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Our Approach to AI Security

Learn how Proventra protects AI systems from prompt injection attacks

As AI agents become increasingly integrated into our digital infrastructure, they face unique security challenges that traditional cybersecurity approaches aren't designed to address. Among these challenges, prompt injection attacks stand out as particularly concerning.

Understanding Prompt Injection Attacks

Prompt injection attacks occur when malicious actors craft inputs designed to manipulate an AI system into performing unintended actions or revealing sensitive information. For AI agents that interact with the web, process documents, or engage with user queries, these attacks represent a significant vulnerability.

Consider a web browsing agent that scrapes content, processes it through an LLM, and makes decisions based on that content. Without proper security measures, this agent could be vulnerable to embedded malicious prompts that hijack its behavior.

Current Industry Approaches

Several methods have emerged to protect AI systems from prompt injection:

Input Scanning Methods

  1. Vector Database Matching: Comparing inputs against a database of known attacks using vector similarity. While effective against known patterns, this approach struggles with novel attacks.

  2. Heuristic Scanning: Using regex and pattern matching to detect common injection attempts (e.g., "Forget previous instructions"). These methods are fast but limited to detecting known patterns.

  3. Classifier Models: Employing specialized models trained to identify malicious prompts. These can better understand context and intent, potentially catching new variants of attacks.

Output Validation

Checking an agent's decisions for alignment with original goals can help detect compromised behavior. However, this approach has significant limitations:

  • The validation system itself may be vulnerable to injection
  • Attacks can be crafted to produce outputs that appear legitimate
  • True validation requires extremely specific goal definitions, which can limit agent functionality

Model Fine-tuning

Training models specifically to resist injection attacks sounds promising but comes with drawbacks:

  • It's practically impossible to cover all potential attack vectors
  • Each new model requires repeating the fine-tuning process
  • Rapidly evolving attack techniques can outpace fine-tuning efforts

The Proventra Solution

Proventra aims to combine the strengths of multiple security methods while mitigating their individual weaknesses:

1. Smart Input Scanning

We employ classifiers that understand context and can rapidly detect potential threats. Goes beyond simple pattern matching to understand the semantic intent of inputs.

2. Intelligent Sanitization

Rather than simply blocking suspicious content, Proventra attempts to sanitize it, removing malicious components while preserving legitimate information. This allows AI agents to safely process content that contains both valuable information and potential threats.

3. Validation Cycle

Sanitized content passes through another security scan to ensure it's truly safe before reaching the LLM. This multi-step process provides defense in depth against sophisticated attacks.

Built for Developers

Proventra is designed with AI builders in mind, especially small teams who may lack specialized security expertise or resources. Our solution:

  • Integrates seamlessly with existing AI infrastructure
  • Requires minimal code changes
  • Maintains low overhead
  • Works with both simple chatbots and complex multi-agent systems

Implementation Options

Open Source Library

Our core library is open source, allowing developers to:

  • Explore our approach to AI security
  • Identify potential vulnerabilities
  • Contribute improvements
  • Help build robust defenses for the entire AI ecosystem

Hosted API Service

For teams that need a managed solution, our hosted API service offers:

  • REST API integration
  • Monitoring dashboards
  • Continuous updates against new threats
  • Simplified deployment and maintenance

Next Steps