AI Security Framework | Protecting LLM Applications

New Technologies, New Threats

Integrating Generative AI into enterprise applications introduces an entirely new attack surface. Traditional firewalls and endpoint protection are blind to attacks hidden inside natural language prompts. A robust AI Security Framework is mandatory before exposing any LLM-powered application to employees or customers.

The OWASP Top 10 for LLM Applications

We base our security architecture on the OWASP Top 10 for Large Language Models. Key threats include:

1. Prompt Injection

The most prevalent threat. Attackers craft inputs that trick the LLM into ignoring its original instructions and executing the attacker's commands instead. (Mitigation: Strict input validation, parameterized prompts, and dedicated "guardrail" models.)

2. Insecure Output Handling

When an application blindly trusts the output of an LLM and executes it. For example, passing LLM output directly into a SQL query or a system shell. (Mitigation: Treat all LLM output as untrusted user input; apply output encoding and strict sandboxing.)

3. Sensitive Information Disclosure

When an LLM accidentally reveals proprietary algorithms, PII, or credentials that were included in its training data or system prompt. (Mitigation: RAG with strict document-level RBAC; PII masking pipelines.)

4. Model Denial of Service

Attackers sending resource-heavy queries designed to consume the LLM's context window or compute limits, driving up API costs or crashing the service. (Mitigation: API rate limiting, token limits, and query complexity analysis.)

TechnoPlanet's Defense-in-Depth for AI

Securing an AI agent requires layers of defense:

The Proxy Layer: Intercepting all API calls to the LLM provider to log requests, mask PII, and block known malicious prompts using AI Firewalls.
The App Layer: Implementing Human-in-the-Loop (HITL) approvals before any agent can execute an action that modifies state (e.g., sending an email, dropping a database table).
The Data Layer: Utilizing Vector Database security controls to ensure a user's RAG search only returns document chunks they are explicitly authorized to read in the underlying source system.