# gambling Published articles for gambling. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The psychophysics of affect in large language models DevFeed: [The psychophysics of affect in large language models]() Original publisher: [Read original article]() Author: Joseph Marks (NatWest Group, London, UK), Marius C. Vollberg (University of Zurich, Zurich, Switzerland, Swiss Center for Affective Sciences, Geneva, Switzerland) Published: 2026-11-02T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Artificial Intelligence](), [Large Language Model](), [LLM evaluation / benchmarking]() Tags: [choice](), [cs-ai](), [cs-cl](), [feelings](), [gambling](), [large-language-models-llms]() ### AI overview In tests involving gambling and stock-trading outcomes, LLMs' anticipated feelings depended on how outcomes were presented. Ratings made one outcome at a time showed diminishing sensitivity to magnitude and stronger reactions to gains than equivalent losses, a pattern the authors call "silicon gain amplification." When outcomes were rated together for comparison, ratings became more linear. In choices, the gain-loss asymmetry largely disappeared, and the models' feeling ratings did not reliably explain choices better than monetary utility. The findings suggest these affect mappings are malleable and do not establish that models use them consistently in decisions. ### Source excerpt arXiv:2610.40985v1 Announce Type: new Abstract: Good decisions often require considering how outcomes will feel. Yet large language models (LLMs) increasingly enter decision workflows, despite little being known about how they map outcomes to feelings or use those mappings in choice. We tested how LLMs anticipated gambling or stock-trading outcomes would feel and whether they incorporated these ratings into choice. When outcomes were rated in isolation, ratings exhibited diminishing sensitivity to magnitude, resembling human value functions; but models rated gains more intensely than equivalent losses, an inversion of canonical loss aversion we call silicon gain amplification. When outcomes were rated together, enabling direct comparison, ratings linearised. In choice, the gain-loss asymmetry largely disappeared, and models' own ratings did not reliably explain their choices better than monetary utility did. These findings reveal malleable mappings from outcomes to affect, raising questions about whether and how agentic LLMs should incorporate anticipated feelings into decisions. ## Borrowed Reputation: Domain Shadowing and Gambling SEO DevFeed: [Borrowed Reputation: Domain Shadowing and Gambling SEO]() Original publisher: [Read original article]() Author: Infoblox Threat Intel Published: 2026-10-26T13:00:40Z Content type: news Language: en Sources: [Infoblox]() Topics: [Domain Name System (DNS)](), [DNS security](), [Search engine optimization (SEO)](), [domain](), [CNAME vs A record](), [credential-based attacks](), [hosting]() Tags: [cloudflare-pages](), [cloudfront](), [cname](), [compromise](), [delegation](), [dns](), [dns-hijacking](), [domain-shadowing](), [edu-bd](), [gambling](), [gambling-seo](), [gov-in](), [government](), [hidden-hawk](), [indian](), [indonesian](), [infoblox-threat-intel](), [ns-delegation](), [parasite-seo](), [seo]() ### AI overview The report describes two unrelated operations that used unauthorized DNS changes to place illegal gambling pages under legitimate school and government domains, borrowing institutional reputation for SEO and traffic. Hidden Hawk added individual CNAME records, mostly pointing school and government subdomains to gambling pages on Cloudflare Pages; researchers observed more than 850 entry points across eight institutional domains, a minimum while research continued. A separate campaign delegated subzones beneath maharashtra.gov.in and online.esic.gov.in to ClouDNS nameservers, then used a wildcard to create many hostnames; pages used several delivery providers, and the delegations were no longer observed in mid-October 2026. The access methods remain unknown, and the Maharashtra activity is not attributed to Hidden Hawk. The report advises DNS operators to monitor zone changes, especially new subdomains, unexpected CNAMEs, NS delegations, and wildcards, secure accounts that can change DNS, and address malicious hostnames and enabling DNS access rather than blocking legitimate institutional domains. ### Source excerpt Borrowed Reputation How domain shadowing placed illegal gambling pages beneath school and government domains Executive Summary We identified two unrelated operations that used domain shadowing to place illegal gambling content beneath legitimate school and government domains. In both cases, an unauthorized party appears to have gained access to DNS administration and created records that the [...] The post Borrowed Reputation: Domain Shadowing and