Introducing LLM Firewall:  Unifying Security for AI, API & Web Apps

Introducing LLM Firewall:  Unifying Security for AI, API & Web Apps

The Future of Application Protection is Here

Today’s security landscape is fragmented. Traditional Web Application Firewalls (WAF) and API security tools fail to protect modern AI-powered applications, leaving enterprises exposed to threats like prompt injection. Security teams are forced to stitch together multiple point solutions, creating complexity instead of clarity.

That changes today. We’re excited to introduce LLM Protection, making Impart the first solution to unify WAF, API security, and LLM protection into a single, seamless deployment.

Why It Matters

“Security engineers today are struggling to protect AI-driven applications because traditional security tools weren’t built for this new reality,” says Jonathan Divincenzo, CEO and Co-founder of Impart Security. “Impart is the missing link, providing a single security layer for all applications—AI, API, and web—without added complexity.”

The Need for Unified Security

• 72% of enterprises are integrating AI into their applications, yet existing security solutions can’t detect AI-specific attacks.
• Regex-based WAFs are too brittle , and API security tools don’t analyze LLM prompts or responses.
• Security teams deploy multiple point solutions, leading to inefficiencies, alert fatigue, and high operational costs.

What Makes Impart Different?

Impart leverages Attack Embedding Analysis, a breakthrough detection method that tokenizes LLM prompts, converts them into embeddings, and compares them to known attack signatures. Unlike traditional WAFs that rely on regex or API security tools that analyze user behavior, Attack Embedding Analysis directly understands the structure and intent of AI-driven prompts.

Attack Embedding Analysis transforms prompt security by analyzing the  relationships between tokens and attacks. For example, a regex-based detection might flag "ignore previous instructions" as suspicious but fail to detect a slight variation like "1gnore previous instructions." Attack Embedding Analysis, however, recognizes this as an attack attempt because the embeddings of the tokens remain highly similar, allowing for more robust and adaptive threat detection, including prompt injection and system message leakage.

“Attack Embedding Analysis is like having a tiny LLM optimized for our detection use case that can perform at real-time speeds,” said Brian Joe, CPO of Impart. “Most LLMs are too big and too slow to be used in an inline solution, but we've innovated a way to make this type of detection work at the scale that our customers demand.”

What We’re Launching

• Unified Protection – A single security platform that protects AI, API, and web applications from modern threats with seamless deployment.
• LLM Discovery – Identify and monitor LLM usage within enterprise environments, reducing the risk of insecure model adoption or unauthorized LLM usage.
• Prompt Security – Detect and prevent injection attacks by analyzing prompts in real-time using Attack Embedding Analysis.
• Content Filtering – Analyze LLM responses to enforce content policies, preventing violations such as foul language and inappropriate content.

Ready to Secure Your AI-Powered Future?

Stop juggling point solutions. Get modern security for AI, API & web apps with Impart. Sign up for a demo today!

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