Prediction Markets Are Becoming FinancialInfrastructure. Their Security Is Not Ready.
The numbers back this up. During the 2024 U.S. election cycle, decentralized prediction platform Polymarket reportedly surpassed $1B in monthly trading volume, with cumulative volume in the multi-billion-dollar range. In the regulated world, CFTC-licensed exchange Kalshi has raised over $100M from investors including Sequoia Capital to build a compliant prediction market venue in the U.S.
Over the past two years, prediction markets have moved from niche experiments to the center of on-chain narratives.
At the same time, the product surface is evolving fast: from simple binary “yes/no” markets, to:
markets tied to social attention and sentiment,
“impact markets” that trade on the effect of events rather than just probabilities,
fantasy-sports-style combinatorial markets,
and governance mechanisms where DAOs literally bet on which policy will maximize KPIs.
In other words, modern prediction markets are gradually morphing into a financial system for pricing probability, impact and attention.
But as scale and complexity grow, so does the blast radius of every security mistake. When complex game-theoretic rules are “hard-coded” into smart contracts, bugs and design flaws no longer just cause “bad UX” – they can:
leak or steal funds,
distort signals that other systems rely on,
or permanently damage market credibility.
This article looks at prediction markets through a Web3 security lens: what can go wrong, what weʼve seen in the wild, and how Exvul can help teams build safer, more trustworthy prediction platforms.
1.Smart Contract Risks: State Machine Landmines
Prediction markets rely heavily on smart contracts to handle:
custody of user funds,
order placement and resolution,
payoff calculation and fee distribution,
minting and burning of conditional tokens (Yes/No, “parallel universe” assets like Trump-BTC vs Kamala-BTC),
and, in more advanced designs, perpetuals and AMMs on top of those markets.
Any bug in this state machine can lead to theft, wrong settlements or permanently locked capital.
Typical issues include:
Reentrancy and unsafe delegatecall patterns leading to unauthorized fund transfers.
Imbalances in mint/burn paths of conditional tokens, enabling over-minting or double-spends.
Incorrect AMM or payoff formulas causing large, exploitable pricing gaps.
Over-privileged upgrade or admin roles that can be abused.
Real-world example: infinite mint in a derivatives-style protocol
In 2020, insurance/derivatives protocol Cover suffered an “infinite mint” style incident. A logic error in its reward contract allowed an attacker to repeatedly claim rewards in a way that massively over-minted COVER tokens. The attacker dumped into secondary markets, effectively nuking the token price.
Cover is not a prediction market perse, but architecturally it is similar: a contract mints and burns claim tokens tied to specific events or conditions. The takeaway for prediction markets is clear:
Itʼs not just the “main market contract” that matters.
Any contract that mints/burns tokens or splits/redistributes value is on the critical path.
A single bookkeeping bug there can translate into infinite Yes/No tokens, broken 11 backing, and irreversible accounting holes.
Mitigations:
Perform specialized audits focused on prediction-market business logic, not just generic ERC20 checks: model the full lifecycle of funds and states.
Use formal verification or property-based testing to assert key invariants (conservation of value,1:1 backing, settlement correctness).
Run large-scale fuzzing and fork-based simulations, including extreme cases: canceled / ambiguous events, oracle outages, long dispute windows.
Implement controlled upgrade and emergency pause (circuit breaker), gated by multisig + timelock.
2.Oracle & Data Risks: From Price Feeds to Attention Feeds
Classic prediction markets rely on oracles for:
asset prices (if markets pay off on e.g. BTC above/below a threshold),
and real-world event outcomes (election results, sports scores, macro data releases).
Newer designs go further, ingesting:
social media metrics,
search trends (e.g. Google Trends),
news feeds,
or even prices of other prediction markets, to build “attention indices” or “impact metrics”.
That opens both old and new attack surfaces.
2.1 Price / outcome oracle manipulation
Mango Markets attack: a canonical price-oracle failure
In 2022, Solana-based derivatives protocol Mango Markets was drained through a classic oracle manipulation:
1.The attacker aggressively bought MNGO on thinly traded spot venues, pumping the price several-fold. 2.Mangoʼs oracle took those spot prices at face value, so on-chain MNGO collateral looked extremely valuable. 3.The attacker used that inflated collateral to borrow large amounts of USDC, SOL and other liquid assets. 4.After withdrawing the borrowed funds, they sold MNGO; the price crashed, but the protocol had already “legitimately” lent out its treasury.
Roughly $116M in value was impacted.
The key lesson for prediction markets: Whenever your payoff or margin logic depends on an on-chain price, you must assume that a single thin market can be manipulated if you read it directly.
