Author: Changan I Biteye Content Team
At Paris Charles de Gaulle Airport, a man stood by the runway, holding a portable heat source and directing it at a weather sensor.
A few minutes later, the Polymarket weather market settled at 22°C, and the positions he had previously built at extremely low prices turned into $34,000.
The entire process involved no sophisticated quantitative strategies, nor any technical barriers. He simply did one thing: he knew where the entire market's settlement data came from and influenced it.
What this article actually wants to discuss is not a specific vulnerability, but a more fundamental question: when a market aims to "reflect reality," is it also providing participants with the motivation to influence reality?
In this article, we will answer three questions:
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Which type of market in prediction markets is most susceptible to manipulation at the source?
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How do these "vulnerabilities" occur in reality?
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What are the real attitudes of Polymarket and Kalshi toward these issues?
I. You Think You're Betting on Reality, But You're Actually Betting on the Data Source
When most people discuss prediction markets, they focus on the rules themselves, such as: How does this market determine a win? But this is only the first layer. The settlement logic of prediction markets has two layers:
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The first layer is the platform rules, which decide "what outcome counts as a win."
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The second layer is the data source, which determines "what actually happened in the real world."
The market is indeed betting on reality itself, but reality must first be "recorded" before it can be settled. So, in the past, people studied the rules, looked up the specific sources cited in the rules to confirm which website was used, or even emailed the upstream data provider directly to try to get the data earlier.
This step is essentially a competition to see who can "know the result sooner." For example, someone might go to a live sports game and place a bet before the score is synced to the official data system.
But there is another, more easily overlooked point: while everyone is trying to "get data faster," some people are bypassing this step and directly influencing the result itself. As long as reality is eventually fed into the market through a specific data source, influencing reality equates to influencing the settlement.
From "checking the rules" to "finding the data source" to "influencing the outcome," these are three stages on the same path. The first two still leverage information asymmetry, while the last step is actively manufacturing the outcome.
This also fundamentally changes the risk of prediction markets. The issue is no longer just whether the rules are rigorous or the data is timely, but whether reality has been preemptively manipulated before it is recorded.
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When you cannot influence this data source, you are predicting.
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When you can influence this data source, you are changing the outcome.
The competition in prediction markets is essentially about one thing: who can determine "the reality that the market reads" sooner, or even directly.
II. Differences in Manipulability Across Different Types of Markets
Not all markets carry the same risk. Based on the logic of manipulation, they can be roughly divided into four categories.
Category 1: Markets relying on a single-point physical data source
Weather markets are often considered the most susceptible to manipulation. Settlement depends on specific readings from specific weather stations, which are physical devices, publicly located, and sometimes poorly maintained. Under certain conditions, an attacker can physically influence the sensor readings.
A deeper issue is that weather data itself has multi-source discrepancies. Measurements from Weather Underground (WU) and aviation METAR data for the same location often differ. Market rules sometimes do not clearly specify which source to use, or the rules themselves have room for interpretation. This ambiguity is itself a risk.
Category 2: Markets where insiders can know the outcome in advance
Markets related to content creators inherently have information asymmetry. Polymarket and Kalshi have both opened numerous markets around MrBeast's videos, betting on which words he will say in the next video, video length, view count, etc. This information is known to the entire production team before the video is released.
Kalshi publicly handled its first such insider trading case in February 2026: MrBeast's editor, Artem Kaptur, showed a near-perfect success rate in bets on markets related to MrBeast, often betting on extremely low-odds, obscure options. This pattern caught the platform's anti-fraud system's attention.
Kalshi determined he used non-public information about the videos to place bets, profiting over $5,000. He was ultimately fined $20,000, banned for two years, and reported to the CFTC.
Markets of the same type also include the case where multiple Israeli Air Force members were investigated or charged for betting on the timing of military strikes against Iran on Polymarket. One officer leaked information about the 2025 strike operation to a colleague; the two collectively profited approximately $244,000 and were ultimately charged with "leaking classified information." Another crew member said during interrogation: "The entire squadron was betting on Polymarket."
Similar signals emerged from Venezuela: In January 2026, a newly created Polymarket account profited over $400,000 in markets related to Maduro's ouster and US military action.
The structural problem with these markets is: Anyone who knows the content can use the prediction market as a monetization channel. KOLs, celebrities, athletes, and those around them are all potential parties with information asymmetry.
Category 3: Markets where the involved party has an incentive to manipulate the outcome
This is a more hidden layer than insider trading: the involved party knows the market exists and can directly manipulate the course of events.
The most typical case is the Andrew Tate tweet count market. Polymarket opened multiple markets on "How many tweets will Andrew Tate post this week?" with the highest single-market trading volume exceeding $240,000.
On March 10, 2026, trader @Euanker published on-chain analysis, alleging that at least seven linked accounts coordinated bets in six such markets, collectively profiting about $52,000. On-chain evidence showed these accounts used the same exchange and Gnosis Safe wallet, highly linked to Tate himself.
This case reveals a problem more fundamental than ordinary insider trading: Tate himself is the controller of the variable. He can win any range by posting more or fewer tweets, essentially being both player and referee.
Another version of the same logic: Coinbase CEO Brian directly read out "Bitcoin, Ethereum, Blockchain, Staking, Web3" during an earnings call. He later said on X it was a "spontaneous joke" to make all Polymarket and Kalshi markets settle to Yes.
