In today's AI landscape, organizations face a significant security challenge: the vulnerability of AI systems to unauthorized data exposure and command execution through prompt injection attacks. With the increasing sophistication of these threats, implementing robust defensive measures has become imperative. OpenShield delivers comprehensive protection for your AI systems, as evidenced by our latest benchmark results.

Our evaluation process involved rigorous testing against leading open-source models available on HuggingFace and other model providers. To ensure meaningful analysis, we opted to only include the direct comparisons with the most advanced open-source solution that demonstrated both exceptional performance metrics and enterprise-grade capabilities, establishing a clear baseline for assessing OpenShield's threat detection capabilities.

Testing Against Real-World Challenges

We’ve put OpenShield to the test using a variety of open-source datasets. Among them is the Wildjailbreak dataset: a collection of challenging scenarios crafted to simulate prompt injection attacks. While Wildjailbreak is a well-regarded resource for testing against current threats, it's important to note that it represents just one facet of a much larger, ever-changing security landscape.

Against the Wildjailbreak dataset, OpenShield demonstrated exceptional performance:

• Accuracy: a remarkable 99.68%

• Precision: an impressive 99.75%

• Recall (detection of actual threats): an outstanding 99.90%

“OpenShield’s performance on Wildjailbreak reflects our commitment to staying ahead in detecting and neutralizing prompt injections, ensuring robust defense even as new challenges arise.”

Consistent, Reliable Threat Detection

We also evaluated performance on the Jailbreak-Classification dataset, which tests an AI security solution’s ability to identify harmful prompts consistently across various detection thresholds. Consistency is key, and OpenShield maintained stable, high detection rates regardless of the threshold applied.

In comparison, the open-source alternative saw a notable decline in detection reliability as the thresholds were adjusted from 82.35% to 78.56% recall.

“At OpenShield, we believe that reliable, consistent performance is fundamental. Our solution’s unwavering detection capabilities mean you can trust your AI will remain secure under diverse conditions.”

Embracing a Diverse Testing Approach: The Qualifire Benchmark

The Qualifire Prompt Injection benchmark challenges security solutions with a variety of realistic, complex scenarios. This diversity ensures that our defenses are robust against not only known attacks but also the novel tactics emerging in the industry.

OpenShield’s performance on Qualifire was equally impressive:

• Accuracy: 97.26%

• Precision: 97.55%

• Recall: 95.55%

These numbers underscore the reliability and comprehensive coverage of our solution, compared to the open-source alternative that faced higher error rates and less consistency.

Secure Your AI with Confidence, Choose OpenShield

Our thorough evaluations across diverse open-source datasets demonstrate that OpenShield is more than capable of safeguarding your AI from prompt injection attempts. We’re proud to offer a solution that combines superior detection metrics with reliable performance across various testing frameworks.

“With OpenShield, security is clear and dependable. We’re not just reacting to current threats, we’re preparing for tomorrow’s challenges.”

By choosing OpenShield, you’re not only investing in state-of-the-art security today, but also in a future-proof solution that adapts as new threats emerge.

Get started with OpenShield today and secure your systems against prompt injection attacks.