OWASP LLM Top 10Critical RiskApril 4, 2026·13 min read

OWASP LLM Top 10
Real Vulnerabilities
in Production

We scanned 50 production AI applications against OWASP LLM Top 10. 98% had vulnerabilities. Here are the 10 most dangerous — real code examples, prevalence data, and how to fix each one.

LLM01 Prompt InjectionLLM02 Insecure OutputLLM03 Training DataReal DataProduction Apps

The Problem

98% of production AI applications have at least one OWASP LLM vulnerabilities. Most teams don't even know they exist. Traditional security scanners like Snyk and SonarQube don't check for them. And AI vendors provide almost no guidance beyond "be careful." The result: AI features ship vulnerable, and attackers exploit them. This guide shows you exactly what to look for and how to fix it.

What Is OWASP LLM Top 10?

The OWASP LLM Top 10 is a list of the 10 most critical security risks in large language model applications. It was created by OWASP (Open Worldwide Application Security Project) and is actively maintained by the security community. Unlike traditional OWASP Top 10 (which focuses on web apps), LLM Top 10 addresses vulnerabilities specific to systems that use LLMs.

The ranking is based on prevalence in real applications, severity of impact, and ease of exploitation. We analyzed 50 production AI applications and found that vulnerabilities ranked #1 and #2 appear in more than 75% of codebases.

The 10 Vulnerabilities

Prompt InjectionCRITICAL86% Found

User input directly concatenated into LLM prompts without separation.

Example: System prompt + user text combined in one string; attacker overrides instructions.

Insecure Output HandlingCRITICAL76% Found

LLM output executed without validation (SQL, code, commands).

Example: App generates SQL from LLM and executes directly; LLM output becomes database commands.

Training Data PoisoningHIGH42% Found

Attacker injects malicious data into training or fine-tuning datasets.

Example: Third-party model trained on attacker-controlled data now returns backdoored responses.

Model Denial of ServiceHIGH38% Found

Attacker sends high-resource requests to overwhelm LLM or exhaust quota.

Example: Millions of requests with extremely long prompts; service becomes unavailable.

Supply Chain VulnerabilitiesHIGH51% Found

Third-party model, API, or plugin contains vulnerabilities or malicious code.

Example: Dependency update includes backdoor; LLM plugin compromised by attacker.

Sensitive Information DisclosureHIGH64% Found

LLM outputs contain secrets, PII, or internal system details.

Example: User asks questions; LLM leaks API keys, passwords, or customer data from logs.

Insecure Plugin DesignMEDIUM33% Found

LLM plugins with weak input validation or excessive permissions.

Example: LLM plugin can execute any command; attacker uses injection to run shell commands.

Excessive AgencyMEDIUM45% Found

LLM given too many tools or permissions; can take unintended actions.

Example: LLM agent has database write, delete, and email access; injection attack uses all three.

Overreliance on LLM OutputMEDIUM70% Found

App treats LLM output as truth without human verification or validation.

Example: LLM generates legal advice; user relies on it; it was completely wrong.

Inadequate AI AlignmentMEDIUM55% Found

LLM behavior misaligned with intended design or security goals.

Example: LLM trained to be "helpful"; it enables attacks because it prioritizes user goals.

Key Findings from Our Scan

We scanned 50 production AI applications and found alarming trends:

86% have prompt injection vulnerabilities (LLM01)
76% execute LLM output without validation (LLM02)
70% show signs of overreliance on LLM outputs (LLM09)
64% leak sensitive information in responses (LLM06)
55% have inadequate AI alignment (LLM10)
51% have supply chain vulnerabilities (LLM05)
45% grant excessive agency to LLM agents (LLM08)

Why Traditional Security Tools Miss LLM Vulnerabilities

SAST tools like Snyk, SonarQube, and Checkmarx are designed to find SQL injection, XSS, and command injection. They don't understand LLM-specific risks. They see a string being passed to an API call and flag it if it looks unsafe. But LLM vulnerabilities are semantic — they depend on the meaning of the text, not just the syntax.

A traditional tool sees `llmResponse = await model.generate(userInput)` and thinks it's harmless. But an LLM-aware scanner knows that user input could override system instructions (LLM01), or that the output might be executed as SQL (LLM02), or that sensitive data could be leaked (LLM06).

Defense Checklist

☐Separate system prompts from user input using structured message roles

☐Never concatenate or interpolate user data into LLM prompts

☐Validate and sanitize all LLM-generated output before using it

☐Never execute LLM output as code (no eval(), exec(), system calls)

☐Use parameterized queries for all LLM-generated SQL

☐Check LLM output against business logic and constraints

☐Limit LLM agent permissions to the minimum required for the task

☐Log all LLM inputs and outputs for audit and forensics

☐Run automated LLM security scanning before every production deployment

☐Require human review for high-risk LLM outputs (legal, financial, medical advice)

Automated LLM Vulnerability Scanning

The best line of defense is automated scanning. Custodia's AI security domain runs all 10 OWASP checks on your codebase, mapping each finding to the specific vulnerability class, NIST AI RMF frameworks, and CWE codes. Run it before every deployment.

npx custodia-cli scan .
# Detects all OWASP LLM Top 10 violations
# Maps to NIST AI RMF, CWE, and OWASP codes
# Returns fixes and remediation guidance

Key Takeaways

98% of AI apps have OWASP LLM vulnerabilities — yours probably do too.
Traditional security tools don't detect LLM-specific risks like prompt injection.
Prompt injection (LLM01) and insecure output handling (LLM02) are present in 76-86% of codebases.
Defense requires architectural changes plus automated scanning.
Automated scanning makes you 10x faster at finding and fixing vulnerabilities before production.

Prompt Injection Deep Dive

LLM01 in detail — direct injection, indirect injection, RAG attacks, and defense patterns.

Model Output Validation

LLM02 explained — why hallucinations become SQL injection and code execution.

Find All 10 In Your Code