       ↓
LLM01 Prompt Injection
       ↓
RAG
       ↓
LLM03 Excessive Agency
       ↓
AI Agent
       ↓
LLM10 Improper Output Handling
       ↓
External Tool
       ↓
Sensitive Information Disclosure

Real security incidents may involve multiple categories rather than exactly one.

This is why the OWASP Top 10 should be used as a thinking framework.

Enterprise Scenario

Imagine an organization deploys:

Security Analyst
      │
      ▼
AI Security Assistant
      │
      ├── LLM
      │
      ├── RAG
      │     └── Security Runbooks
      │
      └── AI Agent
             ├── SIEM
             ├── Cloud
             └── Ticketing

Let’s evaluate it using the OWASP categories.

LLM01 — Prompt Injection

Can user or retrieved input manipulate the assistant?

LLM02 — Sensitive Information Disclosure

Can confidential incident information be exposed?

LLM03 — Excessive Agency

Can the agent perform unnecessary cloud actions?

LLM04 — Supply Chain

Are models, dependencies and libraries trusted?

LLM05 — Data and Model Poisoning

Can malicious runbooks enter the RAG knowledge base?

LLM06 — Unbounded Consumption

Can users generate unlimited expensive requests?

LLM07 — Misinformation

Could incorrect security advice influence incident response?

LLM08 — Hidden Context Exposure

Does the model receive internal instructions or metadata that should not be exposed?

LLM09 — Vector and Embedding Weaknesses

Can analysts retrieve security documents outside their authorization?

LLM10 — Improper Output Handling

Can generated commands be automatically executed?

You have now performed a basic OWASP-based AI security review.

OWASP Architecture Mapping

You can also map categories to architecture layers.

USER
 │
 └── LLM01 Prompt Injection
 │
 ▼
APPLICATION
 │
 ├── LLM02 Sensitive Information Disclosure
 ├── LLM08 Hidden Context Exposure
 └── LLM10 Improper Output Handling
 │
 ▼
LLM
 │
 └── LLM07 Misinformation
 │
 ▼
RAG
 │
 ├── LLM05 Data and Model Poisoning
 └── LLM09 Vector and Embedding Weaknesses
 │
 ▼
AGENT
 │
 └── LLM03 Excessive Agency
 │
 ▼
PLATFORM
 │
 ├── LLM04 Supply Chain
 └── LLM06 Unbounded Consumption

This is not an exclusive mapping.

Many categories span multiple layers.

Using OWASP During Architecture Review

Suppose a new AI project is proposed.

Start with:

Business Use Case
       ↓
Architecture Diagram
       ↓
Data Flow Diagram
       ↓
Trust Boundaries
       ↓
OWASP Risk Review

For every category ask:

Does this risk apply?

Where could it occur?

What controls currently exist?

Are those controls sufficient?

Example Architecture Review Worksheet

OWASP Risk	Applicable?	Existing Control	Gap
Prompt Injection	Yes	Prompt filtering	Agent still overprivileged
Sensitive Information Disclosure	Yes	SSO	RAG authorization missing
Excessive Agency	Yes	Service account	Permissions excessive
Supply Chain	Yes	Dependency scanning	Models not tracked
Data & Model Poisoning	Yes	Restricted upload	No document provenance
Unbounded Consumption	Yes	Authentication	No user quota
Misinformation	Yes	Human review	None for automated workflow
Hidden Context Exposure	Yes	None	Sensitive context present
Vector & Embedding Weaknesses	Yes	Private DB	Tenant filtering incomplete
Improper Output Handling	Yes	None	Output sent directly to tool

This converts OWASP into actionable engineering work.

Using OWASP During Threat Modeling

OWASP can help generate threat scenarios.

Example:

Asset:
Production Cloud Account

Entry Point:
AI Assistant Prompt

OWASP Risk:
Prompt Injection

Related Risk:
Excessive Agency

Attack Path:
Prompt
  ↓
LLM
  ↓
Agent
  ↓
Cloud Tool
  ↓
Production Action

Now ask:

What prevents this?
How would it be detected?
What would the impact be?
What additional controls are required?

This is more useful than merely marking:

LLM01 = Applicable

Using OWASP During Security Testing

A security assessment might use categories to organize test cases.

