Large Language Models (LLMs) are shaking up industries left and right, but they also open a brand-new frontier for attackers. And no, I’m not talking about some far-off dystopian scenario. The risks are here, right now. We’re talking about data leakage, manipulation via prompt injection, or even adversarial attacks that could completely compromise your systems. If you’re not actively addressing these risks, you’re just hoping for the best (and hope is not a security strategy).

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That’s why the 2025 OWASP Top 10 for LLMs is critical. It’s a tactical playbook for anyone serious about keeping their AI systems secure. This framework discusses the unique vulnerabilities LLMs introduce and gives you the tools to deal with them.

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But most organizations don’t even know where they’re vulnerable because they haven’t thought of AI security as a standalone priority. You can’t just slap a firewall on this problem, security needs to be embedded into the very DNA of your AI systems.

Table of Contents

1. Why you need to care about the OWASP Top 10 for LLMs in 2025
2. What you need to know about the NEW 2025 OWASP Top 10 for LLMs
3. Why LLM Security risks are a big deal for your enterprise
4. How to secure your LLM deployments
5. The future is GenAI, but only if it’s secure

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Why you need to care about the OWASP Top 10 for LLMs in 2025

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OWASP (Open Worldwide Application Security Project) has been the gold standard for securing software. If you’ve ever wondered, “What’s the best way to prevent my applications from becoming hacker bait?” OWASP has the answer. For years, their Top 10 lists have been the go-to resource for identifying the most critical vulnerabilities in software. Now, they’re tackling Large Language Models (LLMs), and trust me, this isn’t just another security checklist.

OWASP’s role in securing software and AI apps

OWASP’s mission is simple but critical. It’s to make software safer. They’ve been the voice of reason in the chaos of vulnerabilities, setting the benchmark for secure coding and application practices. But as we step into the AI era, OWASP isn’t just sticking to traditional code. They’re looking ahead to where AI systems, especially LLMs, are changing the game and opening new attack vectors.And let’s be real, LLMs aren’t simply software.

They’re complex systems capable of processing natural language, embedding data, and even making decisions. That’s a massive leap from your average app, and with that leap comes brand-new security challenges.

Why evolving AI Risks demand immediate attention

AI isn’t static. Multimodal AI (where text, images, and other data streams interact) and embedding-based architectures are creating opportunities for innovation and brand-new headaches. These systems are incredibly powerful but also dangerously unpredictable. They don’t just fail; they fail creatively, which makes it harder to anticipate risks.

For instance, your traditional apps don’t deal with users who might manipulate their inputs to poison your AI’s training data. LLMs do. They’re also vulnerable to adversarial attacks that exploit their probabilistic nature, such as maliciously crafted prompts or hidden data injections. OWASP’s focus is on these AI-specific risks because, frankly, the old security playbook isn’t enough anymore.

LLM-specific risks vs. Traditional software vulnerabilities

1. Data poisoning: Attackers can corrupt the training data to make the AI’s outputs biased. In traditional software, you deal with bugs; in LLMs, you deal with intentionally injected bias.
2. Prompt injection attacks: This is a big one. A malicious input can cause the LLM to generate harmful or sensitive outputs without you even realizing it. Regular apps don’t have to deal with such dynamic risks.
3. Embedding exploits: LLMs often rely on embeddings to store semantic relationships. These embeddings can be hijacked or manipulated, which causes the model to “misunderstand” data.
4. Overexposure of sensitive data: Unlike a regular app, which follows strict data access rules, an LLM might inadvertently output sensitive data it’s been trained on.
5. Adversarial inputs: LLMs can be fooled into making dangerous decisions by feeding them cleverly designed inputs. This is a direct attack on the AI’s trustworthiness.

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What you need to know about the NEW 2025 OWASP Top 10 for LLMs

The 2025 OWASP Top 10 for LLMs is a critical update to deal with how these models are being attacked today. If you’re betting big on AI, then you have to read these updates. Here’s what’s new or enhanced and why it matters.

