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.
/ why ai firewall
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.
/ features
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 to create a pipeline.
Zero-configuration integration
Model pipeline support
Input and output flow control
Compatible with OpenAI API
/ use cases
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 for compliance and optimization.
/ architecture
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