Gambling SEO appeared first on Infoblox Blog. ## DraftKings Is Using AI to Supercharge the Harms of Online Behavioral Advertising DevFeed: [DraftKings Is Using AI to Supercharge the Harms of Online Behavioral Advertising]() Original publisher: [Read original article]() Author: Devanshi Nishar Published: 2026-09-24T20:11:52Z Content type: article Language: en Sources: [Deeplinks]() Topics: [Artificial Intelligence](), [Machine learning](), [data](), [Risk]() Tags: [ad-tech](), [ai](), [data](), [gambling](), [machine-learning](), [model](), [online-behavioral-tracking](), [privacy](), [surveillance](), [target]() ### AI overview The article examines how DraftKings uses customers' betting records and machine learning to identify gamblers likely to place losing bets and target them with promotions. It argues that AI intensifies the data collection and privacy harms of behavioral advertising, particularly for people vulnerable to problem gambling. ### Source excerpt Online sports betting company DraftKings is using AI to target customers who are most likely to place losing bets and respond to gambling promotions. This kind of targeting is a form of online behavioral advertising, which is when companies personalize the ads they show you based on the data they've collected about you. The more data a company has, the more personalized the ad can be. While DraftKings is using AI to supercharge the harmful effects of online behavioral advertising, EFF has long argued that all behavioral advertising should be banned. According to the New York Times, DraftKings is using its customers' betting records to train a machine learning model to find losing gamblers. Once found, DraftKings sends these customers targeted advertising designed to lure them back to the site to place more bets--bets that DraftKings thinks will be losing ones. DraftKings has a business incentive to keep losing gamblers coming back to their site, because these are the users actually making DraftKings money. Unfortunately, those considered "problem gamblers" (people who repeatedly gamble despite harm to themselves, their finances, and their relationships) are highly likely to be targeted by this model. By re-engaging these individuals through targeted promotions aimed at keeping them on the platform, DraftKings is capitalizing on their vulnerability for profit instead of mitigating their risk. Predatory online behavioral advertising isn't new, but companies' use of AI to process data and target customers has magnified its harms. Online behavioral advertising incentivizes the collection of vast quantities of data to power ad tech. Adding AI into the mix means that even more data is collected to train and refine models. Because AI operates as a black box, the humans building the models can rarely predict which data points are the most useful to the AI, driving them to continuously collect more data. AI also allows companies to process enormous data sets much faster, and, ## Responsible Gambling and Fraud Monitoring DevFeed: [Responsible Gambling and Fraud Monitoring]() Original publisher: [Read original article]() Author: Ben Price Published: 2026-09-21T23:00:00Z Content type: article Language: en Sources: [Sift Science]() Topics: [Security, Privacy and Abuse Prevention](), [Monitoring](), [Risk](), [data]() Tags: [account](), [behavior](), [bonus-abuse](), [data](), [device](), [fraud](), [fraud-prevention](), [gambling](), [gambling-fraud](), [igaming](), [igaming-fraud](), [igaming-fraud-prevention](), [monitoring](), [payment](), [prevent-fraud](), [regulatory](), [risk]() ### AI overview This article explains why responsible gambling and fraud prevention in iGaming should share behavioral signals, data, and risk monitoring. It describes overlaps involving fake accounts, bonus abuse, payment-method cycling, and indicators of escalating player harm, while noting that separate teams can miss patterns across the player journey. ### Source excerpt Responsible gambling and fraud prevention used to live in separate departments with separate tools and separate KPIs. That split does not hold up anymore. The same behavioral signals that flag a fraudster opening a fake account can also flag a player heading toward harm, and iGaming operators that connect these two disciplines are catching more [...] The post Responsible Gambling and Fraud Monitoring appeared first on Sift. ## Online Gambling Fraud Prevention for iGaming Operators DevFeed: [Online Gambling Fraud Prevention for iGaming Operators]() Original publisher: [Read original article]() Author: Ben Price Published: 2026-09-11T21:00:00Z Content type: article Language: en Sources: [Sift Science]() Topics: [Security, Privacy and Abuse Prevention](), [Bot](), [Sports]() Tags: [2026](), [bonus-abuse](), [bots](), [fraud-prevention](), [gambling](), [gambling-fraud](), [igaming](), [igaming-fraud-prevention](), [online-gambling](), [online-gambling-fraud](), [prevent-fraud](), [sports-betting-fraud]() ### AI