Prompt Injection (LLM01:2025)

Prompts are the instructions that tell your AI what to do, but attackers can manipulate these prompts to make your LLM do things it shouldn’t.

What’s new?

The 2025 version expands on multimodal attacks. This means that vulnerabilities don’t just come from text but also from images or combinations of both. This could be an attacker embedding malicious commands in a QR code or image that the LLM processes. It’s a whole new playground for exploits.

Why it matters:

For enterprises, it’s all about governance. You need clear rules and guardrails to make sure your AI isn’t exploited for fraud, misinformation, or worse. If you don’t get ahead of this, your brand and compliance risks could spiral.

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Sensitive Information Disclosure (LLM02:2025)

LLMs can inadvertently spill sensitive data, even when you don’t expect it.

What’s new?

There’s a stronger focus on unintentional leaks. For example, if someone feeds proprietary or customer data into the model, that information might resurface in unrelated outputs later.

Why it matters:

This has a lot to do with compliance like GDPR, HIPAA, or similar regulations. If sensitive data leaks through your LLM, you’re staring at major fines and trust issues. You need strict data validation strategies to ensure your models are clean and only return safe, approved outputs.

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Unbounded Consumption (LLM10:2025)

Deploying LLMs at scale is a resource management nightmare if you’re not careful.

What’s new?

This category highlights the risks of resource exhaustion. Whether it’s excessive computing costs or systems grinding to a halt due to overwhelming queries, the stakes are high for enterprises.

Why it matters:

Unbounded consumption hits both your budget and disrupts your operations. Enterprises need to implement usage caps, rate limiting, and smart resource allocation to prevent runaway costs or system downtime.

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System Prompt Leakage (LLM07:2025)

Your LLM’s system prompt is its core set of instructions, the guardrails that tell it how to behave. If these prompts are leaked, attackers can reverse-engineer them to exploit your system.

What’s new?

The 2025 update focuses on real-world exploits where attackers extract system prompts and use them to manipulate models into unsafe behavior.

Why it matters:

For businesses, compromised prompts mean two things: reputational harm (your AI acts unpredictably) and security vulnerabilities (it can be weaponized). Enterprises need clear mitigation strategies, like red-teaming and prompt obfuscation, to prevent leakage.

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Vector and Embedding Weaknesses (LLM08:2025)

Embeddings power modern AI, storing data in ways that the model can “understand.” But they come with risks.

What’s new?

The update emphasizes Retrieval-Augmented Generation (RAG) and how embedding-based architectures can be exploited, such as poisoned embeddings where an attacker manipulates what the model “learns” from a dataset.

Why it matters:

If you’re relying on RAG for critical systems, these weaknesses can impact search accuracy, inject malicious data, or cause your AI to generate harmful outputs. To mitigate these risks, you need robust validation and monitoring at every step.

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Why LLM Security risks are a big deal for your enterprise

Let’s talk about the real-world impact of LLM vulnerabilities. They can mess with your operations, drain your budget, and even put your company on the wrong side of the law. If you’re using LLMs in any part of your business, ignoring these issues is only delaying the inevitable.

Operational Risks

When an LLM vulnerability is exploited, it will affect your entire operation. A compromised system disrupts workflows, damages your reputation, and creates long-term inefficiencies.

What’s at stake?

• Customer trust. If your AI goes rogue or leaks sensitive data, your customers will lose confidence in your ability to protect them. Once trust is broken, it’s nearly impossible to regain.
• Business disruptions. Exploits like resource exhaustion can render critical LLM-powered services unusable. Every minute of downtime costs money and customer goodwill.

What to do: Build redundancy into your systems and continuously stress-test your LLM deployments to guarantee they can handle abuse without crashing your business.

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Financial Risks

Security breaches involving LLMs are expensive. Between remediation, lawsuits, and lost customers, the financial blow can be devastating.

