Evading Antifraud Consistently
Ever had this happen? You find a
sweet spot - a method or site thats printing money. For days or weeks youre
riding high carding like a boss. Then suddenly the well runs dry. Your transactions start
tanking orders get
cancelled left and right and youre left wondering what the fuck happened.
Most
newbies think the sites patched their holes or blocked their BINs. But thats rarely the real story. The truth? Youve been
training their AI to sniff out your bullshit without even realizing it.
View attachment 6282
These
fraud detection systems arent just dumb algorithms checking if your address matches your IP. Theyre
sophisticated learning machines evolving with every transaction that passes through them. Even your successful hits are
feeding the beast making it smarter and hungrier for your next attempt.
Youre leaving a trail of
digital breadcrumbs and then acting surprised when the AI follows it straight to your virtual doorstep. Every card you swipe every order you place is another lesson in "How to Catch a Carder 101" and youre the fucking professor.
In this guide were gonna dissect how these AI systems learn from you and more importantly how to stay a step ahead. Well cover ways to keep your patterns unpredictable techniques to mix up your approach and strategies to avoid setting off those statistical alarm bells.
Lets get one thing straight - theres no
magic bullet that lets you hit the same site forever. That fairy tale doesnt exist. This is about understanding the game at a deeper level so you can play it smarter and keep your pockets lined while other carders are bitching about their "patched" methods.
Time to elevate your game. Class is in session and today were teaching you how to
outsmart the machines that are learning to outsmart you. Pay attention or get left behind.
The Life Cycle of A Fraudulent Transaction
Lets talk about how your carded transactions come back to bite you in the ass. Weve already covered what data gets collected in my "Bypassing AI Fraud Systems" guide. Today were focusing on how these AI systems connect the dots and why one fuck-up can
burn your whole operation.
View attachment 6288
Heres the deal: Every time you card something youre not just risking that one transaction. Youre potentially
linking every transaction youve ever done and everything youll do in the future. These AI systems never throw anything away and theyre constantly re-analyzing old data.
The moment you hit that "Place Order" button the AI starts building a
web of connections. Its linking your card details device fingerprint IP address browsing patterns and a ton of other data points. And it doesnt stop there. Its comparing this transaction to every other order in its database looking for similarities.
Now heres where it gets really fucked:
Chargebacks. When a chargeback hits its like setting off a nuke in the AIs system. Suddenly that one transaction isnt just flagged as fraud. The AI goes into overdrive combing through its entire history and flagging anything remotely similar.
This is why you can be hitting a site consistently for weeks and then suddenly nothing works. Its not just that one order that got charged back thats causing problems. The chargeback triggered a
cascade effect. The AI has now linked that fraudulent transaction to every other order youve placed with similar characteristics. So the moment the first order you did charges back/disputes, it starts retroactively feeding to the neural network risks for transactions correlated to you, further adding more data to its arsenal.
View attachment 6289
And Im not just talking about obvious shit like the same card or email. These systems are smart enough to spot patterns in things like your browsing behavior the time of day you place orders or even the specific combination of items you buy. One slip-up and suddenly every transaction that shares any similarities is under scrutiny.
This cascade effect is why changing your email or using a new drop address isnt enough. The AI has already built a
profile of your behavior. Its not looking at individual data points anymore its analyzing patterns. Your entire method of operation becomes your digital fingerprint.
This process never stops. That chargeback from six months ago? Its still influencing how the AI views your current transactions. Every new piece of data every new transaction is being compared against this ever-growing web of connections.
So whats the takeaway here? Every. Fucking. Transaction. Matters. Youre not just trying to get one order through. Youre playing a long game against a system with a perfect memory and an ever-evolving understanding of fraud patterns.
Compartmentalizing Transactions
The key to not getting caught by AI fraud systems is understanding that every transaction you make is potentially linked. Its like youre weaving a web with each order and once that web gets burned you need to move to a whole new corner of the digital universe.
This doesnt just apply to your run-of-the-mill CVV carding. Even when youre working with logs you need to treat each session like its in a vacuum. Every successful hit is leaving breadcrumbs for the AI. Your job is to make those breadcrumbs lead nowhere.