Category 4: Markets where a single individual's action can change the real-world outcome
In August 2025, consecutive incidents occurred in WNBA games where spectators threw green sex toys onto the court. Polymarket subsequently opened series of betting markets. One user, "gigachadsolana," placed a bet of about $13,000 roughly two hours before the incident occurred that such an event would happen, netting over $6,000 after the event.
The core issue of this case is not whether this user knew in advance, but that the market structure itself created an incentive: anyone holding a sufficiently large betting position could lock in profits by personally carrying out the act, at a cost of just a ticket and a prop.
Using Domer's counterparty identification framework: new account, single market, large bet, price insensitive (market order trade), withdraw immediately after betting. This combination satisfies all the characteristics of insider trading. It just happened too fast; by the time others reacted, the market had already settled.
III. The Essence of the Divergence Between Kalshi and Polymarket
Whether vulnerabilities in prediction markets will be punished largely depends on which platform you operate on. The two industry-leading platforms have taken截然不同的 (completely different) paths facing the same problem.
Kalshi's approach is to treat enforcement as brand building. The MrBeast editor case, the congressional candidate case—each handling result is publicly released, with the penalty amount, ban duration, and whether reported to the CFTC clearly stated. In advertisements all over Washington, Kalshi directly states "We ban insider trading."
Polymarket's attitude is much more complex. In November 2025, Polymarket CEO Shayne Coplan was asked about insider trading on CBS's "60 Minutes" and said, "I think it's a good thing that people come to the market with informational advantages. Obviously you need to manage that, you need to be very clear and strict about drawing lines... and ethical standards, we spend a lot of time on that."
The logic behind this statement is: The inflow of insider information into the market actually makes prices more accurate; this is the value of prediction markets. People who know the military action schedule bet, people who know the video content bet—this information originally had no outlet for monetization. Prediction markets provide an outlet for it, while also making market prices closer to the truth.
This logic has some academic basis, but it also means that Polymarket, for a considerable time, held a permissive attitude toward what happened on its platform.
The turning point was the "Van Dyke case." Polymarket said in a statement that when they discovered users were trading using classified government information, they proactively handed the matter over to the Department of Justice and cooperated with the investigation. "Insider trading has no place on Polymarket, and today's arrest proves the system is working."
Identity Verification & Accountability: The Same Person, Two Different Outcomes
Understanding the difference between the two platforms can be done most directly by imagining what would happen to the same insider trader operating on each platform.
Registering an account on Kalshi requires submitting real identity information to complete KYC certification. The platform's AI system continuously scans for abnormal trading patterns. Once a problem is found, Kalshi knows who is behind the account, can contact the person directly, and can also hand over identity information to the CFTC.
Process: System detects anomaly → Platform confirms identity → Public penalty → Report to CFTC.
Registering on Polymarket only requires a crypto wallet address, no real identity information. When community analysts targeted the account "ricosuave666," which made $155,000 in the market on Israeli strikes against Iran...
Polymarket's handling method was to delete the account. But after deletion, the person behind it can immediately return with a new wallet address; the platform has no mechanism to identify this as the same person.
The Van Dyke case was a special circumstance. He registered his Polymarket account with a personal email, leaving a traceable digital footprint, and was eventually found by the FBI following the on-chain records. Polymarket Chief Legal Officer Neal Kumar said afterward: "This is not anonymous. You will be found, just like this person was."
This is the essential difference in accountability capability between the two platforms:
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Kalshi's KYC allows the platform itself to identify and handle problematic accounts;
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Polymarket relies on on-chain transparency plus事后介入 (after-the-fact intervention) by law enforcement agencies, leaving a middle ground with no oversight.
IV. The Reflexivity Paradox of Prediction Markets
The real contradiction of prediction markets lies in this: they are designed as a "tool for discovering the truth," but their incentive mechanisms also influence reality.
This is not just a matter of one platform's design being insufficient, nor a problem that can be solved by regulation alone. It is an inherent contradiction of prediction markets. As long as an event can be traded, it is no longer just an object of observation but becomes a market that can be influenced by participants.
This problem has long existed in financial markets. Soros called it "reflexivity": the market's expectations of reality can, in turn, influence reality itself.
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A falling stock price may lead to financing difficulties.
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Financing difficulties, in turn, further worsen the company's fundamentals.
The market originally reflects reality, but the act of reflection itself changes reality. Prediction markets push this reflexivity to an even more extreme position.
Because they are not trading company stock prices or the future price of some asset, but directly betting on whether a real-world event itself will happen. A person can not only bet that "something will happen," but they may also gain the motivation to make it happen because of that bet.
Weather sensors, live sports games, video content, tweet counts, military actions—these cases seem completely different on the surface, but they all point to the same problem: when reality is financialized, reality itself becomes part of the trade.
Therefore, the most dangerous aspect of prediction markets is not that they might predict incorrectly, but that they might predict so valuably that people start acting around this prediction.
The more successful it is, the more it attracts those with information advantages. The more important it becomes, the more likely it is to change participants' behavior. The closer it gets to reality, the more it may end up shaping reality.
This is the deepest paradox of prediction market: it wants to be a mirror of reality, but when the mirror becomes valuable enough, people start changing the world in front of the mirror.
