OWASP Top 10 for LLM Applications: What Enterprises Need to Know
Why LLM Security Is Different
Large Language Models introduce a fundamentally new attack surface. Unlike traditional applications where inputs are structured and predictable, LLMs accept natural language — making them vulnerable to manipulation techniques that don't exist in conventional software security.
The OWASP Top 10 for LLM Applications (v1.1) provides a framework for understanding and mitigating these risks.
The Top 10 Risks
LLM01: Prompt Injection
The most discussed and most dangerous risk. Attackers craft inputs that override the model's instructions, causing it to ignore safety guardrails, leak system prompts, or execute unintended actions.
Mitigation: Input validation, prompt/response sandboxing, privilege separation between model and tools.
LLM02: Insecure Output Handling
LLM outputs are treated as trusted by downstream systems. If a model generates SQL, HTML, or API calls, these can become injection vectors — SQL injection, XSS, and command injection through the model.
Mitigation: Treat all model outputs as untrusted. Apply the same sanitisation you'd apply to user input.
LLM03: Training Data Poisoning
If attackers can influence training data — through public datasets, fine-tuning datasets, or RAG knowledge bases — they can embed backdoors or biases that persist in the model's outputs.
Mitigation: Data provenance tracking, input validation for RAG sources, regular knowledge base audits.
LLM04: Model Denial of Service
Crafted inputs can cause models to consume excessive compute — long context windows, recursive reasoning loops, or resource-exhaustive queries.
Mitigation: Rate limiting, input length restrictions, timeout policies, cost monitoring.
LLM05: Supply Chain Vulnerabilities
Pre-trained models, third-party plugins, and training datasets introduce supply chain risk. A compromised model weight file or a malicious LangChain plugin can compromise your entire AI stack.
Mitigation: Model provenance verification, dependency scanning, vendor security assessments.
LLM06: Sensitive Information Disclosure
Models can inadvertently reveal PII, proprietary data, or system architecture details present in training data or retrieved context.
Mitigation: Data classification before ingestion, output filtering, PII detection on responses.
LLM07: Insecure Plugin Design
LLM plugins and tool-use capabilities (function calling) can execute actions with excessive permissions — reading files, querying databases, making API calls.
Mitigation: Least-privilege principle for all tools, human-in-the-loop for destructive actions, input validation on tool parameters.
LLM08: Excessive Agency
When LLMs are given too much autonomy — chaining multiple tools, making decisions without oversight — the blast radius of a single error or attack multiplies.
Mitigation: Scope limitations, approval workflows for high-impact actions, audit logging.
LLM09: Overreliance
Users and organisations trust model outputs without verification, leading to decisions based on hallucinated or incorrect information.
Mitigation: Confidence scoring, citation requirements, human review for high-stakes decisions.
LLM10: Model Theft
Trained models represent significant intellectual property. API-based model extraction, side-channel attacks, and insufficient access controls can expose proprietary models.
Mitigation: API rate limiting, watermarking, access controls, monitoring for extraction patterns.
What Should Enterprises Do?
- Assess your current AI systems against the OWASP LLM Top 10
- Classify risk — not all deployments carry equal risk. Customer-facing chatbots need more hardening than internal summarisation tools
- Build security into the development lifecycle — don't bolt it on after deployment
- Test continuously — AI red-teaming should be recurring, not one-off
How SmartGenie and Cygeniq Help
Our AI Security Assessment service, delivered jointly with our strategic partner Cygeniq, evaluates your AI systems against the OWASP LLM Top 10 and provides prioritised remediation guidance.
Get assessed today — before an attacker does it for you.