overview The article examines fraud in U.S. iGaming and sportsbook operations, focusing on signup, deposit, and cashout risks. It highlights organized bonus abuse involving synthetic identities, stolen credentials, linked accounts, bots, device farms, and fabricated identities. ### Source excerpt In just Q1 2026, U.S. iGaming has generated about $3.04 billion, marking over 20% growth year-over-year and nearly $1 billion in April 2026 alone. For the full 2025 year, iGaming hit over $10 billion in revenue, up 27.6% over the previous year. With it being such a profitable industry, there's no wonder why it's ripe [...] The post Online Gambling Fraud Prevention for iGaming Operators appeared first on Sift. ## Testing Trust in Prediction Markets | Part 2 DevFeed: [Testing Trust in Prediction Markets | Part 2]() Original publisher: [Read original article]() Author: David Phillips Published: 2026-09-10T17:44:43Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [trust](), [trust and safety]() Tags: [betting](), [collective](), [contracts](), [data](), [data-insights](), [decentralized](), [digital-trust](), [discovery](), [fraud](), [gambling](), [integrity](), [kalshi](), [manipulation](), [markets](), [military](), [minutes](), [model](), [oracle](), [polymarket](), [prediction-markets](), [sports](), [trust]() ### AI overview Prediction market forecasts reflect the rules and decision structures behind them, which create distinct integrity risks. Kalshi uses detailed contract rules and centralized resolution; the excerpt says this can produce outcomes at odds with common-sense interpretations. Polymarket uses decentralized, oracle-based resolution, where token-holder discretion may affect outcomes. Cited analyses found that five large UMA wallets held about 40% of votes and that some Polymarket traders voted on markets where they had positions. The excerpt also reports that 0.1% of Polymarket accounts received 67% of profits, while cautioning that concentration alone does not prove manipulation or fraud. Separately, the Anti-Corruption Data Collective reported that long-shot military contracts with implied odds below 35% paid off about 52% of the time. The excerpt frames these issues as questions about representation, contract interpretation, and market legitimacy. ### Source excerpt Missed Part 1? Read it here. Prediction markets make a bold claim that they are engines of truth discovery. Proponents argue that by aggregating collective judgment, prediction markets generate forecasts that are far more valuable than traditional gambling or sports betting. While that claim has merit, a prediction market's output is not merely a well [...] The post Testing Trust in Prediction Markets | Part 2 appeared first on Sift. ## Gaming the system: how a Chinese-speaking actor turned Brazilian government sites into an SEO weapon DevFeed: [Gaming the system: how a Chinese-speaking actor turned Brazilian government sites into an SEO weapon]() Original publisher: [Read original article]() Author: stcpresearch Published: 2026-09-02T10:16:16Z Content type: news Language: en Sources: [Check Point Research]() Topics: [Search engine optimization (SEO)](), [Linux](), [Domain Name System (DNS)](), [Encryption](), [SELinux]() Tags: [2025](), [actor](), [asia](), [backdoors](), [campaign](), [check-point](), [check-point-research](), [check-point-research-publications](), [custom](), [cybercrime](), [earth](), [gambling](), [gaming](), [government](), [linux](), [organizations](), [research](), [seo](), [sites](), [speaking](), [system]() ### AI overview Since mid-2025, Check Point Research has tracked a campaign against Brazilian organizations, mainly government and educational institutions, attributed with medium-to-high confidence to Gambling Goblin, linked to Earth Berberoka. The attackers install malicious Apache modules that proxy selected site paths to phishing pages while stripping content-security headers. Those pages impersonate app stores and promote gambling, using compromised high-reputation domains--many Brazilian government sites--to manipulate search rankings and redirect visitors. The report describes a broad, heavily obfuscated Linux toolkit, including reconnaissance tools, downloaders, credential theft and backdoors such as AlphaAgent and oRAT. Related phishing pages in Vietnamese, Spanish and English, plus infrastructure that generates domains daily, suggest the operation can expand across regions. Researchers have not directly observed initial access; the report identifies a risk that the app-download infrastructure could be configured to deliver malware. It recommends patching exposed services, auditing Apache and SSH configurations, and looking for rogue modules and disguised