In the past two years, prediction markets have undergone a transformation from a marginal experiment to mainstream financial infrastructure. Data supports this trend: Polymarket saw its monthly trading volume exceed $1 billion during the 2024 U.S. elections, with a cumulative trading volume surpassing $5 billion; meanwhile, the compliant derivatives exchange Kalshi has secured over $100 million in funding led by Sequoia Capital.

With the explosion 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 portfolios are emerging. Today's prediction markets are financial systems that price probabilities, influence, and human attention in complex ways, driven by real capital.

However, any system that "hardcodes" complex game relationships into smart contracts inevitably exposes itself to higher-dimensional security risks. This article systematically outlines the core security risks that prediction markets may face from the perspective of Web3 security, providing protective ideas based on real cases, while also introducing the professional security services that ExVul can offer to prediction market projects.

1. Main Security Risks Faced by Prediction Markets

1. Smart Contract Vulnerabilities: Invisible Killers in Complex Operations

Prediction markets heavily rely 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). Once a contract has vulnerabilities, attackers may directly steal funds, manipulate market outcomes, or even permanently lock funds.

Common risks include:

• Reentrancy attacks, authorization/delegatecall abuse leading to malicious fund transfers;
• Improper design of liquidation and settlement logic, missing boundary condition handling (event cancellation, long-term non-trigger);
• Imbalance in conditional asset minting/burning, causing double minting, under-burning, or double spending;
• Inadequate implementation of perpetual contracts and AMM pricing formulas, leading to severe deviations between oracle prices and liquidity pool states;
• Permissions for upgrading contracts or proxy contracts not tightened, leading to abuse by operators or attackers.

Real Case: "Precision Truncation Vulnerability" in Order Matching Leading to Continuous Drain of Order Funds

During an audit of the matching layer at Opinion Labs, engineers discovered a highly representative type of economic attack known as a Precision Attack. This type of vulnerability does not rely on permission control or oracle manipulation; it simply exploits the truncation behavior of integer division to profit steadily from the order submitter.

A typical matching formula is roughly as follows:

takingAmount = makingAmount * takerAmount / makerAmount;

When an attacker continuously submits an extremely small makingAmount (small enough for the calculation result to be truncated to 0 in integer division), the system enters a dangerous state:

• takingAmount = 0 — the attacker does not need to pay any tokens for this transaction;
• However, makingAmount will still be deducted from the maker's order balance;
• By repeatedly submitting such "minimal transactions," the attacker can continuously and risk-free drain the order funds bit by bit.

The attack path can be summarized as:

1. The attacker selects a target order and constructs a makingAmount that is extremely small and a relatively large takerAmount;
2. Due to integer truncation, takingAmount becomes 0 during calculation;
3. The matching logic still considers the transaction "successful," transferring makingAmount from the maker's account to the attacker;
4. The attacker repeatedly executes hundreds or thousands of such small transaction calls, ultimately draining the entire order.

In the context of prediction markets, this type of issue is particularly deadly because:

• Order books often have deep liquidity (market-making bots, professional LPs);
• Conditional assets (Yes/No Tokens), composite positions (e.g., Trump-BTC / Kamala-BTC) make orders more fragmented and numerous;
• High-frequency small transactions are part of normal trading behavior, making it harder to detect anomalies 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 overly small transactions that are only used for "exploiting precision";
• Explicitly check takingAmount > 0 / makingAmount > 0 on critical paths; otherwise, directly revert;
• Conduct large-scale fuzz testing and boundary condition testing on the matching module, with particular attention to combinations of "minimal amounts / extreme prices."

Otherwise, what seems like a minor "rounding error" could evolve into an unauthorized withdrawal machine in the eyes of an attacker.

Preventive Measures:

• Conduct professional smart contract security audits tailored to the prediction market business model, covering fund flows, state machines, permission models, and failure modes.
• Perform formal verification and model checking on key invariants (fund conservation, 1:1 asset correspondence, post-liquidation balances, etc.).
• Reuse mature open-source components (standard tokens, permission control, upgrade frameworks) to avoid introducing new pitfalls by reinventing the wheel.
• Conduct large-scale fuzz testing and economic attack simulations in Testnet and fork environments, covering settlement and refund paths under extreme conditions.
• Implement controllable upgrade and emergency pause (circuit breaker) mechanisms, managed by multi-signature and time locks.

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

Traditional prediction markets rely on oracles to provide prices and external event outcomes; however, in Prediction Market 2.0, attention oracles often integrate data from social media, search trends (e.g., Google Trends), news sources, and more.