Example:

LLM01
Test Prompt Handling

LLM02
Test Data Exposure

LLM03
Test Agent Permissions

LLM04
Review Dependencies

LLM05
Test Knowledge Integrity

LLM06
Test Usage Controls

LLM07
Evaluate High-Risk Output Reliability

LLM08
Review Hidden Context

LLM09
Test RAG Authorization

LLM10
Test Output Processing

This creates a structured methodology.

Using OWASP During Reporting

Security findings can reference relevant categories.

Example:

Finding:
AI Agent Has Excessive Cloud Permissions

OWASP Mapping:
LLM03 — Excessive Agency

Risk:
A manipulated AI workflow may perform actions
outside the intended business function.

Recommendation:
Replace administrator permissions with a
restricted read-only security role.

This helps communicate findings consistently.

OWASP Is Not a Severity Rating

Do not assume:

LLM01
=
More Severe Than LLM10

The numbering represents categories and prioritization in the framework.

The actual severity of a finding depends on the organization’s architecture and business context.

For example:

Prompt Injection
+
No Sensitive Data
+
No Tools

may have limited impact.

While:

Excessive Agency
+
Production Administrator Access
+
Automated Actions

could create very high impact.

Always evaluate the actual scenario.

Risk = Architecture + Exposure + Privilege + Impact

A useful engineering mindset is:

Security Risk
      ≈
Weakness
      +
Exposure
      +
Privilege
      +
Data Sensitivity
      +
Business Impact

OWASP identifies the type of problem.

Your job is to determine its importance in the real environment.

OWASP and Defense in Depth

Do not attempt to solve each category with one control.

For example, Prompt Injection cannot be addressed only with:

Prompt Filter

A stronger architecture might include:

Authentication
      ↓
Authorization
      ↓
Context Minimization
      ↓
Prompt Controls
      ↓
LLM
      ↓
Restricted Agent
      ↓
Tool Authorization
      ↓
Human Approval
      ↓
Monitoring

This reduces the impact even if manipulation reaches the model.

Security Controls Should Exist Outside the Model

This is one of the most important principles in the entire lesson.

Weak:

System Prompt:
"Never access confidential data."

Strong:

User Identity
      ↓
Authorization
      ↓
Confidential Data Not Retrieved

Weak:

System Prompt:
"Never delete cloud resources."

Strong:

AI Agent Identity
      ↓
No Delete Permission

The application should technically enforce important security boundaries wherever possible.

OWASP and Zero Trust

Zero Trust principles work well with AI Security.

Do not automatically trust:

User Input
Retrieved Documents
Model Output
Agent Decisions
Tool Output
External APIs
Third-Party Models

Instead:

Verify
   ↓
Authorize
   ↓
Restrict
   ↓
Monitor

OWASP and Secure AI Development

OWASP risks should be considered throughout development.

Design
  ↓
Threat Model
  ↓
OWASP Review
  ↓
Develop
  ↓
Security Test
  ↓
Deploy
  ↓
Monitor

This is better than waiting until production.

Common Beginner Mistakes

Mistake 1 — Memorizing the List

Knowing the names does not mean you can secure an application.

Mistake 2 — Treating OWASP as a Compliance Checklist

Checking ten boxes does not prove the architecture is secure.

Mistake 3 — Looking Only for Prompt Injection

AI Security contains many other risks.

Mistake 4 — Ignoring Traditional Security

IAM, APIs, cloud security and application security remain essential.

Mistake 5 — Assuming System Prompts Are Security Boundaries

Important controls should be enforced outside the model.

Mistake 6 — Ignoring Business Impact

The same category can have very different severity in different architectures.

Mistake 7 — Mapping Every Finding to Only One Category

Real attack chains may involve several OWASP risks.

Mistake 8 — Using an Old OWASP List Without Checking the Version

AI security guidance changes quickly.

Always identify which version your assessment or document uses.

AI Security Engineer OWASP Review Checklist

LLM01 — Prompt Injection

Is untrusted input identified?
Can external content influence the model?
Are high-impact actions protected independently?

LLM02 — Sensitive Information Disclosure

Is sensitive data classified?
Is authorization enforced before retrieval?
Is unnecessary context minimized?

LLM03 — Excessive Agency

Are agent tools necessary?
Are permissions least privileged?
Are high-risk actions approved?

LLM04 — Supply Chain

Are models trusted?
Are dependencies tracked?
Are artifacts validated?

LLM05 — Data and Model Poisoning

Are data sources trusted?
Is document ingestion controlled?
Are models and datasets versioned?

