Online Card Game Security: Collusion, Multi-Accounting, Decision-Influencing Software, and How Players Can Spot It
Online card games in 2026 are safer than they were a decade ago, but the risks haven’t disappeared — they have simply become more technical. The most damaging threats for everyday players are not “rigged decks”, but organised cheating methods: collusion between multiple accounts, the use of hidden extra accounts by one person, and real-time decision support tools that quietly turn an average player into a near-perfect one. This article breaks down how these schemes work, what patterns you can realistically notice as a player, and what practical actions help you protect your bankroll without turning every session into paranoia.
Collusion: when opponents secretly play as a team
Collusion is a coordinated effort by two or more players to gain an unfair advantage by sharing information, shaping pots, or manipulating table dynamics. In poker-style games, colluders may soft-play each other (avoiding big confrontations), funnel chips to a chosen “winner”, or coordinate raises and folds to isolate honest players. In trick-taking and some casino-style card games, the aim may be to exchange hidden signals, delay actions to confirm decisions, or steer outcomes by controlling tempo. Unlike a one-off bad beat, collusion is profitable over large samples, which is why it is a priority for major operators’ security teams.
Most large poker rooms in 2025–2026 publicly acknowledge collusion as a key integrity threat and rely on behavioural analytics, pattern detection, and reporting systems to investigate it. The basic idea is simple: cheaters leave statistical fingerprints. Repeatedly avoiding each other, consistently showing unusual folding patterns against a partner, or creating “strange” action sequences that push a third player out of a hand are all behaviours that become obvious in the data when they happen frequently. Modern detection is often AI-assisted and looks for patterns that don’t match natural competitive play.
From a player’s perspective, the danger is that collusion does not always look like dramatic, obvious cheating. It can be subtle: two players constantly pressuring you with raises while rarely clashing, or one player making “bad” folds that only make sense if they know another player is strong. The best way to protect yourself is to treat repeated odd patterns seriously, not single events. Keep a short written log (time, table, usernames, what looked suspicious), because reports supported by specifics are more likely to be actioned.
How to recognise collusion patterns during real play
The most practical red flags are frequency-based. If you keep seeing the same two accounts at the same tables, especially in low-traffic formats, and their actions repeatedly benefit each other, that is worth noticing. Another classic sign is “selective aggression”: both players play aggressively against everyone else, but suddenly become passive when facing each other, even in spots where competitive players would normally apply pressure. In tournament formats, chip-dumping is a major clue — one player repeatedly risks stacks with weak hands in situations that conveniently transfer chips to a partner.
Pay attention to timing and communication-like behaviour. In some collusion setups, one player stalls or speeds up decisions in a way that seems to “coordinate” the hand flow. While timing alone proves nothing, consistent timing patterns combined with odd betting decisions can be meaningful. Also watch for unusual multi-way pots where one player makes a sacrifice move (like a bizarre overbet bluff) that only makes sense if they know another player will benefit from it.
When you suspect collusion, your most effective move is often the simplest: change tables and reduce exposure. Cheaters profit from volume, not from a single victim. If the room offers hand histories, save them. Then report via the official channel, including hand IDs. Major operators actively investigate these cases, because collusion destroys liquidity and trust — and in regulated markets they are expected to demonstrate strong security controls and auditing processes.
Multi-accounting: one person hiding behind many identities
Multi-accounting happens when a single individual uses multiple accounts, sometimes with different devices, names, or even people acting as “fronts”. The goal can be simple: bypass limits, claim multiple bonuses, or sit in the same games to create artificial advantages. In poker-style environments, multi-accounting can be used to scout tables, avoid detection after a ban, or quietly replace an account that has built a suspicious history. In games with ladders, leaderboards, or promotions, it can also be used to manipulate rankings and rewards.
By 2026, reputable operators rely on layered identity controls to reduce multi-accounting, including document verification, payment-method matching, device fingerprinting, and behavioural checks. In some jurisdictions, regulators push licensed companies to adopt strong information security standards and secure remote gambling requirements. For example, the UK Gambling Commission’s Remote gambling and software technical standards (RTS) include security requirements aligned with recognised information security practices, and updates have continued through 2025. That regulatory pressure is one reason multi-accounting is harder to sustain long-term on well-regulated sites.