The risks arising from this are both old problems and new challenges:

Price/Outcome Oracles:

• Manipulating short-term prices using flash loans;
• Data sources concentrated in a single exchange or institution, directly affecting settlement if malicious or down;
• Errors or attacks on L2 → L1 message bridges leading to abnormal result reporting.

Attention Oracles:

• Manipulating social media data through bot accounts, volume manipulation, and witch attacks;
• Using small prediction markets with poor liquidity to low-cost inflate underlying "attention inputs";
• Multi-platform coordination, manipulating data on one platform to affect index settlements on another.

Real Case: The 2025 Polymarket War Map Faces Serious Controversy Due to Reliance on a Single Data Provider.

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

Community trackers pointed out:

• At that time, all independent mappers did not mark the area as occupied or changed;
• No third-party reports of Russian troops entering or advancing;
• The change occurred in the highly sensitive settlement window of the market;
• The rollback happened immediately after the settlement was completed;
• All operations occurred at the "critical junction" explicitly designated by the market.

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

Whether or not there was malice, this incident fully illustrates:

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

Therefore, for event-based prediction markets (especially those relying on news agencies or manual data inputs in geopolitics, disasters, public opinion, elections, sports, etc.), it is essential to avoid a single source architecture and adopt:

• Multiple map providers (ISW, AMK, OSINT, geographic communities)
• Multiple journalists / OSINT cross-verification
• Multi-source aggregated optimistic oracles
• Allowing community questioning and arbitration with a delayed settlement window

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

Preventive Measures:

• Multi-source aggregation: Use multiple oracles for prices and event outcomes, data from multiple exchanges, and cross-chain data sources, aggregating through median or weighted average while excluding outliers.
• Introduce "embedded manipulation costs" for attention oracle inputs: Require manipulators to build real positions in binary prediction markets, increasing the cost of malice.
• Implement anti-volume manipulation and anti-witch mechanisms: Account reputation, social graphs, frequency limits, and LLM-assisted identification of abnormal patterns.
• Audit the oracle contracts themselves: Ensure that update logic, permission control, pause/circuit breaker, and upgrade mechanisms are secure.
• Set up failure protection: Automatically pause settlements or only allow position reductions when oracles do not update for an extended period or show extreme deviations.

3. Market Manipulation: When "Embedded Manipulation Costs" Are Insufficient for Defense

In theory, using the prices of the prediction market itself as oracle inputs can increase manipulation costs. However, in practical environments, if liquidity is insufficient and depth is overly concentrated among a few market makers, this mechanism may still fail.

Typical manipulation methods include:

• Using large amounts of capital to unilaterally market-make in the underlying prediction market, creating false probability signals;
• Cross-platform collaboration: Pumping the probability of a certain event on Polymarket, then making large counter-bets on perpetual contract platforms linked to that probability;
• Creating high trading volumes through bots and wash trading to induce following strategies.

Real Case: During the 2024 U.S. elections, a whale account known as "Fred" appeared on Polymarket, controlling over $30 million in positions to unilaterally bet on specific outcomes. This massive capital not only changed the odds but also created misleading signals on social media that "winning rates equate to polls," sparking widespread controversy over capital manipulation. Additionally, many emerging prediction markets also commonly exhibit "wash trading" phenomena where users self-bet to earn points.

Preventive Measures:

• Design reasonable fee and slippage mechanisms, significantly increasing the cost of large unilateral trades;

• Deploy on-chain and off-chain anomaly behavior monitoring systems to model abnormal fund flows and related index linkage changes.

• Introduce a "protection mode" for key indices: temporarily increase margin during severe fluctuations, limit maximum position changes, and only allow position reductions;

• Guide and incentivize liquidity to be more decentralized, avoiding control of single points by a single LP or market maker.

4. DDoS Attacks and Infrastructure Layer Risks

Prediction markets are not just contracts; they are a complete set of mixed Web3 + Web2 infrastructure: front-end websites, API gateways, matching/settlement backends, nodes and RPCs, L2 sequencers, etc., all of which can become attack targets.

Risk Scenarios:

• High-volume DDoS attacks targeting websites and API gateways, preventing users from placing orders or viewing market data;

• Attacks on nodes or RPCs causing transaction sending delays and oracle update failures;

• DoS attacks on L2 sequencers or bridges affecting cross-chain settlements and asset transfers.

