Using a powerful frontier model for automated red teaming is ineffective. Its built-in safety mechanisms cause it to refuse to generate the jailbreaks or attacks it's tasked with creating. Effective automated red teaming requires models specifically trained for adversarial purposes, often without the same safeguards.

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.

Anthropic admits perfect model safety is currently unachievable. Like software bugs, undiscovered "zero-day" jailbreaks that bypass all safeguards are an expected and constant threat, creating a continuous cat-and-mouse game between developers and malicious actors.

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.

Current AI safety solutions primarily act as external filters, analyzing prompts and responses. This "black box" approach is ineffective against jailbreaks and adversarial attacks that manipulate the model's internal workings to generate malicious output from seemingly benign inputs, much like a building's gate security can't stop a resident from causing harm inside.

Hackers are exploiting AI models not just to write malicious code, but by circumventing safety protocols to extract sensitive or useful information embedded within the AI's training data. This represents a novel attack surface.

Most AI "defense in depth" systems fail because their layers are correlated, often using the same base model. A successful approach requires creating genuinely independent defensive components. Even if each layer is individually weak, their independence makes it combinatorially harder for an attacker to bypass them all.

The world's top AI researchers at labs like OpenAI, Google, and Anthropic have not solved adversarial robustness. It is therefore highly unlikely that third-party B2B security vendors, who typically lack the same level of deep research capability, possess a genuine solution.

Despite frontier model developers' efforts to harden their systems, the UK's AI Safety Institute reports its expert red team has never failed to jailbreak a model. While it is getting harder, this 100% success rate highlights the persistent vulnerability of current AI safeguards.