Securing LLMs: Protecting Against Threats
and Ensuring Data Integrity

• Intent Override with Prompt Injections: Malicious manipulation of LLM outputs through misleading or harmful prompts.
• Insecure Outputs / Malicious Code Execution: Risk of generating outputs containing malicious code or sensitive li information.
• Denial of Service (DoS): Potential for overwhelming the system with excessive requests, causing unavailability.
• Model Poisoning: Adversarial manipulation of training data leading to biased or compromised outputs.
• Data Leakage: Unintentional exposure of sensitive or confidential information in generated text.

Defend Against Attacks, Ensure Safety,
and Optimize Performance

• Easy Integration: Choose between a proxy or side-car integration method, requiring only a few lines of code.
• Monitor: Provide completion logs, classify threats, detect anomalies, analyze trends, track costs, identify li hallucinations, and monitor bias.
• Prevent: Real-time detection and interception of malicious LLM outputs with minimal impact on execution time.
• Model Agnostic: Work seamlessly with any LLM architecture.
• Scale/Availability: Distributed services globally to ensure scalability as user operations expand.

Join our Community, Play 'Wild LLaMa',
and Learn About LLM Boundaries!

Join our community to engage in discussions, attend webinars and workshops led by industry experts, access valuable resources, and participate in our educational and fun game/challenge called "Wild LLaMa." Experience firsthand how chatbots can be vulnerable in a hands-on and interactive way. Stay informed about the latest trends in LLM security through our blog and social media presence. Together, let's build a secure future in the age of language models.

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