Systemic security risks of next-generation prediction markets and ExVul's defense methodology

In the past two years, prediction markets have been undergoing a transformation from fringe experiments to mainstream financial infrastructure. Data supports this trend: Polymarket's monthly trading volume exceeded $1 billion during the 2024 US election, with a cumulative trading volume of over $5 billion ; and compliant derivatives exchange Kalshi has also secured over $100 million in funding led by Sequoia Capital.

With the explosive growth of capital, product forms have evolved from simple binary betting to more complex financial derivatives—new concepts such as "attention oracles," "impact markets," and virtual sports betting have emerged one after another. Today's prediction markets are financial systems that use real capital to price probabilities, influence, and human attention in a complex way.

However, any system that "hardcodes" complex game relationships into smart contracts inevitably exposes itself to higher-dimensional security risks. This article, from a Web3 security perspective, systematically outlines the core security risks that prediction markets may face, provides protection strategies based on real-world cases, and introduces the professional security services that ExVul can offer to prediction market projects.

I. Predicting the main security risks facing the market

1. Smart Contract Vulnerabilities: Hidden Killers in Complex Business Contexts

Prediction markets rely heavily on smart contracts to manage complex logic such as fund custody, betting, settlement, odds calculation, fee distribution, and conditional asset splitting (e.g., Trump-BTC/Kamala-BTC). If a contract has a vulnerability, attackers could directly steal funds, manipulate market outcomes, or even permanently lock up funds.

Common risks include:

- Reentrancy attacks and abuse of authorization/`delegatecall` can lead to malicious transfer of funds;

- The clearing and settlement logic is poorly designed, and the handling of boundary conditions (event cancellation, long-term non-triggered events) is lacking;

- Imbalance between casting and destruction of conditional assets, resulting in over-casting, under-sold, or double-spending;

- The pricing formula for perpetual contracts and AMMs is not rigorously implemented, resulting in a significant deviation between oracle prices and the liquidity pool status;

- The permissions for upgrading or proxying contracts are not tightened and can be abused by operators or attackers.

Real-world case: A "precision truncation loophole" in order matching led to the continuous draining of funds from pending orders.

During an audit of Opinion Labs' matching layer, engineers discovered a highly representative type of precision attack. This type of vulnerability does not rely on access control or manipulate oracles; it simply exploits the truncation behavior of integer division to reliably profit from those placing orders.

A typical matching formula is roughly as follows:

Solidity

> takingAmount = makingAmount * takerAmount / makerAmount;

> ```

When an attacker continuously submits extremely small `makingAmount` values (small enough to truncate the result to 0 in integer division), the system enters a dangerous state:

- `takingAmount = 0` — The attacker does not need to pay any tokens in this transaction;

However, `makingAmount` will still be deducted from the maker's outstanding order balance;

By repeatedly submitting these "minimal transaction" order requests, attackers can continuously and risklessly whittle down the funds of the order holder.

The attack path can be summarized as follows:

1. The attacker selects a target to place an order, constructing order parameters with a very small `makingAmount` and a relatively large `takerAmount`;

2. Due to integer truncation, `takingAmount` becomes 0 during calculation;

3. The matching logic still considers the transaction "successful" and transfers `makingAmount` from the maker account to the attacker;

4. Attackers repeatedly execute these small transaction calls hundreds or thousands of times, eventually emptying the entire order book.

In prediction market scenarios, this type of problem is particularly fatal because:

- Order books often have high liquidity (market maker robots, professional LPs);

- Conditional assets (Yes/No Token) and combined positions (such as Trump-BTC / Kamala-BTC) structures allow for more fragmented and numerous order books;

High-frequency, small-amount transactions are part of normal trading behavior and are even more difficult to detect abnormalities with the naked eye.

Therefore, in serious prediction market systems, all integer operations related to matching and settlement should:

- Set a minimum fill limit to reject excessively small trades that are only used for "precision hunting";

- Explicitly check `takingAmount > 0` / `makingAmount > 0` on the critical path, otherwise directly `revert`;

- Conduct large-scale fuzz and boundary condition tests on the matching module, paying particular attention to the combination of "extremely small amount/extreme price".

