OWASP Top 10 for LLM Applications 2025: What It Really Means for Teams Deploying Ai features

As organizations embed AI copilots, assistants, and automation into nearly every workflow, something important is becoming clear: LLMs introduce completely new security risks that traditional AppSec was never designed to handle. The newly released OWASP Top 10 for LLM Applications 2025 is the strongest signal yet that AI security is entering its own era—one where text, prompts, tools, plugins, embeddings, and datasets merge into an attack surface unlike anything before.

But the true value of the OWASP list isn’t in the definitions—it’s in understanding how these risks translate into real operational exposure, and what organizations must adjust now.

The OWASP Top 10 for LLM Applications

OWASP highlights these ten risks as the most critical for modern AI deployments:

1. Prompt Injection

2. Sensitive Information Disclosure

3. Supply Chain Vulnerabilities

4. Data & Model Poisoning

5. Improper Output Handling

6. Excessive Agency

7. System Prompt Leakage

8. Vector & Embedding Weaknesses

9. Misinformation Risks

10. Unbounded Consumption

These are not theoretical risks—they reflect real incidents, exploited weaknesses, and emerging adversarial techniques.

What These Risks Actually Mean for Your AI Strategy

Prompt Injection → The New “Remote Code Execution”

Prompt injection is now the #1 way attackers influence LLM behavior. Hidden instructions in text, documents, or images can silently redirect the model. Unlike SQLi, there’s no reliable “input filter” that solves this.

Practical takeaway: Build external guardrails; don’t expect the model to police itself.

Sensitive Information Disclosure → LLMs Remember More Than You Think

LLMs may leak patterns, training data, internal system prompts, or prior conversations.

Practical takeaway: Sanitize logs, enforce zero retention, and never place secrets in prompts.

Supply Chain & Poisoning → Your Model Sources Are Your Weakness

Organizations increasingly import pre-trained models, LoRA adapters, datasets, and embeddings from third parties. Many are unvetted, outdated, or compromised.

Practical takeaway: Maintain a model SBOM and perform model integrity checks.

Improper Output Handling → Treat LLM Output Like Raw User Input

LLM outputs can become SQL queries, file paths, HTML, or system commands. Without validation, this leads to SQLi, XSS, SSRF, or even RCE.

Practical takeaway: All LLM output must be validated before hitting downstream systems.

Excessive Agency & Tool Use → Overpowered AI Assistants

When an LLM can call APIs, perform actions, or interface with tools, hallucinations or attacks become dangerous.

Practical takeaway: Only expose minimal, highly-scoped actions.

RAG & Embeddings → Attackers Can Poison the “Truth” You Feed the Model

Embedding stores and RAG pipelines can be manipulated to serve false or malicious context.

Practical takeaway: Validate retrieval sources; never trust embeddings blindly.

Summary: AI Security Must Mature Before AI Deployment Scales

The OWASP Top 10 makes one thing clear: LLMs must be treated as untrusted components, with strict boundaries, least-privilege policies, detailed monitoring, and aggressive red teaming. As AI adoption accelerates, the organizations that succeed will be the ones that build these controls early—before attackers exploit the gaps.