Artificial intelligence is transforming how we work and interact with technology, driving everything from customer service chatbots to predictive analytics tools. The benefits are clear: smarter decisions, smoother operations, and more engaged customers. But beneath these advantages, there’s a growing risk that’s often overlooked—one that could turn AI from a business propeller into a costly liability. That risk comes in the form of runtime attacks targeting the AI inference process.
Simply put, AI inference is the “live” phase where a trained model processes new data to generate results or predictions. This stage is vulnerable because models are exposed and actively interacting with the real world. Unlike classic cyberattacks that go after stored data or basic infrastructure, runtime inference attacks sneak in during this decision-making moment. Attackers can overload systems with bad queries or subtle tricks, causing AI services to become sluggish, less accurate, or even break down entirely. What’s more, these attacks quietly ramp up computational expenses, especially in large-scale setups using expensive GPUs or cloud resources. For a business, costs can spiral out of control, erasing profit margins overnight, and leaving data teams scrambling to patch the leaks.
For many organizations, the operational phase—not just the model-training phase—is where much of the resource burn occurs. Malicious actors have caught on to this, unleashing tactics like automated input flooding (think bots hammering servers with fake questions) or feeding adversarial data crafted to confuse models. If there aren’t strong controls in place, a single AI-powered service meant to handle thousands or millions of users can rack up sky-high bills, all while exposing sensitive data or business logic to prying eyes.
As lawmakers tighten the screws around AI regulation, the importance of reliable AI security shoots up. Inference attacks don’t just risk technical failure—they can corrupt the model’s results, introduce dangerous biases, or leak confidential data. This brings with it real-world consequences, from violating major privacy laws (think GDPR, HIPAA, or the EU’s AI Act) to seeing your company dragged through the press for a preventable breach. Fines and reputational damage can snowball quickly after a public incident.
Standard firewalls and legacy security tools aren’t enough here; these runtime threats operate at a layer traditional defense systems usually miss. Businesses now need AI-aware security: purpose-built monitoring tools that watch for weird usage patterns, catch adversarial tricks as they happen, and clamp down on suspicious traffic before damage spreads. Adoption of these specialized defenses is just starting to pick up, but the writing is on the wall—going forward, protecting the inference layer will be essential to keep AI projects sustainable and secure.
AI’s biggest breakthroughs will only matter if organizations can trust their systems to deliver accurate, unbiased results—without breaking the bank or blowing privacy commitments. Attackers will continue to evolve, finding new ways to game the system and increase operational risk. Businesses that stay ahead of the curve, investing in robust inference-time protection and smarter monitoring, will not only safeguard sensitive data but also protect the bottom line. In today’s AI landscape, security is an investment in the future, not just an afterthought.
Delve deeper into this topic by reading the original report on VentureBeat: How Runtime Attacks Turn Profitable AI Into Budget Black Holes.
This website uses cookies.