AI Firewall - Protect Your AI Models

A transparent proxy firewall designed specifically for AI models. OpenShield provides rate limiting, content filtering, keyword filtering, and tokenizer calculation to protect against OWASP Top 10 LLM attacks and malicious AI usage.

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Transparent Proxy Protection for AI Models

Rate Limiting

Custom rate limits for OpenAI endpoints. Protect your AI models from abuse and control usage per user, per model, or per API key.

Content Filtering

Advanced content filtering with Python and LLM-based rules. Detect and block malicious prompts, injection attacks, and inappropriate content.

Tokenizer Calculation

Accurate tokenizer calculation for OpenAI models. Monitor token usage and costs in real-time with precise counting.

/ owasp top 10 llm attacks

Protection Against Critical LLM Vulnerabilities

LLM01: Prompt Injection

Protect against crafted inputs that manipulate LLMs to gain unauthorized access, cause data breaches, or compromise decision-making.

LLM02: Insecure Output Handling

Validate LLM outputs to prevent downstream security exploits, including code execution that compromises systems and exposes data.

LLM03: Training Data Poisoning

Detect tampered training data that could impair LLM models and lead to compromised security, accuracy, or ethical behavior.

LLM04: Model Denial of Service

Prevent overloading LLMs with resource-heavy operations that cause service disruptions and increased costs through intelligent rate limiting.

LLM05: Supply Chain Vulnerabilities

Protect against compromised components, services, or datasets that undermine system integrity and cause data breaches.

LLM06: Sensitive Information Disclosure

Prevent disclosure of sensitive information in LLM outputs that could result in legal consequences or loss of competitive advantage.

LLM07: Insecure Plugin Design

Secure LLM plugins processing untrusted inputs with sufficient access control to prevent remote code execution exploits.

LLM08: Excessive Agency

Control LLM autonomy to prevent unintended consequences that jeopardize reliability, privacy, and trust.

LLM09: Overreliance

Enable critical assessment of LLM outputs to prevent compromised decision making, security vulnerabilities, and legal liabilities.

LLM10: Model Theft

Prevent unauthorized access to proprietary large language models that risks theft, competitive advantage loss, and sensitive information dissemination.

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Comprehensive AI Security Features

Custom Rate Limiting

Set custom rate limits for OpenAI endpoints. Control usage per user, per model, or per API key with flexible configuration options.

Rate limiting per user

Rate limiting per model

API key-based limits

Configurable thresholds

Tokenizer Calculation

Accurate tokenizer calculation for OpenAI models. Monitor token usage and costs in real-time with precise counting for billing and usage tracking.

OpenAI model support

Real-time token counting

Cost tracking and analytics

Usage monitoring

Python & LLM-Based Rules

Advanced content filtering with Python and LLM-based rules. Create custom rules to detect and block malicious prompts, injection attacks, and inappropriate content.

Python rule engine

LLM-based content analysis

Keyword filtering

Custom rule definitions

Transparent Proxy Architecture

Sits transparently between your AI model and the client. No changes required to your existing infrastructure. Chain multiple AI models together.

Zero-configuration integration

Model pipeline support

Input and output flow control

Compatible with OpenAI API

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Protect Your AI Infrastructure

Prevent Prompt Injection Attacks

Challenge

AI models are vulnerable to prompt injection attacks that can manipulate responses and expose sensitive data

Solution

Content filtering and keyword detection block malicious prompts before they reach your AI model, preventing unauthorized access and data breaches.

Control API Usage & Costs

Challenge

Uncontrolled API usage leads to unexpected costs and potential denial of service attacks

Solution

Rate limiting per user, model, or API key with accurate token counting helps control costs and prevent abuse while ensuring fair resource allocation.

Content Moderation

Challenge

AI models can generate inappropriate, harmful, or sensitive content that needs to be filtered

Solution

Python and LLM-based content filtering analyzes both input and output to block inappropriate content, ensuring safe AI interactions.

Monitor & Audit AI Usage

Challenge

Lack of visibility into AI model usage, costs, and potential security incidents

Solution

Comprehensive logging, token counting, and analytics provide full visibility into AI usage patterns, costs, and security events.

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Transparent Proxy Flow

Input Flow

Client requests flow through OpenShield before reaching your AI model:

Rate limiting validation

Content filtering & keyword detection

Prompt injection detection

Request forwarding to AI model

Output Flow

AI model responses are filtered and analyzed before returning to clients:

Output content validation

Tokenizer calculation

Sensitive data detection

Response delivery to client

Protect Your AI Models Today

Firewall for AI Models - Transparent Proxy Protection