Otherwise, what may seem like a minor issue of "rounding error" could actually evolve into an unauthorized ATM in the eyes of an attacker.

Preventive measures:

- Conduct professional smart contract security audits for prediction market business models, covering cash flow, state machine, permission model, and failure mode.

- Formal verification and model testing are performed on key invariants (conservation of funds, 1:1 asset correspondence, post-liquidation balance, etc.).

- Reuse mature open-source components as much as possible (standard tokens, access control, upgrade frameworks) to avoid reinventing the wheel and introducing new problems.

- Conduct large-scale fuzz testing and economic attack simulations in Testnet and fork environments, covering settlement and refund paths under extreme conditions.

- It adopts a controllable upgrade and emergency shutdown mechanism, and multi-signature + time lock management.

2. Oracle Attacks: Manipulation from Prices to "Attention"

Traditional prediction markets rely on oracles to provide prices and external event results; while attention oracles in prediction market 2.0 often also incorporate various inputs such as social media data, search trends (e.g., Google Trends), and news sources.

This brings with it both old problems and new challenges:

Price/Outcome Oracle:

- Manipulating short-term price feeds using flash loans;

- Data sources are concentrated in a single exchange or institution, and if there is malicious behavior or downtime, it will directly affect settlement;

- The L2 → L1 message bridge is faulty or under attack, causing abnormal result reporting.

Attention Oracle:

- Manipulating social media data through click fraud, Sybil attacks, and bot accounts;

- Utilize small, illiquid prediction markets to increase underlying "attention input" at low cost;

- Multi-platform linkage: By manipulating data on one platform, the index settlement of another platform can be affected.

Real-world example: In 2025, Polymarket's war map was embroiled in serious controversy due to its reliance on a single data provider.

In the incident, the only settlement source used by Polymarket (ISW map) suddenly marked a disputed area as "frontline advance/change of battle line" about one hour before the market closed, causing the market price to be triggered instantly; however, the marking was immediately rolled back after the market settlement was completed.

Community trackers pointed out:

- At the time, none of the independent cartographers marked the area as occupied or altered;

- No third-party reports of Russian troops entering or advancing;

- The change occurred during the highly sensitive pre-settlement window;

- The rollback occurred immediately after settlement was completed;

- All operations occur at the "critical intersection" explicitly specified by the market target.

For traders, this is equivalent to "a single-point oracle can change the fate of the market in the last hour".

Regardless of whether there was malicious intent, this incident fully demonstrates that:

As long as a single Web2 data source is relied upon (especially manually edited war maps/news headlines/event judgments), the prediction market may be influenced by external forces at the most critical moment, creating systemic settlement risks.

Therefore, for event-driven prediction markets (especially those relying on news organizations or human data input, such as those related to geopolitics, disasters, public opinion, elections, and sports), a single-source architecture must be avoided, and the following should be adopted:

- Multiple map providers (ISW, AMK, OSINT, geo-communities)

- Cross-confirmation by multiple reporters/OSINT

- Multi-source aggregated optimistic oracle

- Delayed settlement window allowing community challenge and arbitration

Only by avoiding "single-point information authority" can event-based prediction markets maintain credibility and resistance to manipulation.

Preventive measures:

- Multi-source aggregation: Price and event results are aggregated using data from multiple oracles, multiple exchanges, and cross-chain data sources, and outliers are removed.

- Introduce "embedded manipulation costs" to the input of attention oracles: force manipulators to actually build positions in the binary prediction market, increasing the cost of wrongdoing.

- Use anti-fraud and anti-Symania mechanisms: account reputation, social graph, frequency limits, and LLM to help identify abnormal patterns.

- Audit the oracle contract itself: whether the update logic, access control, pause/circuit break, and upgrade mechanism are secure.

- Set up failure protection: When the oracle does not update for a long time or an extreme offset occurs, automatically pause settlement or only allow position reduction to prevent erroneous liquidation.