关键在于：一旦你的成功率开始下降，不要只是坐在那里想哪里出了问题。要积极主动。定期
更换你的代理服务器。
切换你的反检测设置。尽可能地改变一切，确保你的下一笔交易与你之前所做的一切毫无关联。
尽力改变大多数（如果不是全部）数据点，它们可以将你当前的交易方式与之前的交易方式关联起来。不同的浏览器指纹、新的 IP 地址范围、不同的消费模式——所有这些。你希望每次刷卡操作看起来都像是来自完全不同的人。
View attachment 6290
想象一下你管理着一支国际间谍团队。每个行动都需要用各自的工具、身份和方法进行隔离。即使一个行动失败，其余的行动也能保持安全。这就是你需要达到的隔离级别。
不要重复使用成功的模式。代理反检测和卡类型或BIN的某种组合曾经有效，并不意味着你应该一直使用它。混合使用。让AI不断猜测。
记住，这些人工智能系统不断学习、不断进化。它们不仅仅关注单个数据点，还会分析数百万笔交易的模式。你的任务就是随机、不可预测，以至于你甚至不会被它们的雷达发现。
所以下次你准备刷卡的时候，问问自己：“这次和我上次的刷卡区别大吗？”如果还有一丝疑问，就赶紧改。新的代理，新的反侦测配置文件，一切都要重新来过。把每一次刷卡都当成你的第一次也是最后一次。因为在这个游戏中，一旦你感到安心，你就已经完蛋了。
画布的诀窍
我一直告诉那些问我这个问题的人一个特别的技巧，它与他们的反检测画布和客户端矩形有关。应该设置为噪声还是真实？答案是视情况而定。原因如下：
当您首次访问某个网站或跨多个平台进行卡片采集时，
真实的画布是您的最佳伙伴。为什么？因为这些人工智能系统拥有庞大的合法画布指纹数据库。大多数具有相同架构和 GPU 的设备都共享相同的画布指纹。通过展示您设备的真实精确画布，您实际上可以融入数百万合法用户。
视频：
* 隐藏文本：无法引用。*
这些欺诈检测系统所见过的设备指纹比你想象的还要多。它们知道每种硬件组合的真实画布是什么样子。当你拿着一张真正的画布照片出现时，你就是在告诉人工智能：“嘿，看，我只是一个拿着普通设备的无聊用户。” 这就像不费吹灰之力就找到了一个确凿的不在场证明。
另一方面，使用噪音（你的反检测工具会随机化画布指纹）实际上可能会引起警觉。为什么？因为你生成的画布指纹很可能与他们庞大的数据库中的任何指纹都不匹配。你不是在融入其中，而是在脱颖而出。
但棘手的是：如果你反复访问同一个网站，规则就会改变。在这种情况下，
随机画布就成了你的新朋友。让我来给你分析一下：
假设你正在
亚马逊上刷单。第一笔订单使用的是真实画布——你的欺诈评分只有20分。使用生成的画布可能会让你的评分上升到45分（虽然有点粗略，但仍然可行）。按理说，你会坚持使用真实画布，对吧？
错误的。
每次使用那块真实的画布，你都会留下相同的数字指纹。这就像每次工作都穿着同一双独特的鞋子去犯罪。日复一日，周复一周，你都在建立自己的个人资料。最初的欺诈评分是20分？它会慢慢升到30分、40分、50分。不知不觉中，你的所有交易都失败了。
这时噪音就能帮你一把了。当然，你可能一开始的欺诈评分可能更高，但问题是——每次评分都不一样。你实际上就像为每项工作都穿一双新鞋。人工智能无法构建一致的个人资料，因为你每次都不一样。
View attachment 6291
那么，要点是什么？如果你把业务分散到多个地点，或者只是试水，那就选择真正的“画布”。融入人群。但如果你反复在一个地点发力？那就制造声势。你用略高的初始风险换取长期的可持续性。
结束语
人工智能欺诈检测领域正在快速发展。昨天奏效的方法，明天可能就会让你吃亏。保持领先的关键是什么？
时刻保持警惕。
每一次交易，每一次鼠标点击，都可能给这些系统提供更多攻击你的弹药。你的工作不仅仅是刷卡——而是要像一条
数字变色龙，不断变换，永不落入俗套。
这不再仅仅关乎赚钱。而是要智胜那些专门用来抓取像你这样的人的交易的系统。这是一场高风险的猫捉老鼠游戏，而且猫咪们的智商一天比一天高。
下课了。现在出去刷卡，就像你的自由取决于它一样——因为它他妈的确实如此。
Evading Antifraud Consistently
Ever had this happen? You find a
sweet spot - a method or site thats printing money. For days or weeks youre
riding high carding like a boss. Then suddenly the well runs dry. Your transactions start
tanking orders get
cancelled left and right and youre left wondering what the fuck happened.
Most
newbies think the sites patched their holes or blocked their BINs. But thats rarely the real story. The truth? Youve been
training their AI to sniff out your bullshit without even realizing it.
View attachment 6282
These
fraud detection systems arent just dumb algorithms checking if your address matches your IP. Theyre
sophisticated learning machines evolving with every transaction that passes through them. Even your successful hits are
feeding the beast making it smarter and hungrier for your next attempt.
Youre leaving a trail of
digital breadcrumbs and then acting surprised when the AI follows it straight to your virtual doorstep. Every card you swipe every order you place is another lesson in "How to Catch a Carder 101" and youre the fucking professor.
In this guide were gonna dissect how these AI systems learn from you and more importantly how to stay a step ahead. Well cover ways to keep your patterns unpredictable techniques to mix up your approach and strategies to avoid setting off those statistical alarm bells.
Lets get one thing straight - theres no
magic bullet that lets you hit the same site forever. That fairy tale doesnt exist. This is about understanding the game at a deeper level so you can play it smarter and keep your pockets lined while other carders are bitching about their "patched" methods.
