Our Solutions
AI/LLM Evaluation
Conducting an AI/LLM Evaluation before deployment ensures compliance with security standards such as the OWASP Top 10 and Mitre Atlas framework. This assessment identifies vulnerabilities, tests resilience to attacks, and ensures safe deployment by mitigating data leaks, adversarial inputs, and misuse.
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OWASP Top 10 for LLM
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Mitre Atlas
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AI/LLM guardrail evaluation
Why organizations need an AI/LLM Evaluation
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Protect Against Data Exposure
Improper handling of sensitive data can result in unauthorized access or breaches, jeopardizing user privacy and data integrity.
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Maintain Model Integrity
Unauthorized modifications to the model can affect its accuracy and reliability, leading to potentially harmful or misleading outputs.
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Prevent Bias in AI Outcomes
Models may unintentionally generate biased results if trained on skewed data, potentially leading to unfair or discriminatory outcomes.
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Control Access
Inadequate access controls can permit unauthorized users to interact with or alter the model, increasing the risk of misuse or security breaches.
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Defend Against Misuse
Models can be compromised by adversarial inputs that cause unpredictable behavior, undermining their effectiveness and security.
What to expect
Scope and Planning
We work with your team to define the objectives for sensitive data exposure, access controls, and compliance standards to establish scope.
Architectural Review
We conduct a security-focused architectural review of LLMs to identify vulnerabilities, ensure compliance, and enhance resilience against potential threats.
Functional testing of AI/LLM
We utilize industry-standard tools and processes, such as OWASP and MITRE, to test the model for vulnerabilities and exposure of sensitive information.
Reporting and Remediation
We deliver a comprehensive report to document findings and provide actionable security improvements.
Secure Innovation: The Value of an AI/LLM Evaluation
AI adoption is accelerating, but so are the risks. From data leaks and bias to adversarial inputs and misuse, large language models introduce unique security challenges.
In this video, discover how an AI/LLM Evaluation helps you proactively identify vulnerabilities, meet compliance standards, and deploy with confidence.
Pulsar Security brings offensive expertise and cutting-edge testing to every engagement, delivering clarity, assurance, and a stronger foundation for safe AI innovation.
Frequently Asked Questions
Do you perform brute force or Distributed Denial of System (DDoS) attacks?
The goal of AI/LLM tests is to assess the security posture of the model itself, rather than testing the availability of its underlying infrastructure. Therefore, we do not conduct brute force or DDoS attacks as part of these assessments.
Why is it important to assess the security of an LLM?
Evaluating the security of an LLM is crucial for several reasons, including protecting data, maintaining model integrity, and ensuring robust access controls.
How are the findings from the LLM security assessment reported?
Findings are documented in a detailed report that includes identified vulnerabilities, data protection issues, compliance status, and actionable recommendations for improving the model’s security and performance.
How often should should we conduct an LLM security assessment?
It is recommended to conduct security assessments periodically or whenever significant updates or changes are made to the LLM to ensure ongoing protection and compliance.
