Kalshi A.I CyberSecurity Scoring
Kalshi
Company Information
Website:https://www.kalshi.com
Employees number:487
Number of followers:64,089
NAICS:52
Industry Type:Financial Services
Homepage:kalshi.com
Kalshi Risk Score (AI oriented)
Between 750 and 799
KalshiFinancial Services
Updated:
29/04/2026
29/04/2026
758/1000
Fair
Baa
Kalshi Global Score (TPRM)
xxxx
KalshiFinancial Services
Score locked

KalshiFair
Current Score
758Baa (FAIR)
01000
1 incidents
0 avg impact
Incident timeline with MITRE ATT&CK tactics, techniques, and mitigations.
AUGUST 2026
758
JULY 2026
758
JUNE 2026
758
MAY 2026
758
APRIL 2026
758
MARCH 2026
758
FEBRUARY 2026
758
JANUARY 2026
758
DECEMBER 2025
758
NOVEMBER 2025
758
OCTOBER 2025
758
SEPTEMBER 2025
758
OCTOBER 2008
758
Cyber Attack
16 Oct 2008 • Kalshi
Kalshi and Polymarket: Betting on Cybercrime – Prediction Markets and Hacking
Cybercriminals Exploit Prediction Markets to Profit from Insider Knowledge
738
HIGH-20
KALPOL1777451477
Cybercriminals Exploit Prediction Markets to Profit from Insider Knowledge
Cybercrime has long revolved around monetizing unauthorized access from credit card theft to ransomware. Now, attackers are leveraging prediction markets like Kalshi and Polymarket to profit from foreknowledge of real-world events, turning future outcomes into tradable assets.
These platforms allow users to bet on everything from corporate data breaches to regulatory decisions, but hackers are no longer just passive observers. By gaining early access to nonpublic information or manipulating systems, they can predict or even control the outcomes they bet on.
### How Attackers Could Game the System
- Data Breach Betting: A hacker breaches a company, discovers an undisclosed incident, and places a bet on its public disclosure profiting when the breach is reported.
- DeFi Exploits: An attacker identifies a vulnerability in a decentralized finance project, bets on its compromise, then executes the hack earning twice.
- Regulatory Insider Trading: Similar to the EDGAR hack, attackers access embargoed corporate or government filings and bet on outcomes tied to that information.
- Sensor Manipulation: In markets tied to physical data (e.g., temperature readings), hackers alter sensor feeds to skew results in their favor.
- Oracle & Voting Exploits: In decentralized markets, attackers influence outcome-determining mechanisms (e.g., oracles or votes) to rig results.
- Disinformation + Betting: Attackers take a position on a negative event (e.g., a company’s stock drop) and amplify false narratives to ensure the outcome.
- Legal Filing Exploits: Early access to court documents (via systems like PACER) allows betting on lawsuit disclosures before they become public.
- Ransomware + Market Manipulation: After breaching a company, attackers could bet on breach disclosures or operational disruptions, then adjust tactics (e.g., data leaks) to guarantee payouts.
### Why This Is Different
While insider trading and market manipulation aren’t new, prediction markets introduce a financial layer where events themselves become tradable commodities. Existing laws such as data breach disclosure requirements can inadvertently create exploitable windows, giving attackers a predictable timeline to act.
Though no major prosecutions have yet targeted this specific scheme, the building blocks are already in place. Cybercriminals have long stolen early information, manipulated systems, and profited from timing. Prediction markets simply connect these tactics into a new revenue stream.
The core risk? These markets assume participants are passive predictors but attackers are anything but. With the ability to see behind the curtain or pull the strings, betting on the future becomes a far more dangerous game.
INCIDENT DETAILS -
TYPE
MOTIVATION
IMPACT
DATA BREACH
REFERENCES
Frequently Asked Questions
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