Israeli Military Catching Insiders on Polymarket

Odaily星球日报Publicado a 2026-02-13Actualizado a 2026-02-13

Resumen

Israeli military and civilian prosecutors have charged a reservist soldier and a civilian with using classified military information to profit from bets on the prediction market platform Polymarket. The suspects allegedly leveraged insider knowledge on the timing of military operations to place winning bets, netting over $150,000. One user, identified as "Rundeep," reportedly achieved a 100% success rate in six Israel-related predictions, often betting when odds were below 50%. The case highlights concerns about insider trading on prediction markets, especially regarding sensitive areas like military actions, where such activities could compromise operational security and potentially influence real-world outcomes. Regulatory scrutiny may follow as these platforms face growing scrutiny over their role in public affairs betting.

Original | Odaily Planet Daily (@OdailyChina)

Author | Azuma (@azuma_eth)

Insider information providing an unfair competitive advantage has long been a controversial focus on prediction markets like Polymarket.

Previously, during the US military's operation to capture Venezuelan President Maduro, the odds for related events on Polymarket showed abnormal movements ahead of time (see "When War is Settled Before the News: How Prediction Markets 'Priced' the Maduro Capture Operation 6 Days Early"). If that suspected insider activity could still be explained away by fluctuations in the "Pizza Index," then this time, it can be said that there is definitely an insider on Polymarket, completely confirmed.

Israeli Military Catching "Insiders" Internally

On February 12, Israel's largest English-language newspaper, The Jerusalem Post, reported that the Tel Aviv District Court indicted an Israeli civilian and an Israel Defense Forces (IDF) reservist on Monday, accusing the two of using classified military information to place bets on Polymarket for profit. The court revealed on Thursday that Israeli authorities believe this behavior poses a serious operational security risk during wartime.

According to a statement approved for release by the prosecution, the suspects were arrested in a joint operation by the Israel Security Agency (Shin Bet), the investigation unit under the Ministry of Defense's security agency, and the Israeli police. Investigators suspect that some reservists are using the classified information they access through their military duties to bet on the timing of military operations and profit from it.

Following the investigation, the prosecution stated that it has obtained evidence of misconduct by the civilian and the reservist and has therefore decided to indict the two on charges of "serious security crimes" as well as bribery and obstruction of justice. Meanwhile, the prosecution requested the court to extend the suspects' detention until the conclusion of the trial.

Apart from the information released above, more details of the case remain under a legal gag order, including the identities of the defendants, the specific betting content, and the alleged information flow situation.

Tracing the Insider's Activities

Although we cannot learn the true identity and account information of this insider, the X community had already discovered an account on Polymarket with clearly abnormal behavior. The Jerusalem Post also included a screenshot of this account's profits in its report.

As shown in the image above, this user named Rundeep joined Polymarket in June 2025 and subsequently achieved a 100% win rate in six prediction markets related to Israeli military actions, with five of those bets placed when the probability was below 50%, ultimately profiting over $150,000.

It is worth mentioning that Odaily Planet Daily found that Rundeep had only one loss on Polymarket aside from these "six wins in six attempts." However, this failed prediction was not directly related to Israel but was about "whether the US military would take action against Iran before Saturday (June 21, 2025)"... It seems allied intelligence isn't very reliable after all.

The Real-World Repercussions of Prediction Markets, Truly Frightening to Ponder

Due to Polymarket's open, permissionless nature, anyone can freely place bets on the platform, which objectively provides a more convenient channel for "monetizing information" for those with intelligence advantages — driven by profit, those holding unequal information advantages find it hard to resist the temptation, making it inevitable that insiders will step in to make money.

If such things happened in conventional areas like sports or entertainment, the impact might still be somewhat controllable. But when similar insider betting events occur in sensitive areas like politics or even war, the potential连带 (liándài -连带 means连带, chain, related) terrifying consequences are hard to imagine.

Taking this article as an example, if opposing forces guessed the direction of an Israeli operation in advance through insider betting on Polymarket before the action took place, could it have a huge impact on the subsequent evolution of events? Most people might not easily sympathize with Israel, but in fact, such events could happen to any entity.

In traditional gambling, public affairs such as political elections, legislative outcomes, and wars are usually subject to clear restrictions. Whether prediction markets will face similar regulatory restrictions in the future may involve a long-term regulatory博弈 (bóyì -博弈 means game theory,博弈, struggle,博弈).

Preguntas relacionadas

QWhat is the main subject of the article regarding the Israeli military and Polymarket?

AThe article reports that the Israeli military is prosecuting a civilian and an IDF reservist for allegedly using classified military information to place bets and profit on the prediction market platform Polymarket.

QWhich user on Polymarket was identified by the X community as having suspiciously successful betting activity related to Israeli military actions?

AA user named Rundeep was identified, who achieved a 100% win rate across six prediction markets related to Israeli military actions, with five of those bets placed when the probability was below 50%, earning over $150,000.

QWhat potential risk does the article suggest that insider betting on platforms like Polymarket could pose in the context of military operations?

AThe article suggests that insider betting could pose a severe operational security risk in wartime, as opposing forces might detect upcoming military actions by observing suspicious betting patterns on the prediction market, potentially altering the course of events.

QWhat was the outcome of the single bet that the user Rundeep lost on Polymarket, and what was it about?

ARundeep's only lost bet was on the prediction 'Will the US military take action against Iran by Saturday (June 21, 2025)?', which was not directly related to Israeli operations.

QHow does the article contrast the regulation of traditional betting markets with prediction markets like Polymarket concerning public affairs?

AThe article notes that traditional betting markets often have explicit restrictions on public affairs like political elections, legislation, and war, and it suggests that prediction markets may face a long-term regulatory battle over similar restrictions in the future.

Lecturas Relacionadas

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbitHace 5 min(s)

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbitHace 5 min(s)

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbitHace 9 min(s)

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbitHace 9 min(s)

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbitHace 10 min(s)

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbitHace 10 min(s)

Trading

Spot
活动图片