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Upwind, the runtime-first cloud security platform, has unveiled research demonstrating high-precision, real-time detection of malicious Large Language Model (LLM) prompts in production environments. Using Nvidia technology, the solution achieves approximately 95% precision while keeping inference times under one millisecond, making advanced AI security practical at scale without sacrificing latency or cost efficiency.
As enterprises rapidly adopt generative AI, with Gartner predicting more than 80% will use generative AI APIs, models, or enabled applications in production this year, the natural language interface itself has become a primary attack surface. Threats such as prompt injection, jailbreaks, data exfiltration, and social engineering embedded in language are difficult for traditional security controls to address.
“LLMs don’t just process input, they interpret intent,” said Moshe Hassan, VP Research & Innovation, at Upwind. “That changes the security model entirely. Organizations aren’t just trying to block bad code anymore, they have to stop attempts that twist language and manipulate systems. Our research with Nvidia shows you can do that effectively in live production environments, without slowing things down or driving up costs.”
Upwind engineered a layered detection system to balance latency, cost, false-positive tolerance, and explainability. The architecture avoids reliance on a single heavyweight model or static rules.
A lightweight classifier filters incoming traffic to determine whether a request is LLM-bound. This stage runs in under a millisecond and achieves 99.88% accuracy, ensuring that deeper semantic analysis is applied only when necessary.
LLM-bound requests are analyzed using the Nvidia nv-embedcode-7b-v1 model deployed through NVIDIA NIM microservices. This stage distinguishes normal prompts from malicious ones, including indirect jailbreaks and prompt injections, achieving 94.53% detection accuracy with inference times well under 0.1 milliseconds.
High-risk or uncertain cases are escalated to the NVIDIA Nemotron-3-Nano-30B model for deeper analysis. NVIDIA NeMo Guardrails is integrated to enforce predefined rules and structured output formats, ensuring consistent security policy alignment while maintaining overall system efficiency.
By embedding LLM threat detection directly into Upwind’s runtime and cloud visibility platform, malicious prompts are not treated as isolated events. Instead, they become actionable security incidents within the broader cloud ecosystem, enabling faster investigation and response.
The research demonstrates that organizations can secure generative AI workloads at production scale without compromising performance or incurring prohibitive costs. As language-based threats become an operational reality, Upwind’s approach with Nvidia technology proves that innovation and security can advance together.
About Upwind
Upwind is the next-generation cloud security platform built to lead the Runtime revolution. Headquartered in San Francisco, California, Upwind brings together a unified vision for cloud and application-layer protection, empowering organizations to run faster, detect threats earlier and secure their environments with unmatched precision. The company was founded by Amiram Shachar and the founding team behind Spot.io (acquired by NetApp for $450 million) and is backed by leading investors including Bessemer, Salesforce Ventures, Greylock, Cyberstarts, Leaders Fund, Craft Ventures, TCV, Alta Park, Cerca Partners, Swish Ventures and Penny Jar Capital. Upwind has raised $430 million since its founding in 2022 and is trusted by forward-thinking enterprises globally to bring real-time runtime intelligence to modern cloud security.
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