Discover how Lakera's security solutions correspond with the OWASP Top 10 to protect Large Language Models (2025), as we detail each vulnerability and Lakera's strategies to combat them.
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The OWASP Top 10 for LLM Applications has become the most widely referenced framework for understanding and mitigating risks in generative AI systems. The 2025 edition brings important updates that reflect the growing complexity of real-world threats: from model training vulnerabilities to deployment-stage attacks and misuse in production environments.
Lakera has been closely involved in shaping this evolving security landscape. We’ve contributed to several OWASP initiatives, including the Top 10 for LLMs (2025) and the AI Vulnerability Scoring System (AIVSS), helping define how risks should be prioritized and addressed in production-grade AI systems.
Lakera’s alignment with the OWASP framework goes beyond theory. We’ve operationalized it across our entire security approach:
Together, they help teams ship secure, compliant, and trustworthy GenAI applications, backed by the industry’s leading standards.
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Lakera helps secure generative AI applications by aligning with the OWASP Top 10 for LLMs (2025). Our focus spans two key stages:
While we provide strong coverage across most OWASP risks, certain areas, like supply chain vulnerabilities, fall outside the scope of runtime protections and are only partially addressed through model behavior evaluation.
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The 2025 OWASP LLM Top 10 reflects a key insight—
LLM risks don’t just show up at runtime. They emerge across the entire AI lifecycle, from the moment you ingest training data to how the model responds to user input in production.
This means that securing LLMs requires both proactive and reactive defenses. You need to uncover vulnerabilities before deployment and stay protected once your application is live.
That’s where Lakera comes in:
In the next section, we’ll map each OWASP risk to Lakera’s coverage, so you can see exactly how these threats are addressed across development and deployment.
Risk Impact: Bypasses safety measures, exposes sensitive data, and enables unauthorized system access.
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<thead>
<tr>
<th>Coverage</th>
<th>Level</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lakera Guard</td>
<td>🟢 Strong</td>
<td>Prompt Attack detector identifies direct, indirect, and jailbreak attempts in real time.</td>
</tr>
<tr>
<td>Lakera Red</td>
<td>🟢 Strong</td>
<td>Continuous red-team testing for injection vulnerabilities across attack vectors.</td>
</tr>
</tbody>
</table>
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**💡 Want a deeper dive into how attackers exploit LLMs? Read our guide to prompt injection.**
Risk Impact: Leads to PII leakage, proprietary data exposure, and privacy violations.
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<table>
<thead>
<tr>
<th>Coverage</th>
<th>Level</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lakera Guard</td>
<td>🟢 Strong</td>
<td>Data Leak detector identifies PII patterns; custom guardrails and regex enable precise proprietary data protection.</td>
</tr>
<tr>
<td>Lakera Red</td>
<td>🟢 Strong</td>
<td>Systematic evaluation of data exposure scenarios, including PII extraction.</td>
</tr>
</tbody>
</table>
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**💡 Learn how protecting personal data is evolving in the age of GenAI in our post o personally identifiable information risks.**
Risk Impact: Compromised models, vulnerable dependencies, and licensing issues.
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<table>
<thead>
<tr>
<th>Coverage</th>
<th>Level</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lakera Guard</td>
<td>⚪ Not Applicable</td>
<td>Outside runtime guardrail scope.</td>
</tr>
<tr>
<td>Lakera Red</td>
<td>🟡 Limited</td>
<td>Can evaluate model behavior but not supply chain integrity.</td>
</tr>
</tbody>
</table>
</div>
Risk Impact: Introduces bias, backdoors, and compromised output quality.
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<table>
<thead>
<tr>
<th>Coverage</th>
<th>Level</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lakera Guard</td>
<td>🟢 Strong</td>
<td>Prompt Attack detector identifies poisoning triggers; custom guardrails detect harmful outputs.</td>
</tr>
<tr>
<td>Lakera Red</td>
<td>🟢 Strong</td>
<td>Evaluates model behavior for poisoning indicators and backdoor activation.</td>
</tr>
</tbody>
</table>
</div>
**💡 Prompt engineering and adversarial inputs often expose poisoned behaviors. See our advanced prompt engineering guide to understand the techniques attackers use, and how to counter them.**
Risk Impact:Enables remote code execution (RCE), XSS, SQLi, and phishing through unsanitized outputs.
