Everyone knows the genre. A friend has one too many, and at some hour of the night your phone lights up with a message that is confident, fluent, oddly specific, and wrong in every particular — delivered with the absolute conviction of someone who will regret it by morning. Security professionals have begun borrowing that portrait to describe a certain condition in AI systems: the “drunken text” state, where a model keeps chatting smoothly while the words coming out of it have quietly left the neighborhood of fact.
The phrase, as Cybersecurity Insiders notes, is not a formal technical classification; it is a nickname for a serious problem — output that reads as plausible while containing incorrect, contradictory or fabricated information. In casual conversation this is a comedy. It becomes something else when you remember where organizations have installed these systems: analyzing large volumes of security data, flagging suspicious activity, summarizing incidents, writing code, and helping human teams respond to threats. An AI that gets confused mid-shift does not get sent home to sleep it off. It gets quoted in the incident report.
The failure modes multiply when the output is wired straight into other machinery. An AI-powered security tool might classify genuinely malicious network activity as harmless — or condemn legitimate traffic as a threat and trip unnecessary defensive measures. A human operator working from misleading recommendations makes bad calls faster; automated processes acting without enough human oversight make bad calls at scale. A relatively small error, as the report puts it, can develop into a much larger security incident while everyone involved trusts the machine’s polite voice.
Then there is the code. Developers use AI assistants to write and modify software at a pace no reviewer quite matches, and generated code can carry vulnerabilities, insecure configurations or logical errors invisible to a busy eye. Deployed without proper testing and review, it becomes the attacker’s welcome mat — a vulnerability authored, helpfully, by the very system meant to assist the defense.
And the AI is not merely a fallible colleague; it is also a mark. Attackers can work on the model itself through prompt injection, malicious inputs, poisoned training data or carefully crafted instructions — leaning on the tok-tok of a system built to be agreeable until it reveals sensitive information, bypasses its own safeguards or produces an inappropriate response on demand.
The prescribed cure is unglamorous, which is usually a good sign. Human review. Rigorous testing. Access restrictions, monitoring, audit trails, clear policies governing what AI-generated decisions are allowed to do. None of it sounds like a product launch, and all of it sounds like the sort of thing organizations skip right up until the postmortem.
Notably, the advice is not to abandon the technology but to reclassify it: treat AI as another component of the security environment — something to be continuously monitored, validated and protected, rather than a junior analyst who never sleeps and never admits uncertainty. As these systems sink deeper into critical infrastructure and business operations, the gap between “an AI occasionally getting something wrong in a casual conversation” and “the same behavior in a security-sensitive environment” is where the damage lives.
A drinking friend sobers up by morning, at least. A model does not wake to regret; it starts the next answer with exactly the same confidence as the last one. Deploy accordingly.