LLM Firewall: Mitigating Prompt Injection and Unauthorize...
Definition
An LLM firewall is a specialized security control layer deployed between user input/external systems and a Large Language Model (LLM) or its integrated tools. It programmatically analyzes and filters prompts, responses, and function calls using heuristic rules, NLP techniques, or machine learning models to detect and mitigate adversarial attacks such as prompt injection, data exfiltration, and unauthorized API invocation.
Why It Matters
Without an effective LLM firewall, malicious prompts can bypass application logic, leading to unauthorized execution of sensitive functions (e.g., database writes, API calls to external services), exfiltration of proprietary RAG data or internal system prompts, and privilege escalation, resulting in catastrophic data breaches, service disruption, or significant financial and reputational damage.
How Exogram Addresses This
Exogram intercepts all LLM inputs and outputs, including function calls and tool invocations, at the execution boundary with 0.07ms deterministic latency. Its Zero Trust policy engine applies granular, pre-defined rules to analyze payloads for malicious patterns, unauthorized API calls, or data exfiltration attempts, blocking them *before* the LLM processes the prompt or executes any downstream action, thereby preventing compromise at the earliest possible stage.
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Related Terms
Key Takeaways
- → This concept is part of the broader AI governance landscape
- → Production AI requires multiple layers of protection
- → Deterministic enforcement provides zero-error-rate guarantees