processes. ### Source excerpt Research by: Amit Yardeni Key Points Introduction Since mid-2025, Check Point Research has tracked a sustained campaign against Brazilian organizations. The tradecraft points to a Chinese-speaking cybercrime group connected to Earth Berberoka, an actor first documented targeting gambling sites across Asia. Once inside a victim, the group deploys a broad Linux toolkit: a custom downloader, several backdoors, [...] The post Gaming the system: how a Chinese-speaking actor turned Brazilian government sites into an SEO weapon appeared first on Check Point Research. ## Sable Squirrel Uses Expired Domains for Streaming, Gambling, and Malware Command-and-Control DevFeed: [Sable Squirrel Uses Expired Domains for Streaming, Gambling, and Malware Command-and-Control]() Original publisher: [Read original article]() Author: Infoblox Threat Intel Published: 2026-08-13T12:59:29Z Content type: news Language: en Sources: [Infoblox]() Topics: [Cybercrime](), [C2](), [domain](), [infoblox](), [ransomware](), [Website]() Tags: [90phut](), [asyncrat](), [c2](), [cakhia](), [command-and-control](), [dcrat](), [dns](), [domain](), [dropcatch](), [expired-domains](), [gambling](), [hidden-tear](), [illegal](), [illegal-streaming](), [infoblox-threat-intel](), [malware](), [malware-c2](), [mitom](), [nanocore](), [news](), [njrat](), [piracy](), [quasar](), [ransomware](), [rat](), [remcos](), [remote-access-trojan](), [sable-squirrel](), [socolive](), [streaming](), [threat-intel](), [threat-intelligence](), [trojan](), [vietnam](), [xoi-lac-tv](), [xoilac]() ### AI overview Infoblox Threat Intel reports that the Sable Squirrel cybercriminal operation controls more than 10,000 domains used for illegal sports streaming, gambling promotion, and malware command-and-control. The investigation attributes more than $430,000 in identified dropcatch purchases and estimates total expired-domain spending above $7 million. ### Source excerpt Sable Squirrel spends millions on expired domains to deliver illegal gambling, streaming, RAT malware, and ransomware in a massive cybercriminal enterprise. Infoblox Threat Intel | Dropcatch series | Part 2 of 3 Executive Summary A decade-old news site, a failed startup's domain, a former corporate campaign, and a football club website do not usually end up in the same [...] The post $7 Million in Expired Domains Fuel a Streaming Empire with a Malware Secret appeared first on Infoblox Blog. ## Disrupting a Criminal Scam Operation DevFeed: [Disrupting a Criminal Scam Operation]() Original publisher: [Read original article]() Published: 2026-07-31T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [Cybercrime](), [fake account fraud](), [multi-accounting fraud](), [fraud signal sharing](), [ChatGPT](), [generative-ai-tools]() Tags: [actor](), [behavior](), [chatgpt](), [crime](), [criminal](), [cryptocurrencies](), [dating](), [disrupted](), [disruption](), [emotion](), [employee](), [employment](), [exploitation](), [gambling](), [impersonation](), [investment](), [openai](), [safety](), [scam](), [support](), [using]() ### AI overview OpenAI says it banned a coordinated network likely operating around Poipet, Cambodia, that used ChatGPT to run several scams at once. The group created fake dating, investment, gambling, and law enforcement personas; generated and translated messages and forged-document images; and used emotional pressure, secrecy, urgency, and fabricated fees to solicit money. Some account activity also suggested forced labor or trafficking, including recruitment ads and records of worker debts and discipline, though the excerpt says individual circumstances could not be determined. OpenAI shared indicators with industry partners and authorities and took steps to impede the network's return. The operation's full financial losses are unknown; its communications suggested hundreds of targets, and claims of individual losses could not be independently verified. ### Source excerpt OpenAI disrupted a Cambodia-based scam operation using ChatGPT to support investment, romance, gambling, and impersonation schemes. ## Protecting Your Kid From the Internet DevFeed: [Protecting Your Kid From the Internet]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-01-13T00:00:00Z Content type: article Language: en Sources: [Scott Johnson]() Topics: [secure web gateway](), [Internet](), [Network](), [network security]() Tags: [blocking](), [circle](), [disney](), [free-speech](), [gambling](), [hardware](), [internet](), [kids](), [network](), [network-traffic](), [software-engineer](), [spam](), [wifi]() ### AI overview The author, despite being philosophically opposed to Internet filtering, argues that parents should protect minors from adult content online. Disney Circle is described as a hardware device that plugs into a Wi-Fi network, intercepts traffic, and blocks