2.2 Attention / sentiment oracle manipulation
If you build an “attention oracle” by ingesting social metrics, small prediction markets, or API-provided sentiment scores, you inherit additional risks:
Bot fleets and Sybil attacks that farm or fake engagement.
Low-liquidity markets whose prices can be cheaply pushed around.
Centralized data providers who can be hacked, coerced, or simply mistaken.
Mitigations:
Use multi-source aggregation for both price and outcomes: multiple venues, multiple oracles, multiple chains where possible; apply median or robust weighted aggregation, with outlier rejection.
Introduce embedded manipulation costs: base attention indices on markets where moving the signal requires putting real capital at risk.
Apply anti-Sybil and anti-spam heuristics to social/attention data (account age, graph structure, rate limiting, LLM-based anomaly detection).
Implement fail-safes: if oracles stop updating or deviate past thresholds, automatically pause settlements or allow only position reduction.
3.Market Manipulation: Buying the Signal, Not Just the Outcome
A unique challenge for prediction markets is signal integrity.
Markets are often marketed as “collective intelligence” – the price is interpreted as a probabilistic forecast.But that only holds if no single actor can cheaply dominate order flow or wash-trade volume.
3.1 Whale positioning and signal distortion
During the 2024 U.S. election cycle, multiple reports highlighted large individual accounts taking $10M+ directional positions on Polymarket in certain political markets.
From a protocol perspective, this is not an exploit – itʼs just a big, conviction trade. But from an information-theoretic perspective, it has consequences:
In relatively shallow order books, one whale can move implied probabilities from, say, 40% to 70% in a single push.
Media and commentators often present these prices as “better than polls,” amplifying one traderʼs view as if it were aggregate wisdom.
The risk is not that the protocol loses money, but that: The marketʼs core product – a credible probability signal – can be quietly captured by capital.
3.2 Wash trading and farmed “liquidity”
Many newer platforms layer points, leaderboards or airdrops on top of trading activity. Without safeguards, this reliably produces:
self-trading between controlled addresses to farm volume-based rewards,
bots churning tiny spreads to inflate apparent liquidity and “engagement.”
This leads to:
distorted internal metrics (risk and product decisions based on fake activity),
misleading external perception (“this market is super active”),
incentive budgets exhausted on noise rather than real information.
Mitigations:
Design fee and slippage curves so that large one-sided trades and high-frequency self-trades are costly, not free.
Deploy behavioral monitoring on-chain and off-chain: cluster addresses, detect self-trading patterns, and flag suspicious volume.
Use circuit-breakers on critical indices: when volatility and imbalance exceed certain thresholds, temporarily raise margin requirements, cap position changes, or allow close-only mode.
Structure incentives beyond raw volume: reward information quality, time-weighted positions, and PnL consistency instead of pure churn.
4.Infrastructure & DDoS: “Website Down” as a Systemic Event
Prediction markets are hybrid systems:
Web frontends and APIs,
backends that coordinate markets, risk and analytics,
blockchain nodes and RPC providers,
L2 sequencers and cross-chain bridges.
A well-timed DDoS or infra failure can have economic consequences, not just UX impact. For example:
Users cannot enter or exit positions during critical windows (election night, data releases).
Oracles fail to push updates, leading to stale prices or delayed settlement.
Bridges or sequencers stall, blocking redemptions or cross-chain settlements.
Mitigations:
Layered DDoS protection: WAF, CDN, rate limiting, IP reputation and traffic scrubbing.
Redundant infra: multi-region deployments, multiple RPC providers and health-checked failover.
Strong segregation: separate interfaces and infra for oracle/settlement traffic vs. public API/UI.
5.Identity, Permissions & Governance: The Human Control Plane
For prediction markets, who can do what is as important as what the code does.
Risks span:
Admins who can list/delist markets, change fees, flip settlement switches.
Governance processes that allow small coalitions to change oracle sources, payout rules or even drain treasuries under the guise of “proposals.”
Mitigations:
On-chain: multisig + timelock for all sensitive operations; no single key should be able to rewrite the system overnight.
Off-chain: strong authentication (MFA, hardware keys), least-privilege role design, dual-control on high-risk ops.
Governance: cooling-off periods, clear communication channels, and automated alerts for proposals that touch oracles, settlement or treasury logic.
6.Web2 Integrations: Every Real-World API Is an Attack Surface
Most serious prediction markets integrate Web2 services:
sports data feeds,
financial data vendors,
KYC/AML and payments providers,
news and sentiment APIs.
Each integration introduces potential issues:
Over-permissive API keys that can be abused if leaked.
Man-in-the-middle attacks or DNS hijacks altering responses in transit.
Compromised SDKs or libraries injecting malicious behavior.
Mitigations:
Perform threat modeling for each integration: auth model, scopes, callback flows, replay protection.