However, “harder” doesn’t mean “impossible”. Multi-accounting still appears most often in softer markets, in unregulated environments, or in games where onboarding is minimal. It can also involve “account renting” — a skilled player using someone else’s verified account. For honest players, the harm is that the playing field becomes distorted: you may think you’re playing against a range of different opponents when you are actually facing one coordinated strategy.
What multi-accounting looks like from the player’s side
Multi-accounting rarely announces itself, but there are patterns. You might notice multiple new accounts that appear together, play similar styles, and rotate in and out at the same times. In poker, this can show up as several “different” players using almost identical bet sizes across many situations, or making strangely consistent strategic choices that feel solver-like. In some formats, a multi-accounter will use one account to pressure tables and another to sit in the best seat or pick off weakened opponents.
Another clue is unnatural “skill distribution”. If a brand-new account plays with unusual confidence in high-complexity spots, never tilts, and seems to avoid learning curves, it may be a recycled identity. Again, none of these signs are proof — but repeated patterns justify caution. If the room allows it, check player notes, track repeated appearances, and avoid consistently suspicious clusters.
If you believe multi-accounting is occurring, document concrete examples and report them. Operators can compare device fingerprints, payment details, and log-in patterns, which players cannot see. In 2025–2026, many sites increasingly rely on behavioural and technical checks — including biometrics and machine-learning-driven anomaly detection — precisely because traditional “one account per person” rules are easy to break without these tools.
Decision-influencing software: real-time assistance, solvers, and hidden tools
The most controversial cheating threat in 2026 is real-time assistance (RTA): software that suggests optimal decisions during play. This can be as simple as charts and preflop ranges that are referenced live, or as advanced as solver-based tools that calculate actions in real time. In practice, RTA can turn a mediocre player into someone making near game-theory-optimal decisions, especially in formats where standard lines dominate. The reason it’s so damaging is that it doesn’t need teamwork; it scales with one person and enough volume.
High-profile enforcement actions have made RTA a mainstream integrity issue. In 2025, GGPoker publicly announced bans of accounts for RTA use in collaboration with GTO Wizard, and industry coverage discussed the growing pressure on poker rooms to identify solver-driven behaviour. The issue is not theoretical: it has been openly discussed in poker media, with examples of bans and rule clarifications, and it has led to stricter fair-play messaging across major networks.
Importantly, there is a difference between studying with tools and using them while playing. Most operators allow training tools for off-table learning, but prohibit live assistance. That boundary is increasingly enforced, and some tournament organisers have also tightened rules around AI and live coaching. From a player’s perspective, this matters because “too perfect” play patterns are no longer just a suspicion — they are part of an active anti-cheating war, with operators using data, client monitoring, and targeted investigations.
Practical signs of RTA or decision-substitution tools
The most realistic sign is not that someone never loses, but that their decisions are unusually consistent in complex, high-pressure spots. Humans change gears, adjust emotionally, and sometimes choose slightly different lines for the same scenario. Solver-driven decision-making often looks “smooth” and uniform: bet sizes are consistently precise, timing is strangely stable, and lines remain disciplined even under stress. Over a large enough sample, this can feel like you are always playing against the same calibrated engine rather than a person.
Another sign is the absence of typical leaks. Even very strong players show small weaknesses: occasional overbluffs, overfolding, impatience, or predictable timing. RTA users can appear oddly balanced, especially in spots where most human players deviate. That said, you should avoid accusing individuals publicly — false accusations can backfire and rarely help. The smart approach is to protect yourself: reduce table time against suspicious accounts, change game selection, and report to the operator with hand references.
Finally, understand that detection is improving, but it isn’t perfect. Some operators use client-side tools, behavioural analytics, and investigations triggered by reports, and the wider industry is discussing stronger identity controls and security requirements. Players contribute by reporting clearly, choosing reputable, regulated operators, and avoiding environments that have weak verification, weak enforcement, or unclear rules about live assistance.