Defenders of AI models are "fighting against infinity" because as model capabilities and complexity grow, the potential attack surface area expands faster than it can be secured. This gives attackers a persistent upper hand in the cat-and-mouse game of AI security.

Claiming a "99% success rate" for an AI guardrail is misleading. The number of potential attacks (i.e., prompts) is nearly infinite. For GPT-5, it's 'one followed by a million zeros.' Blocking 99% of a tested subset still leaves a virtually infinite number of effective attacks undiscovered.

Many AI safety guardrails function like the TSA at an airport: they create the appearance of security for enterprise clients and PR but don't stop determined attackers. Seasoned adversaries can easily switch to a different model, rendering the guardrails a "futile battle" that has little to do with real-world safety.

The public narrative about AI-driven cyberattacks misses the real threat. According to Method Security's CEO, sophisticated adversaries aren't using off-the-shelf models like Claude. They are developing and deploying their own superior, untraceable AI models, making defense significantly more challenging than is commonly understood.

Instead of relying on flawed AI guardrails, focus on traditional security practices. This includes strict permissioning (ensuring an AI agent can't do more than necessary) and containerizing processes (like running AI-generated code in a sandbox) to limit potential damage from a compromised AI.

AI 'agents' that can take actions on your computer—clicking links, copying text—create new security vulnerabilities. These tools, even from major labs, are not fully tested and can be exploited to inject malicious code or perform unauthorized actions, requiring vigilance from IT departments.

A core pillar of modern cybersecurity, anomaly detection, fails when applied to AI agents. These systems lack a stable behavioral baseline, making it nearly impossible to distinguish between a harmless emergent behavior and a genuine threat. This requires entirely new detection paradigms.

Unlike traditional software where a bug can be patched with high certainty, fixing a vulnerability in an AI system is unreliable. The underlying problem often persists because the AI's neural network—its 'brain'—remains susceptible to being tricked in novel ways.

While sophisticated AI attacks are emerging, the vast majority of breaches will continue to exploit poor security fundamentals. Companies that haven't mastered basics like rotating static credentials are far more vulnerable. Focusing on core identity hygiene is the best way to future-proof against any attack, AI-driven or not.