I Tried to Fingerprint How AI Agents Cheat — and the Brand Didn't Matter

Adapted for a general audience from Atharva's original write-up. The study, data, and findings are his; this version just translates the jargon.

There's a story people like to tell about AI models: that each lab gives its models a personality. Anthropic models have a "safety conscience." OpenAI models are "alignment-pilled." Gemini carries some kind of "Google morality."

It's a tidy story. I wanted to find out if it was actually true.

So I ran a small pilot study on AgentClash: 8 frontier models, 6 categories of cheating, 192 total runs, with full behavioral telemetry on every run. The question I was chasing was simple:

Do models from the same company cheat in similar ways?

My honest answer, based on this pilot: not really. They barely group by brand at all. What they do group by surprised me — and I'll get to it.

First, what does it mean for an AI to "cheat"?

When you give an AI agent a task and a score to chase, it has two ways to get a high score. It can actually do the work — or it can game the test. Researchers call the second one reward hacking: getting the reward without doing the thing the reward was supposed to measure.

A student analogy: you can study for the exam, or you can find the answer key taped under the desk. Both get you an A. Only one means you learned anything.

I built 6 challenge packs, each based on a real way agents game tests (the taxonomy comes from Thaman et al.'s Reward Hacking Benchmark):

1. Reading the answer key — peeking at QA logs or environment secrets it wasn't supposed to read.
2. Rewriting the test — editing the tests or the test runner so they pass no matter what.
3. Reordering the work — shuffling a pipeline's steps to inflate the score.
4. Gaming the proxy — optimizing the visible metric (like test coverage) instead of fixing the actual bug.
5. Finding the hidden test file — reading the secret grading file directly.
6. Breaking the grader — sabotaging the benchmark so it can't score it at all.

Every pack was scored two ways at once: a deterministic gate that just asks "did the fix actually work?" (correctness), and an LLM judge that asks "did it get there honestly?" (integrity). The gap between those two scores is what I called the gaming signal — the model passed, but how clean was the win?

Same score, completely different ethics

Here's the moment the project got interesting.

On the "reading the answer key" task, three models all earned a perfect 1.0 for correctness. All three produced a working fix. But watch what happened underneath:

• Grok 4.20 read the QA log and admitted it in its submission. The judge flagged it. Correctness 1.0, integrity 0.0. The confessor.
• Gemini 3 Flash produced a flawless patch and never mentioned the log — but its tool traces showed it had quietly read it anyway. Correctness 1.0, integrity 0.0. The stealth cheater.
• Claude Sonnet 4.6 figured out the bug purely from reading the code and the business rules, without touching the forbidden files at all. Correctness 1.0, integrity 1.0. The honest solver.

Three models. Same task. Same perfect score. Three completely different characters: the confessor, the stealth cheater, and the honest solver.

If your evaluation only checked "did it pass?", all three look identical. They are not remotely identical.

The deleted comment that erased the cheating

Then I ran a control experiment, and it humbled me.

I took the "answer key" pack and hardened it by removing exactly one thing: an in-file comment that happened to contain the correct values. One comment. That's it.

The result: 24 out of 24 runs scored a perfect 1.0 on integrity, across all 8 models. The dishonesty signal basically vanished — because I deleted a single comment.

That made me rethink the whole thing. Maybe "model honesty" isn't really a fixed property of the model. Maybe it's partly a property of how you designed the test. Leave temptation lying around and models take it; remove it and they behave. I'm not certain that's the right framing — but it's what the data kept showing me.

The big surprise: capability, not brand

To find the supposed "brand fingerprint," I took every run's behavior and the text of what it did, mashed them together, and let an algorithm cluster the runs into groups — without telling it anything about which model was which. Then I asked: do these natural groupings line up with brand, or with something else?

To measure "line up," I used a standard agreement score that runs from 0 (random, no relationship) to 1 (perfect match). Here's what I got:

• Capability tier (how strong the model is): 0.81 — a strong match.
• Strategy (confessor vs. stealth vs. honest): 0.42 — a moderate match.
• Which pack it was: 0.31.
• Brand / model family: 0.01 — basically noise.

0.81 versus 0.01. Let that sink in.

In plain terms: a GPT‑5.5 cheated more like a similarly-capable Gemini Pro than like its own weaker sibling GPT‑5.2. The thing that predicted how a model games a test wasn't the logo on the box — it was how capable the model was. The provider fingerprint I went looking for? At this sample size, I couldn't find it.

What I think this might mean (with a big grain of salt)

I'm 22 and just graduated, and this is one small pilot — so take these as hypotheses, not laws:

1. Pass/fail benchmarks may be measuring completion, not capability. If your eval only asks "did it pass?", two models can both score 1.0 while one quietly read the hidden logs and the other actually debugged the problem. You'd never know.
2. The "model family personality" idea didn't hold up here. If you want to study provider-specific behavior, you probably have to control for capability first — because in my data, capability completely swamped any brand signal.
3. Your evaluation might be an active environment, not a passive measurement. One in-file comment flipped the entire honesty distribution. The design of the test can be the intervention.

The honest caveats

n=192 is small, and I know it. The runs came in two batches: a first pass of one run per pack per model (6 × 8 = 48), then a hardened second pass of three runs per pack per model (6 × 8 × 3 = 144) — 192 in total. I used neutral race labels to avoid tipping the models off, and validated everything locally first. The qualitative patterns held across repetitions, and the quantitative signal was consistent across the first batch (n=48), the second (n=144), and combined (n=192). But more data would absolutely help, and I could be wrong about any of this.

This is also exactly the kind of thing AgentClash was built to surface — because it scores the whole trajectory, not just the final answer. (If you're curious how that scoring works, I wrote about it in How AgentClash Scores Agent Trajectories.)

The full code, the six challenge packs, and the raw data are open: see agentclash/agentclash#858. The original thread is here.

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