categories such as pornography, gambling, and drug-related content. Its parental controls allow category-level choices, including permitting Facebook while blocking Twitter. The author recommends checking compatibility before buying: Circle may not work with mesh networking products such as Eero. The excerpt does not explain the technical filtering method or provide further evaluation. ### Source excerpt I don't care how permissive / how liberal a parent you are, I suspect that you would have problems with your relatively young kid (let's say under 18) going into a place like this: I look at putting the Internet into your home without some kind of Internet filtering as analogous to letting your kids walk into a place like this and not stopping them. In the rest of this post I'll talk about Internet filtering and the first ever product, Disney Circle, that I've found is worth using My Credentials to Even Write This On a personal level I am a husband and father (two sons, 18 and 13). I'm also a software engineer who has been active on the Internet since the pre-web days and I've spent a large amount of my professional life dealing with spam and bad actors on the Internet. Finally I'm a firm believer in free speech and as my wife might comment, I'm the furthest thing from a prude in any way, shape or form. Those last two points, free speech and not being a prude make this a hard post for me to write as I am generally philosophically opposed to Internet filtering. Now, that said, I would never let my kid walk into a place like that pictured above and I don't think you should either - what happens there is an adult activity and there is a reason why its actually illegal to be in there if you're under 18. Internet Filtering Overview Internet filtering is just that - the idea of selectively blocking portions of the Internet i.e. filtering. From the bits, bytes and megabytes perspective, internet filtering is a hard technical problem for a whole bunch of reasons. And, alas, those actually don't matter all that much so I'm going to skip nerding out here as much as I might like to do a deep dive into Internet architecture. Disney Circle Disney Circle is a hardware box that plugs into your wifi network and acts to intercept network traffic and block things that fall into the category of malicious stuff including: Porn Gambling Drug related content There are a variety of well t ## Martingales and the Optional Stopping Theorem DevFeed: [Martingales and the Optional Stopping Theorem]() Original publisher: [Read original article]() Published: 2014-03-03T10:00:38Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Mathematics]() Tags: [2-sat](), [conditional-probability](), [expectation](), [gambling](), [martingales](), [mathematics](), [optional-stopping-theorem](), [primer](), [probability-theory](), [random](), [random-variables](), [randomized-algorithm](), [stochastic-processes](), [theory]() ### AI overview This primer introduces martingales as models of fair betting games and explains their connection to probability theory. It begins with a geometric-distribution exercise involving repeated die throws, then introduces the ABRACADABRA problem using a monkey typing random letters. ### Source excerpt This is a guest post by my colleague Adam Lelkes. The goal of this primer is to introduce an important and beautiful tool from probability theory, a model of fair betting games called martingales. In this post I will assume that the reader is familiar with the basics of probability theory. For those that need to refresh their knowledge, Jeremy's excellent primers (1, 2) are a good place to start. ## Optimally Stacking the Deck--Texas Hold 'Em DevFeed: [Optimally Stacking the Deck--Texas Hold 'Em]() Original publisher: [Read original article]() Published: 2012-04-09T09:00:26Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [Mathematics](), [Programming]() Tags: [card-games](), [fairness](), [gambling](), [permutations](), [poker](), [steepest-ascent](), [texas-hold-em]() ### AI overview The article solves a combinatorial puzzle about arranging a standard two-player Texas Hold 'Em deck so that one player wins regardless of where the deck is cut. It explains how cutting the deck normally supports fairness and frames optimal stackings as a measure of game complexity. ### Source excerpt Main Theorem: There exist optimal stackings for standard two-player Texas Hold 'Em. A Puzzle is Solved (and then some!) It's been quite a while since we first formulated the idea of an optimal stacking. In the mean time, we've gotten distracted with graduate school, preliminary exams, and the host of other interesting projects that have been going on here at Math ∩ Programming. And so months later, after traversing the homotopic hills of topology and projective plains of algebra, we've finally found time to solve the problem.