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<table>
<thead>
<tr>
<th>Coverage</th>
<th>Level</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lakera Guard</td>
<td>🟡 Limited</td>
<td>Can detect suspicious input patterns, but full prevention requires proper system integration design.</td>
</tr>
<tr>
<td>Lakera Red</td>
<td>🟢 Strong</td>
<td>Tests for dangerous output patterns including executable code and injection payloads.</td>
</tr>
</tbody>
</table>
</div>
Risk Impact: Over-permissioned systems, unauthorized actions, and privilege escalation.
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<table>
<thead>
<tr>
<th>Coverage</th>
<th>Level</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lakera Guard</td>
<td>🟡 Limited</td>
<td>Primarily a design/architecture issue; Prompt Attack detector identifies exploitation attempts.</td>
</tr>
<tr>
<td>Lakera Red</td>
<td>🟢 Strong</td>
<td>Evaluates agent behavior and permission boundaries through systematic testing.</td>
</tr>
</tbody>
</table>
</div>
**💡 To learn more about securing agentic AI systems, explore Lakera’s Guide to Securing AI Agents in Production, packed with tactical guidance and grounded in real deployments.**
Risk Impact: Exposes internal functionality, rules, and security controls.
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<table>
<thead>
<tr>
<th>Coverage</th>
<th>Level</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lakera Guard</td>
<td>🟢 Strong</td>
<td>System prompt leakage defenses prevent extraction in real-time.</td>
</tr>
<tr>
<td>Lakera Red</td>
<td>🟢 Strong</td>
<td>Systematic testing for prompt extraction using advanced techniques.</td>
</tr>
</tbody>
</table>
</div>
Risk Impact: Unauthorized RAG data access, cross-context leaks, and embedding-based attacks.
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<table>
<thead>
<tr>
<th>Coverage</th>
<th>Level</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lakera Guard</td>
<td>🟢 Strong</td>
<td>Data Leak detector protects PII and embedding data; custom guardrails for context-specific protection.</td>
</tr>
<tr>
<td>Lakera Red</td>
<td>🟡 Limited</td>
<td>Can test RAG-accessible vulnerabilities but with limited scope.</td>
</tr>
</tbody>
</table>
</div>
Risk Impact: Generates false information, hallucinations, or biased outputs that affect decision-making.
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<table>
<thead>
<tr>
<th>Coverage</th>
<th>Level</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lakera Guard</td>
<td>🟢 Strong</td>
<td>Content Moderation detector identifies harmful or biased outputs; custom guardrails enforce factuality standards.</td>
</tr>
<tr>
<td>Lakera Red</td>
<td>🟢 Strong</td>
<td>Systematic evaluation of factual accuracy, groundedness, and bias patterns.</td>
</tr>
</tbody>
</table>
</div>
Risk Impact: DoS attacks, resource exhaustion, model theft, and financial impact.
<div class="table_component" role="region" tabindex="0">
<table>
<thead>
<tr>
<th>Coverage</th>
<th>Level</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lakera Guard</td>
<td>🟢 Strong</td>
<td>Custom guardrails detect suspicious usage patterns and potential abuse indicators.</td>
</tr>
<tr>
<td>Lakera Red</td>
<td>🟢 Strong</td>
<td>Comprehensive testing for resource exhaustion, model extraction, and consumption-based attacks.</td>
</tr>
</tbody>
</table>
</div>
**💡 Curious how red teaming for GenAI differs from traditional pen testing? Read how we’re redefining AI red teaming to meet the unique demands of LLM security.**
The most effective AI security strategies address vulnerabilities before, during, and after deployment. Here’s how Lakera enables that level of coverage:
Whether you’re building GenAI apps from scratch or scaling to production, Lakera helps you stay secure at every step.
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Discover risks and solutions with the Lakera LLM Security Playbook.
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