Preventive Measures:

• Use multi-layer protection systems such as DDoS protection from cloud providers, WAF, CDN, and rate limiting;

• Deploy across multiple regions and RPC providers, designing for automatic failover;

• Isolate key settlement and pricing interfaces from regular user access, setting different protection intensities;

• Regularly conduct DDoS drills and emergency plan validations, pre-designing "degradation modes" (e.g., only allowing position closures, read-only access).

5. User Authentication and Permission Management: More Than Just Login Issues

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

• Administrators may have permissions to trigger settlements, modify parameters, add/remove markets, and manage black/white lists;

• In governance modules, a few individuals may "legally" modify oracle sources, fee structures, or even control funds through governance proposals.

Preventive Measures:

• For on-chain permissions: use multi-signature wallets + time locks to manage key contract operations, eliminating single-person overreach;

• For operational backends: enable multi-factor authentication (MFA), fine-grained permission control, and require dual approval for sensitive operations;

• Regularly audit permission allocations, cleaning up idle accounts and long-unused high-permission accounts;

• Introduce a "cooling-off period" and community alert mechanisms in governance to allow sufficient response time for significant changes.

6. Web2 API Integration Security: The "First Line of Defense" for Real-World Data

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

• Weak authentication or excessive authorization leading to third-party API abuse;

• Man-in-the-middle attacks altering API responses, resulting in incorrect settlements or distorted metrics;

• Poisoned third-party SDKs introducing supply chain attacks.

Preventive Measures:

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

• Enforce HTTPS/mTLS, request signing, nonce + timestamps, IP whitelisting, and other basic protections;

• Decouple the middle layer: Web2 data should first enter an internal verification/rate-limiting layer before entering core business logic;

• Perform supply chain security scans on third-party dependencies, locking versions and enabling private image sources.

7. Web3 Wallet and Key Management: Dual Risks from Users to Project Parties

User-Side Risks:

• Phishing websites, fake wallets, malicious frontends that induce users to sign high-risk transactions;

• Complex Permit/Permit2 signatures leading users to unknowingly authorize unlimited amounts.

Project-Side Risks:

• Oracle pricing wallets, team treasury, multi-signature member key leaks;

• Improper configuration of MPC or hardware wallets leading to excessively low signing thresholds.

Preventive Measures:

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

• Clearly mark high-risk operations (e.g., unlimited authorizations, cross-contract batch operations) on the frontend and add secondary confirmations;

• Use hardware wallets, HSMs, or MPC management for project operational keys to avoid single-point risks with hot wallets;

• Integrate transaction simulation/security plugins to provide users with risk warnings before signing.

8. Frontend and Interaction Layer Attacks: It's Not the Contracts Being Hijacked, But the Users

Many attacks do not require breaching contracts; they only need to guide users to "fake frontends":

• DNS hijacking, spoofed domains, certificate phishing;

• Malicious scripts injected into frontend JS, quietly replacing contract addresses or transaction parameters.

Real Case: The Augur ecosystem once experienced counterfeit sites/frontends that fabricated or misleadingly presented markets and data to induce user interaction, a typical method of "fake website phishing," amplifying user-side signing and fund authorization risks (reference: https://thenextweb.com/news/augur-fake-data-bug?utm_source=chatgpt.com). Once the prediction market frontend is counterfeited or tampered with, users can easily interact with incorrect contracts or addresses without realizing it.

Preventive Measures:

• Enable HSTS, DNSSEC, and monitor for spoofed domains and certificate anomalies;

• Use strict CSP and Subresource Integrity (SRI) to reduce third-party script risks;

• Ensure that the build process and deployment pipeline 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:

• For users in certain jurisdictions, the platform may be considered unlicensed online gambling or derivative trading;

• Involving political, electoral, and other events may face additional compliance requirements;

• Inadequate KYC/AML processes may lead to restrictions from banks, payment channels, or even on-chain infrastructure.

Preventive Measures:

• Introduce compliance perspectives during the product design phase, distinguishing between information markets and financial products;

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

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

• Design smart contracts and platform architecture with adjustable space to respond to future regulations.

Conclusion

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

ExVul aims to help prediction market projects establish 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 traditional binary markets, virtual sports, opportunity markets, or attention perpetual contracts), we welcome you and your team to detail the current architecture and potential risks together, and we can provide a customized security plan based on your specific design.

About Us ExVul

ExVul is a Web3 security company offering services that include smart contract audits, blockchain protocol audits, wallet audits, Web3 penetration testing, security consulting, and planning. ExVul is committed to enhancing the overall security of the Web3 ecosystem and always stands at the forefront of Web3 security research.

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