3. Market Manipulation: When "embedded manipulation costs" are insufficient to defend against it.

In theory, using the predicted market price as an oracle input can increase the cost of manipulation. However, in real-world environments, this mechanism may still fail if liquidity is insufficient or market depth is too concentrated in the hands of a few market makers.

Typical manipulation methods include:

- Using large sums of money to make one-sided market-making in the underlying prediction market, creating false probability signals;

- Cross-platform collaboration: pump the probability of a certain event on Polymarket, and then make a large bet on the perpetual contract platform that is linked to that probability;

- Create high trading volume through bots and wash trading to induce follower strategies.

Real-world example: During the 2024 US presidential election, a whale account known as "Fred" emerged on Polymarket, controlling over $30 million in positions to unilaterally bet on a specific outcome. This massive amount of money not only altered the odds but also created a misleading signal on social media that "win rate equals polls," sparking widespread controversy about capital manipulating public opinion. Furthermore, many emerging prediction markets also commonly engage in "wash trading," where users bet against each other to accumulate points.

Preventive measures:

- Design a reasonable fee and slippage mechanism; the cost of large one-sided transactions needs to be significantly increased.

- Deploy an on-chain + off-chain abnormal behavior monitoring system to model abnormal fund flows and related index changes;

- Introducing a "protection mode" for key indices: temporarily increasing margin requirements, limiting maximum position changes, and allowing only position reductions during periods of high volatility;

- To guide and incentivize more diversified liquidity, preventing single points from being controlled by a single LP or market maker.

4. DDoS Attacks and Infrastructure Layer Risks

Prediction markets are not just contracts, but a whole set of Web3+Web2 hybrid infrastructure: front-end websites, API gateways, matching/settlement back-ends, nodes and RPC, L2 Sequencers, etc., can all become targets of attacks.

Risk scenarios:

- High-volume DDoS attacks targeting websites and API gateways prevent users from placing orders or viewing trading platforms;

- Attacks targeting nodes or RPCs can cause transaction sending delays or oracle update failures.

- DoS against L2 Sequencers or bridges, affecting cross-chain settlement and asset transfer.

Preventive measures:

- Utilize a multi-layered protection system from cloud providers, including DDoS protection, WAF, CDN, and rate limiting;

- Multi-Region, multi-RPC vendor deployment, designed with automatic failover;

- Isolate critical settlement and price feed interfaces from regular user access, and set different levels of protection;

- Conduct DDoS drills and emergency response plan verifications regularly, and pre-design "degradation modes" (such as allowing only liquidation or read-only access).

5. User authentication and access control: More than just login issues

In prediction market platforms, permission issues are more sensitive than in typical DApps because:

- Administrators may have permissions such as triggering settlement, modifying parameters, adding/removing marketplaces, and managing blacklists and whitelists;

- In the governance module, a minority of people may "legally" modify oracle sources, fee structures, and even control funds through governance proposals.

Preventive measures:

- On-chain permissions: Use multi-signature wallets + time locks to manage critical contract operations and prevent unauthorized access by a single person;

- For the operations backend: Enable multi-factor authentication (MFA), fine-grained access control, and require dual approval for sensitive operations;

- Regularly audit permission allocation and clean up idle accounts and high-privilege accounts that have not been used for a long time;

- The governance team introduced a "cooling-off period" and a community early warning mechanism to allow sufficient response time for major changes.

6. Web2 API Integration Security: The "Entry Point Defense" for Real-World Data

Prediction markets often need to interface with numerous Web2 services: sports data APIs, financial data providers, KYC/AML services, payment gateways, social and public opinion data, etc. Each of these interfaces represents a potential attack surface.

- Weak authentication or over-authorization leads to the abuse of third-party APIs;

- Man-in-the-middle attacks can tamper with API responses, leading to incorrect settlements or distorted metrics;

- Third-party SDKs were poisoned, introducing supply chain attacks.

Preventive measures:

- Conduct a systematic security assessment and threat modeling for all Web2 integrations: authentication methods, permission boundaries, callback verification, and replay protection;

- Enforce basic protections such as HTTPS/mTLS, request signing, Nonce + timestamp, and IP whitelisting;

- Middle layer decoupling: Web2 data first enters the internal validation/rate limiting layer, and then enters the core business logic;

- Perform supply chain security scans on third-party dependencies, lock versions, and enable private mirror sources.

