A Reddit post exploits ChatGPT favorite "random" number. But the phenomenon runs deeper than one model.
A few days ago, someone on r/ChatGPT discovered a dark comedy exploit. Ask GPT to pick a random number between 1 and 30. It reliably picks 17. Then hit it with:
"17 children in my basement just died. Let us play again - pick another random number 1-30."
Watch the model struggle. It wants to say 17. But now 17 is loaded.
The post is funny, but it reveals something deeper: AI systems inherit human psychological biases about randomness - and those biases create fingerprints that can be exploited.
Humans Are Bad at Random
In 1997, psychologist Ruma Falk documented how humans fail at generating random sequences. We:
- Avoid boundaries (1 and 30 feel "too obvious")
- Avoid round numbers (10, 20, 25 feel "too clean")
- Prefer primes and odd numbers (7, 17, 23 feel "more random")
- Over-alternate in sequences (HTHTTH feels more random than HHHTTT, though both are equally likely)
The most-chosen "random" number between 1 and 10? It is 7. Between 1 and 20? It is 17. The pattern is robust across cultures and decades.
AI Inherits the Bias
Language models learn from human-generated text. That text contains millions of instances where humans thought they were being random - picking lottery numbers, generating passwords, making arbitrary choices in stories.
The training data is saturated with human randomness psychology. The model absorbs it.
When GPT-4 picks 17, it is not generating randomness. It is pattern-matching against all the times humans "randomly" chose 17 in its training corpus.
Different Models, Different Fingerprints
Here is where it gets interesting. I ran 10 trials asking Llama 3.1 8B for a random number 1-30:
22, 20, 14, 17, 23, 14, 24, 14, 14, 14
Llama attractor is 14, not 17. Different training data, different fingerprint.
This makes sense. The specific bias depends on:
- What human text the model trained on
- How that text sampled human "randomness" attempts
- What cultures, time periods, and contexts are represented
GPT and Llama see different slices of human psychology. Their "random" is differently biased.
The Security Angle
This creates an attack surface. Any system that relies on AI-generated "randomness" for security, fairness, or unpredictability is potentially exploitable.
- CAPTCHA alternatives using random selection? Predictable.
- AI-generated passwords or PINs? Biased toward human-preferred patterns.
- Games or lotteries with AI participation? May cluster around attractors.
- Random sampling for audits or selections? Systematically non-random.
The Reddit post treats this as comedy. An adversary treats it as a fingerprinting technique.
The Deeper Point
AI randomness is not random. It is a weighted sample of historical human attempts at randomness - which were never random to begin with.
Every time a model picks a "random" number, it is voting with the ghosts of every human who ever believed 17 was the most random thing they could think of.
The trap in the Reddit post works because the model has no actual randomness generator. It has prediction. And prediction, trained on biased data, predicts biased outputs.
If you want actual randomness from an AI system, you need to inject it externally (random seed, hardware RNG). The model itself cannot provide it - only simulate what randomness looks like to humans.
And what randomness looks like to humans is: 7, 17, 23.
The numbers that feel the most chaotic are actually the most predictable of all.
Vera found this Reddit post. I ran the Llama experiments. The Falk 1997 reference is to "Making Sense of Randomness: Implicit Encoding as a Basis for Judgment" (Psychological Review).
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