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What you’re really paying for:

• Regulatory fines. GDPR and AI Act penalties can run into millions if your AI leaks sensitive data. And regulators aren’t exactly known for leniency.
• Legal fees and settlements. A class-action lawsuit from affected customers or partners can drag your finances down for years.
• Lost business opportunities. Reputation damage scares off customers and makes potential partners and investors hesitant about getting involved with your business.

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‍What to do: Quantify the financial impact of a breach now. Treat LLM security investments as cost savings instead of an expense.

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Regulatory Risks

Governments and regulators worldwide are putting AI under the microscope. The EU’s AI Act, GDPR, and other regulations are setting the stage for strict compliance standards. Failing to secure your LLMs could make your life so much more difficult.

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The risks look like this:

• Data protection laws. If an LLM leaks sensitive data, you’re looking at fines under GDPR or similar laws.
• AI governance. The AI Act and other emerging frameworks will require enterprises to prove that their AI systems are safe, secure, and ethical. Falling short could mean losing access to key markets.

‍What to do: Position AI security as a differentiator. Show your stakeholders, as well as customers, partners, and investors, that you’re using AI responsibly and securely.The strategic risks of LLM vulnerabilities are unfolding in real time. From customer trust to financial stability and compliance headaches, the risks are too big to ignore.

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How to secure your LLM deployments

Now that we’ve unpacked the risks, let’s talk about solutions. If you’re deploying Large Language Models (LLMs) across your enterprise, these actionable recommendations are essential to keep your AI secure, reliable, and compliant.

Proactive measures to stop problems before they start

1. Use secure defaults from day one. Don’t wait for an exploit before you start securing your settings. Configure your LLMs with secure defaults right out of the gate. That means things like enforcing strict authentication, rate-limiting access, and securing unnecessary features.
2. Regularly audit AI pipelines. Your AI pipeline, from training data to deployment, is a high-value target. Conduct regular vulnerability scans and audits to catch issues before attackers do. Review everything: training data integrity, model behavior, and deployment configurations.
3. Monitor continuously. Don’t rely on “set it and forget it.” Use AI monitoring tools to track unusual patterns or anomalies in real-time to make sure that any potential breaches are identified immediately.

Technical strategies to control what your models can and can’t do

1. Constrain model behavior. Define strict rules for how your LLM interacts with inputs. Use tools to enforce prompt adherence and prevent malicious instructions from overriding the system’s behavior.
2. Validate outputs with deterministic code. Don’t blindly trust your LLM outputs. Use deterministic checks to validate its results. If it generates something outside the expected range or format, flag it for review.
3. Adopt semantic filtering and privilege control. Filter model outputs for sensitive or harmful content using semantic analysis. Combine this with privilege controls to restrict what the model can access and expose during interactions.
4. Secure embeddings and RAG pipelines. If you’re using Retrieval-Augmented Generation (RAG), validate your embeddings to guarantee that no malicious data sneaks into your systems. Regularly check your retrieval processes for vulnerabilities.

Enterprise collaboration to build a culture of AI security

1. Build cross-functional teams. Align your development, operations, and security teams under DevSecOps principles. Regular collaboration keeps security baked into every phase of your AI lifecycle.
2. Invest in security training. Your teams need to stay sharp. Partner with platforms like AppSecEngineer to upskill your developers and security teams on the latest LLM vulnerabilities and mitigation strategies. Training is a continuous process to keep up with new threat vectors.
3. Engage vendors and partners. Work closely with your LLM providers to understand their security roadmap and make sure that they’re prioritizing updates and patches. Your vendors are part of your security ecosystem, hold them accountable.

Additional recommendations to stay future-ready

1. Adopt AI governance policies. Set clear rules for how AI should be deployed, accessed, and monitored in your organization. Governance is for compliance, consistency, and risk management.
2. Test with red teams. Regularly simulate attacks using red-teaming exercises to identify weak spots in your LLM deployments. Better to catch vulnerabilities yourself than let attackers find them.
3. Plan for incident response. Have a robust AI-specific incident response plan. If an LLM is compromised, you need clear steps for containment, remediation, and communication.

