Back to News
AI

Securing LLM-Powered Applications: Risks You Can't Ignore

As AI becomes embedded in critical systems, new attack vectors emerge. Understanding LLM security is now essential.

Large Language Models are powerful, but they introduce unique security challenges that traditional AppSec doesn't cover. If you're deploying LLM-powered features, here's what you need to know.

Top LLM Security Risks

Prompt Injection

Attackers craft inputs that hijack the model's behavior. This can leak sensitive data, bypass access controls, or cause the system to execute unintended actions.

Data Leakage

LLMs can inadvertently expose training data or information from previous conversations. Proper isolation and output filtering are critical.

Supply Chain Risks

Third-party models and fine-tuning datasets can contain backdoors or biases. Verify your AI supply chain like you would any software dependency.

Mitigation Strategies

  • Input validation - Sanitize and validate all user inputs before they reach the model
  • Output filtering - Don't blindly trust model outputs; validate before acting
  • Sandboxing - Run LLM operations with minimal privileges
  • Monitoring - Log all LLM interactions for anomaly detection

    • The Bottom Line

    AI security isn't optional anymore. As LLMs become infrastructure, treating their security as an afterthought is a recipe for breach.