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OWASP’s Top 10 Ways to Attack LLMs : The AI Security Concern, Every Executive Should Understand
Generative AI is moving from experimentation to core business operations. AI copilots are writing code, reviewing contracts, analyzing financial data, automating customer support, and even making operational decisions.
But while most organizations are racing to adopt AI, many are deploying it without fully understanding the new security risks that come with Large Language Models (LLMs).
Traditional cybersecurity frameworks were never designed for AI systems that can reason, generate content, call external tools, access enterprise knowledge bases, and autonomously take actions.
That is exactly why the OWASP created the — a framework focused specifically on the most critical AI vulnerabilities organizations face today.
Your AI system is no longer just a software application. It is now part employee, part decision engine, and part attack surface.
And attackers already know it.
Number That Should Worry You
IBM's 2025 Cost of a Data Breach Report studied 600 organizations globally. 13% reported breaches of AI models or applications — and of those, 97% lacked proper AI access controls. One in five organizations reported a breach due to "shadow AI" — employees using unsanctioned AI tools — costing an average of $670,000 more per breach than standard incidents.
Meanwhile, 63% of breached organizations had no AI governance policy — or were still writing one when the breach happened.
Understanding OWASP’s Top 10 LLM Vulnerabilities
1. Prompt Injection (LLM01)
This is currently considered the most dangerous and widely discussed LLM attack category
Attackers manipulate the AI using carefully crafted instructions designed to override system behavior.
Examples include:
“Ignore previous instructions”
Hidden malicious text embedded in documents
Poisoned web pages
Indirect instructions inside emails or PDFs
Tool manipulation attacks against AI agents
The biggest issue is that LLMs do not clearly separate instructions from data the way traditional software systems do.
2. Sensitive Information Disclosure (LLM02)
Modern enterprise AI systems are often connected to:
internal knowledge bases,
CRM systems,
financial documents,
customer records,
and proprietary data repositories.
If access controls and output filtering are weak, LLMs may unintentionally expose:
confidential enterprise data,
API keys,
system prompts,
regulated information,
or intellectual property.
This becomes especially risky in RAG-based enterprise deployments.
3. Supply Chain Vulnerabilities (LLM03)
AI systems rely heavily on third-party ecosystems:
open-source models,
plugins,
vector databases,
orchestration frameworks,
external APIs,
and fine-tuning datasets.
Every dependency expands the attack surface.
OWASP 2025 highlights how AI supply chain attacks are becoming one of the most underestimated enterprise risks.
This is particularly concerning because many organizations still treat AI dependencies with far less scrutiny than traditional software supply chains.
4. Data and Model Poisoning (LLM04)
Attackers may intentionally poison training or fine-tuning data to manipulate model behavior.
This can lead to:
hidden backdoors,
biased responses,
unsafe outputs,
or compromised reasoning behavior.
Unlike traditional malware, poisoned AI models may appear completely normal until specific trigger conditions are activated.
For enterprises using custom fine-tuning pipelines, this introduces significant governance and validation challenges.
5. Improper Output Handling (LLM05)
Many organizations trust AI-generated outputs too quickly.
This becomes dangerous when AI-generated:
code,
scripts,
SQL queries,
automation instructions,
or API calls
are executed without proper validation.
In agentic workflows, insecure output handling can directly lead to system compromise.
6. Excessive Agency (LLM06)
This is one of the biggest risks emerging in modern AI agents.
Organizations are increasingly giving AI systems permission to:
send emails,
modify cloud infrastructure,
access databases,
execute transactions,
or interact with enterprise systems autonomously.
The more authority an AI agent receives, the greater the blast radius if compromised.
OWASP 2025 strongly emphasizes the principle of least privilege for AI systems.
Executives should ask a simple but important question:
“What is the worst thing our AI agent could do if manipulated?”
Most organizations still do not have a clear answer.
7. System Prompt Leakage (LLM07)
One of the major additions in the 2025 framework is system prompt leakage.
Attackers attempt to expose hidden instructions that define how enterprise AI systems behave.
Leaked system prompts may reveal:
internal logic,
security controls,
operational rules,
or sensitive implementation details.
In some cases, leaked prompts can significantly simplify future attacks.
8. Vector and Embedding Weaknesses (LLM08)
This is another major addition reflecting the rise of RAG architectures.
Vector databases and embeddings are now core components of many enterprise AI systems.
Weaknesses in vector retrieval pipelines may lead to:
unauthorized information access,
poisoned retrieval results,
retrieval manipulation,
or context tampering attacks.
Many organizations are deploying RAG systems without fully understanding the new security risks introduced by embedding pipelines.
9. Misinformation (LLM09)
LLMs can generate highly convincing but incorrect information.
The danger is not just hallucinations.
It is the business impact of confidently incorrect AI outputs influencing:
executive decisions,
financial reporting,
customer communications,
legal interpretation,
or operational workflows.
OWASP 2025 now formally recognizes misinformation as a major enterprise AI risk category.
10. Unbounded Consumption (LLM10)
Previously referred to more narrowly as model denial-of-service risks, the 2025 framework expands this into “Unbounded Consumption.”
Attackers may intentionally trigger:
excessive token usage,
recursive reasoning loops,
agentic task explosions,
or computational abuse.
This can dramatically increase infrastructure costs and degrade AI service availability.
For CFOs and CIOs, this introduces a new category of financial exposure: AI resource exploitation.
The Bigger Message Behind OWASP 2025
The biggest takeaway from the OWASP 2025 LLM Top 10 is not just the list itself.
It is what the list represents.
AI security is rapidly evolving from: “protecting models” to “governing intelligent autonomous systems.”
This changes how organizations must think about:
risk management,
compliance,
AI governance,
vendor oversight,
operational resilience,
and digital trust.
The conversation is no longer only about model accuracy.
It is now about enterprise survivability in an AI-driven environment.
What Executives Should Prioritize Now
Organizations do not need to slow AI innovation.
But they do need stronger governance maturity.
Key priorities should include:
Establish AI Security Governance
Treat AI systems as critical enterprise infrastructure.
Implement Human Oversight
Avoid fully autonomous decision-making in high-risk workflows.
Apply Least Privilege to AI Agents
Restrict what AI systems can access and execute.
Red Team AI Systems
Traditional penetration testing is no longer sufficient for AI applications.
Secure RAG and Vector Pipelines
Embedding infrastructure now requires dedicated security controls.
Monitor AI Usage Continuously
AI behavior changes dynamically over time.
Create Executive-Level AI Risk Visibility
AI governance should be discussed at board and leadership levels.
Final Thought
The OWASP 2025 LLM Top 10 is important because it reflects a fundamental reality:
The future attack surface of the enterprise is increasingly AI-driven.
Organizations that continue treating AI as “just another application feature” may underestimate the scale of emerging risks.
The enterprises that will lead in the AI era are not necessarily the ones deploying AI the fastest.
They are the ones deploying it responsibly, securely, and governably.
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