Do Not Tank Your Career without understanding LLM Risks: Improper Output Handling
The Hidden Risk Lurking in using Gen AI / LLMs
A no-fluff guide to protecting yourself and your organization from LLM vulnerabilities like Improper Output Handling
Hey there—Mario here.
We’ve all seen the explosive growth of Large Language Models (LLMs). Companies are racing to integrate them into customer support, content generation, and even decision-making processes. But with this gold rush comes a hidden risk that could expose your organization to serious consequences: improper output handling. Let's break down one of OWASP’s Top 10 LLM Application risks for 2025 (Improper Output Handling) and what you need to watch out for.
What Is Improper Output Handling—and Why Should You Care?
Improper output handling occurs when LLM-generated responses aren’t validated or sanitized before being presented to users or passed downstream into other systems. In simpler terms, it’s about what your AI spits out and whether you’re checking it before it goes live.
Neglecting this step can result in:
- Harmful or inappropriate content appearing in customer-facing applications—imagine an AI-powered chatbot providing offensive or inaccurate advice.
- Brand reputation damage—one bad AI-generated response can erode years of trust.
- Regulatory compliance issues—unfiltered AI outputs could leak sensitive data or violate content regulations, leading to legal and financial repercussions.
- Unintended system failures—errors in automated workflows can propagate unchecked, leading to costly mistakes.
How to Prevent Costly AI Failures: Mitigation Strategies
Rather than waiting for a PR disaster, proactive organizations are implementing safeguards to keep their AI-driven systems reliable. Here’s what the experts recommend:
1. Establish Output Validation Pipelines
Think of this as a quality control process for AI-generated text. Implement automated systems that scan LLM responses for harmful, inappropriate, or unauthorized content before they reach the end user.
2. Implement Response Scoring Mechanisms
Use algorithms that flag or block outputs exceeding predefined risk thresholds. These can filter out problematic content automatically, reducing the likelihood of errors slipping through.
3. Incorporate Human Oversight
AI is powerful, but it’s not perfect. Introduce human review, especially for high-risk interactions. Regularly test edge cases to ensure outputs align with your organization’s ethical and operational standards.
4. Leverage Explainability Tools
AI transparency matters. Use tools that provide insights into why a model produces certain outputs, making it easier to detect and correct problematic responses before they escalate.
Final Thoughts: Stay Ahead of the Risks
The companies that succeed with LLMs aren’t just those that deploy them the fastest; they’re the ones that deploy them responsibly. By integrating proper output handling safeguards, you can harness AI’s transformative power while protecting your brand, data, and bottom line.
If your organization is serious about leveraging AI, take these mitigation strategies seriously. What steps are you taking to ensure your AI outputs are safe and compliant? Let’s discuss in the comments.
Stay safe out there.
—Mario