LLM06 — Unbounded Consumption

Are rate limits configured?
Are token limits defined?
Are costs monitored?

LLM07 — Misinformation

Are high-impact outputs verified?
Is human review required where necessary?
Are trustworthy sources used?

LLM08 — Hidden Context Exposure

Is unnecessary hidden context minimized?
Are secrets excluded from prompts?
Is conversation memory appropriately protected?

LLM09 — Vector and Embedding Weaknesses

Is RAG authorization enforced?
Is tenant isolation implemented?
Is the vector database protected?

LLM10 — Improper Output Handling

Is model output validated?
Can generated output execute directly?
Are downstream systems least privileged?

Interview Perspective

You may be asked:

What is the OWASP Top 10 for LLM Applications?

A strong answer is:

The OWASP GenAI LLM Top 10 is a security awareness and guidance framework covering important risks affecting applications built with Large Language Models and Generative AI. I use it as part of architecture review, threat modeling and security testing rather than treating it as a standalone compliance checklist.

Another question may be:

Which OWASP LLM risk would concern you most in an AI agent?

A strong answer is:

Excessive Agency would be particularly important because an agent may have tools and permissions that allow it to affect enterprise systems. I would review its functionality, permissions and autonomy, apply least privilege and require independent approval for high-impact actions. Prompt Injection would also be important because manipulated input could potentially influence how those capabilities are used.

Another question may be:

How would you prevent sensitive information disclosure from a RAG application?

A strong answer is:

I would enforce authorization before retrieval so that the model only receives documents the authenticated user is permitted to access. I would also apply data classification, context minimization, tenant isolation and appropriate logging rather than relying only on a system prompt telling the model not to reveal confidential information.

Another question may be:

Why is Improper Output Handling dangerous?

A strong answer is:

Because LLM output may become input to another system. If generated SQL, commands, HTML, code or API arguments are trusted without validation, the model can become part of an injection or execution path. Model output should therefore be treated according to its trust level and validated before sensitive use.

Practical Assessment Method

When reviewing an AI application, use:

1. Draw Architecture
       ↓
2. Identify Assets
       ↓
3. Identify Trust Boundaries
       ↓
4. Map OWASP Risks
       ↓
5. Identify Controls
       ↓
6. Identify Gaps
       ↓
7. Test Controls
       ↓
8. Assess Impact
       ↓
9. Recommend Remediation
       ↓
10. Retest

This turns OWASP from theory into security engineering.

Key Takeaways

The current OWASP GenAI LLM Top 10 covers:

LLM01 — Prompt Injection
LLM02 — Sensitive Information Disclosure
LLM03 — Excessive Agency
LLM04 — Supply Chain
LLM05 — Data and Model Poisoning
LLM06 — Unbounded Consumption
LLM07 — Misinformation
LLM08 — Hidden Context Exposure
LLM09 — Vector and Embedding Weaknesses
LLM10 — Improper Output Handling

Do not memorize the names without understanding the architecture.

Instead learn:

Risk
  ↓
Where Does It Exist?
  ↓
What Can Exploit It?
  ↓
What Asset Is Affected?
  ↓
What Is the Business Impact?
  ↓
What Prevents It?
  ↓
How Do We Detect It?

The most important lesson is:

OWASP gives you a structured way to think about LLM security, but secure AI requires combining OWASP with architecture review, threat modeling, IAM, application security, cloud security, data security and monitoring.

What’s Next?

➡️ 08 — MITRE ATLAS

OWASP helps us understand important categories of AI application security risk.

The next lesson changes perspective.

Instead of asking:

What vulnerabilities should we look for?

we will ask:

How might an adversary actually attack an AI system?

You will learn how MITRE ATLAS helps security teams understand adversarial behavior against Artificial Intelligence and Machine Learning systems.

You will explore:

What MITRE ATLAS is
Why it exists
AI adversary tactics
AI attack techniques
Reconnaissance
Initial access
ML supply-chain compromise
Data poisoning
Model manipulation
Prompt injection
Model extraction
Defense evasion
Exfiltration
Impact
Mapping AI attack paths
Using ATLAS with threat modeling
Using ATLAS with OWASP
Using ATLAS during AI security assessments

You will move from:

OWASP
"What security risks exist?"
        ↓
MITRE ATLAS
"How might an adversary attack?"

Together, these frameworks will give you a much stronger foundation for the later AI Threat Modeling and AI Red Teaming modules.

➡️ Next: 08 — MITRE ATLAS