* The graph shows differences in the intensity of light received from different colored targets when a KEYENCE fiber optic sensor (red light) is used. It shows that combinations such as white and red, or orange and yellow are difficult to differentiate by simply looking at received light intensity only.

We utilize industry-standard tools and processes, such as OWASP and MITRE, to test the model for vulnerabilities and exposure of sensitive information.

A color sensor is a type of "photoelectric sensor" which emits light from a transmitter, and then detects the light reflected back from the detection object with a receiver.A color sensor can detect the received light intensity for red, blue and green respectively, making it possible to determine the color of the target object.This section explains the features of color sensors.

Models can be compromised by adversarial inputs that cause unpredictable behavior, undermining their effectiveness and security.

By using a red wavelength photoelectric sensor, there are some color combinations, such as red and white, that are difficult to differentiate. A color sensor allows for stable detection, even for these kinds of difficult combinations.

When the distance to a target changes, the following occurs for conventional photoelectric sensors (received light intensity) and color sensors (ratio of light received).

With a conventional photoelectric sensor, when the distance to the target object changes, the received light intensity also changes. On the other hand, with a color sensor, there is no change in color identification even when the distance to the target changes. As a result, the target's color can be stably differentiated even if the distance changes or the target is tilted.

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.

When the distance to the target changes, the received light intensity also changes.Example: Vibration of a conveyor. Variations in target's passing position.

Why are reflective objects not allowed inlasercontrolled areas

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.

We work with your team to define the objectives for sensitive data exposure, access controls, and compliance standards to establish scope.

Improper handling of sensitive data can result in unauthorized access or breaches, jeopardizing user privacy and data integrity.

Unauthorized modifications to the model can affect its accuracy and reliability, leading to potentially harmful or misleading outputs.

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Since the light source is not just red, but includes the red, green, and blue wavelengths, and the ratio between each of these lights can be calculated, it is possible to differentiate the appearance and color of target pieces.

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.

Pulsar Security’s highly-skilled team provides expert analysis, customized evaluations, and actionable insights to secure your AI/LLM deployment. Our rigorous process improves security readiness by addressing sensitive data exfiltration, system exploitation, and access controls.

Red colored lights impact on laser sensorsreddit

Models may unintentionally generate biased results if trained on skewed data, potentially leading to unfair or discriminatory outcomes.

We conduct a security-focused architectural review of LLMs to identify vulnerabilities, ensure compliance, and enhance resilience against potential threats.

Inadequate access controls can permit unauthorized users to interact with or alter the model, increasing the risk of misuse or security breaches.

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.

Evaluating the security of an LLM is crucial for several reasons, including protecting data, maintaining model integrity, and ensuring robust access controls.