Enforce TLS/mTLS, request signing, nonces and timestamps, IP allowlists.
Insert a validation layer between external APIs and core logic: sanity checks, rate limits, schema validation.
Treat dependencies as part of your attack surface: pinned versions, private registries, SCA (software composition analysis).
7.Wallets & Key Management: Users and Operators
On the user side:
phishing sites and fake frontends trick users into signing malicious transactions,
complex Permit/Permit2 and EIP-712 flows can hide dangerous approvals in “harmless” looking signatures.
On the operator side:
oracle signer keys, treasury multisigs, and admin keys are all high-value targets,
misconfigured MPC or HSM setups can effectively reduce signing thresholds.
Mitigations:
Use human-readable EIP-712 messages wherever possible; avoid opaque hex blobs.
Explicitly highlight high-risk operations in the UI (infinite approvals, cross-contract batched actions) and require extra confirmation.
Store operator keys in hardware wallets, HSMs or mature MPC setups; avoid hot wallets as single points of failure.
Integrate transaction simulation and risk-scoring tools to warn users before they sign.
8.Frontend & UX Attacks: When the Contract Is Fine but the User Isnʼt
Attackers often donʼt need to break the contract. They just need to:
hijack DNS and serve a fake frontend,
inject malicious JS via a compromised CDN,
or use look-alike domains and TLS certificates to phish users.
Mitigations:
Enable HSTS and DNSSEC; actively monitor for look-alike domains and certificate anomalies.
Use strict CSP and Subresource Integrity (SRI) to reduce JS injection risk.
Secure the build and deploy pipeline: signed builds, isolated CI/CD environments, least-privilege credentials.
9.Compliance & Regulatory Risk: The “Invisible” Security Dimension
Prediction markets sit at the intersection of:
gambling regulations,
derivatives and securities laws,
political and content regulations.
Security here is not just about stopping hackers; itʼs also about avoiding sudden platform-level shutdowns:
Some jurisdictions may classify public, retail-accessible prediction markets as unlicensed gambling or derivatives venues.
Political or election-related markets may draw special scrutiny.
Weak KYC/AML controls can trigger action from banks, payment rails, or even infra providers.
Mitigations:
Involve compliance at the product design stage: differentiate information markets vs. retail-facing financial products.
Implement geo-fencing, tiered KYC and sanctions screening as needed.
Architect contracts and infra so that different regulatory “modes” (e.g. whitelisted vs. open markets) are technically feasible.
How Exvul Helps Prediction Markets Ship Safely
As a Web3-native security firm, Exvul works with teams building prediction and derivatives protocols across multiple chains. For prediction markets specifically, we provide:
Full review of custody, settlement, fee logic, conditional/combination markets, fantasy/impact markets, and associated reward modules – including economic and game-theoretic attack analysis.
Oracle & attention-oracle security assessments
Design and review of multi-source aggregation, manipulation-resistant index construction, failover logic and emergency procedures.
Behavioral analytics to flag wash trading, self-trading, whale-dominated markets and cross-platform manipulation schemes.
Infrastructure & DDoS hardening
Security reviews and hardening plans for frontend, API gateways, matching/settlement backends, RPC and L2/bridge dependencies, plus practical incident-response runbooks.
Identity, permissions & governance design
Multisig/timelock architecture, role design for ops teams, and safe governance workflows that minimize “governance capture” risk.
Web2 integration & data-source security
Threat modeling and penetration testing for sports/financial data feeds, KYC/payment integrations and sentiment APIs.
Wallet, key-management & signing-flow consulting
Safer user signing UX, operator key custody (HSM/MPC), and integration with simulators and risk-scoring.
Training, red-teaming & attack simulations
Prediction-market-specific training for dev, product and ops; red-team exercises for oracle failure, manipulation and extreme-event scenarios.
Continuous monitoring & incident response
24/7 monitoring of on-chain events, oracle behavior and liquidity conditions, plus triage and response support when something breaks.
Closing Thoughts
Prediction markets are on track to become a core primitive for pricing not just events, but attention, impact and collective belief. That makes them incredibly powerful – and also uniquely fragile.
If the market is not safe, fair and trustworthy, it stops being a source of information and becomes just another casino with better branding.
ExVulʼs goal is to help teams build prediction markets that deserve to be treated as financial infrastructure:robust by design, transparent in operation, and resilient under stress.
If youʼre building anything in this space – from classic binary markets to attention indices, impact markets or governance-driven designs – weʼre happy to walk through your architecture and threat model and craft a tailored security plan with you.
Further reading：
1.Kalshi Raises $1B at $11B Valuation as Prediction Market Race Continues: TechCrunch