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The future is GenAI, but only if it’s secure

Right now, having secure AI is a must have. When you secure your AI systems early, you're showing everyone that you mean business. Your customers will trust you more, and your teams can build cool stuff without worrying about security problems.

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But, waiting is risky.  If something goes wrong with your AI, it's going to hurt. We're talking about damaged trust, big fines from regulations like GDPR and the AI Act, and major problems with your day-to-day work. And fixing all that? Way more expensive than getting security right from the start.

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AI is changing everything in business, but only if we keep it safe. Want to learn how? Join us on February 12, 2025 at 9 AM PST for LLM Secure Coding - The Unexplored Frontier webinar. We'll show you how to protect your AI using the latest OWASP Top 10 for LLMs - 2025 framework.

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FAQs

What is the OWASP Top 10 for LLMs, and why is it important?

The OWASP Top 10 for LLMs - 2025 is a list of the most critical vulnerabilities and risks specific to Large Language Models. It provides a framework to help enterprises understand, address, and mitigate the unique security challenges posed by AI systems. It’s essential for ensuring your AI deployments are secure, compliant, and resilient against attacks.

What are some examples of risks in the OWASP Top 10 for LLMs?

Key risks include:

• Prompt Injection: Malicious inputs that manipulate an LLM’s behavior.
• Sensitive Information Disclosure: Unintentional leaks of proprietary or customer data.
• System Prompt Leakage: Exposure of internal instructions that could be exploited by attackers.
• Unbounded Consumption: Resource exhaustion risks in large-scale deployments.

These risks are unique to LLMs and require specific mitigation strategies.

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How do LLM vulnerabilities differ from traditional software vulnerabilities?

Unlike traditional software, LLMs are dynamic and context-driven. Their vulnerabilities often involve misuse of natural language inputs, adversarial manipulation, or unintended consequences of their probabilistic nature. For example, traditional software vulnerabilities might involve static code bugs, whereas LLM vulnerabilities could stem from malicious user prompts or poisoned training data.

Why should decision-makers care about securing LLMs?

LLM security isn’t just a technical concern—it’s a business-critical issue. Unsecured LLMs can lead to data breaches, regulatory fines, operational disruptions, and reputational damage. Decision-makers who proactively secure their AI systems gain a competitive edge by building customer trust and demonstrating leadership in responsible AI deployment.

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What is the business impact of delayed action on LLM security?

Delaying action can lead to significant financial and operational consequences, including:

• Regulatory fines from non-compliance with GDPR, AI Act, and other standards.
• Reputational damage caused by data breaches or unreliable AI outputs.
• Increased operational costs due to resource exhaustion or system downtime.

Taking a reactive approach is far more expensive than investing in proactive security

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How can enterprises start addressing LLM security risks?

Enterprises can start by:

• Implementing secure defaults for LLM deployments.
• Regularly auditing AI pipelines for vulnerabilities.
• Constraining model behavior through strict prompt adherence.
• Training cross-functional teams to integrate security into the AI lifecycle.

Additionally, using the OWASP Top 10 framework as a guide ensures a comprehensive approach to risk mitigation.

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Are there tools or platforms to help with LLM security?

Yes, there are several tools and best practices to secure LLMs:

• Monitoring tools: Track anomalies in real time.
• Filtering systems: Semantic filtering to prevent harmful or sensitive outputs.
• Training platforms: Tools like AppSecEngineer help upskill teams with hands-on training for LLM security.

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What happens if LLM vulnerabilities are left unaddressed?

Unaddressed vulnerabilities can lead to:

• Exploits like prompt injection that manipulate outputs.
• Leaks of proprietary data, causing reputational and financial damage.
• Operational inefficiencies from resource exhaustion.

Ignoring these issues increases the risk of cascading failures across your systems.

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How does the OWASP Top 10 align with regulatory compliance?

The OWASP Top 10 provides a structured approach to securing LLMs, helping organizations meet the requirements of regulations like GDPR, HIPAA, and the AI Act. By addressing risks proactively, enterprises can ensure compliance and avoid costly penalties.

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