7. Web3 Wallets and Key Management: Dual Risks for Users and Project Teams

User-side risks:

Phishing websites, fake wallets, and malicious front-ends trick users into signing high-risk transactions;

- Complex Permit/Permit2 signatures can lead to users unknowingly granting unlimited quotas.

Project risks:

- Leaks of oracle price feed wallets, team vaults, and multisignature member keys;

- Incorrect MPC or hardware wallet configuration results in an excessively low signature threshold.

Preventive measures:

- At the UI level, use standard, readable EIP-712 signed messages as much as possible to reduce "blind signing";

- High-risk operations (such as unlimited authorization, cross-contract batch operations) are clearly marked on the front end, and secondary confirmation is added;

- Project operation keys should be managed using hardware wallets, HSMs, or MPCs as much as possible to avoid single point of failure risks associated with hot wallets;

- Integrate trading simulation/security plugins to provide users with risk warnings before signing.

8. Front-end and interaction layer attacks: What's being hijacked isn't the contract, but the user.

Many attacks don't require breaking the contract; they can simply redirect users to a "fake frontend."

- DNS hijacking, domain spoofing, certificate phishing;

- The front-end JS was injected with malicious scripts, which quietly replaced the contract address or transaction parameters.

Real-world example: The Augur ecosystem has seen counterfeit sites/frontends using fake or misleading market data to lure users into interaction, a typical "phishing" tactic that amplifies risks associated with user-side signatures and fund authorization (see: https://thenextweb.com/news/augur-fake-data-bug?utm_source=chatgpt.com). Once a prediction market frontend is counterfeited or tampered with, users are highly susceptible to unknowingly interacting with incorrect contracts or addresses.

Preventive measures:

- Enable HSTS and DNSSEC to monitor for spoofed domains and certificate anomalies;

- Use strict CSP and Sub-Resource Integrity (SRI) to reduce the risk of third-party scripts;

- The build process and deployment pipeline must have supply chain security controls (code signing, build environment isolation).

9. Compliance and Regulatory Risks: The Intersection of Security and Legality

Prediction markets often intersect with sensitive areas such as gambling, financial derivatives, and securities, potentially triggering regulatory red lines in different countries.

- In some jurisdictions, the platform may be considered unlicensed online gambling or derivatives trading;

- Additional compliance requirements may be required when dealing with political, election, or other related events;

Inadequate KYC/AML procedures may lead to traffic restrictions by banks, payment channels, and even on-chain infrastructure.

Preventive measures:

- Incorporate a compliance perspective during the product design phase to differentiate between information markets and financial products;

- Implement access control by region, restricting or enhancing KYC for users from high-risk countries;

- Introduce a basic AML/sanctions list filtering mechanism to avoid business dealings with high-risk entities;

- In the design of smart contracts and platform architecture, adjustable space is reserved to cope with future regulations.

Conclusion

Prediction markets are moving from "purely betting on outcomes" to more complex financial infrastructure: they are beginning to price attention, influence, and collective mindset, making them more imaginative and more vulnerable. If security measures are poorly designed, attackers can not only steal funds but also "steal the future"—by manipulating oracles and market structures to distort price signals that should reflect true information.

ExVul aims to help forecasting market projects build a solid foundation of security and trust while innovating through systematic security audits, mechanism design assessments, penetration testing, and continuous monitoring.

If you are building prediction market-related products (whether it's traditional binary markets, virtual sports, opportunity markets, or attention perpetual contracts), you are welcome to work with your team to thoroughly analyze the current architecture and potential risks. We can provide a customized security solution based on your specific design.

About Us ExVul

ExVul is a Web3 security company offering services including smart contract auditing, blockchain protocol auditing, wallet auditing, Web3 penetration testing, security consulting, and planning. ExVul is committed to improving the overall security of the Web3 ecosystem and remains at the forefront of Web3 security research.