Time to elevate your game. Class is in session and today were teaching you how to
outsmart the machines that are learning to outsmart you. Pay attention or get left behind.
The Life Cycle of A Fraudulent Transaction
Lets talk about how your carded transactions come back to bite you in the ass. Weve already covered what data gets collected in my "Bypassing AI Fraud Systems" guide. Today were focusing on how these AI systems connect the dots and why one fuck-up can
burn your whole operation.
View attachment 6288
Heres the deal: Every time you card something youre not just risking that one transaction. Youre potentially
linking every transaction youve ever done and everything youll do in the future. These AI systems never throw anything away and theyre constantly re-analyzing old data.
当你点击“下单”按钮的那一刻，AI 就开始构建一个
连接网络。它会将你的银行卡信息、设备指纹、IP 地址、浏览模式以及大量其他数据点关联起来。而且它不止于此，还会将这笔交易与数据库中的其他所有订单进行比较，寻找相似之处。
现在，真正棘手的地方来了：
退款。退款就像在人工智能系统中引爆了一颗核弹。突然间，这笔交易就不仅仅是被标记为欺诈了。人工智能会疯狂地梳理整个历史记录，标记任何稍有相似之处的交易。
这就是为什么你可能会连续几周访问一个网站，然后突然就什么都打不开了。问题不仅仅是因为那笔被拒付的订单。拒付引发了
连锁反应。人工智能现在已经将这笔欺诈交易与你所有具有类似特征的订单联系起来。所以，当你的第一笔订单发生拒付/争议时，它就开始追溯性地将与你相关的交易风险反馈给神经网络，从而进一步增加其数据库。
View attachment 6289
我说的可不只是像同一张卡片或一封邮件这样显而易见的事情。这些系统足够智能，能够识别出你的浏览行为、下单时间，甚至购买商品的特定组合等模式。一旦出现一个失误，所有类似的交易都会立即受到审查。
这种级联效应解释了为什么更改邮箱地址或使用新的投递地址是不够的。人工智能已经构建了
你的行为档案。它不再关注单个数据点，而是分析模式。你的整个操作方式都变成了你的数字指纹。
这个过程永无止境。六个月前的退款？它仍然影响着人工智能如何看待你当前的交易。每一笔新交易、每一条新数据，都会与这个不断增长的连接网络进行比对。
那么，关键在于什么？每一笔交易都至关重要。你的目标不仅仅是完成一笔订单。你是在与一个拥有完美记忆、对欺诈模式不断加深理解的系统进行一场持久战。
划分交易
避免被人工智能欺诈系统发现的关键在于理解你进行的每笔交易都可能存在关联。这就像你用每笔订单编织一张网，一旦这张网被烧毁，你就需要转移到数字宇宙中一个全新的角落。
这不仅适用于普通的CVV卡片梳理。即使你处理日志，也需要将每次会话视为真空状态。每一次成功的攻击都会给AI留下一些线索。你的任务就是让这些线索毫无意义。
关键在于：一旦你的成功率开始下降，不要只是坐在那里想哪里出了问题。要积极主动。定期
更换你的代理服务器。
切换你的反检测设置。尽可能地改变一切，确保你的下一笔交易与你之前所做的一切毫无关联。
尽力改变大多数（如果不是全部）数据点，它们可以将你当前的交易方式与之前的交易方式关联起来。不同的浏览器指纹、新的 IP 地址范围、不同的消费模式——所有这些。你希望每次刷卡操作看起来都像是来自完全不同的人。
View attachment 6290
想象一下你管理着一支国际间谍团队。每个行动都需要用各自的工具、身份和方法进行隔离。即使一个行动失败，其余的行动也能保持安全。这就是你需要达到的隔离级别。
不要重复使用成功的模式。代理反检测和卡类型或BIN的某种组合曾经有效，并不意味着你应该一直使用它。混合使用。让AI不断猜测。
记住，这些人工智能系统不断学习、不断进化。它们不仅仅关注单个数据点，还会分析数百万笔交易的模式。你的任务就是随机、不可预测，以至于你甚至不会被它们的雷达发现。
所以下次你准备刷卡的时候，问问自己：“这次和我上次的刷卡区别大吗？”如果还有一丝疑问，就赶紧改。新的代理，新的反侦测配置文件，一切都要重新来过。把每一次刷卡都当成你的第一次也是最后一次。因为在这个游戏中，一旦你感到安心，你就已经完蛋了。
画布的诀窍
我一直告诉那些问我这个问题的人一个特别的技巧，它与他们的反检测画布和客户端矩形有关。应该设置为噪声还是真实？答案是视情况而定。原因如下：
当您首次访问某个网站或跨多个平台进行卡片采集时，
真实的画布是您的最佳伙伴。为什么？因为这些人工智能系统拥有庞大的合法画布指纹数据库。大多数具有相同架构和 GPU 的设备都共享相同的画布指纹。通过展示您设备的真实精确画布，您实际上可以融入数百万合法用户。
视频：
* 隐藏文本：无法引用。*
这些欺诈检测系统所见过的设备指纹比你想象的还要多。它们知道每种硬件组合的真实画布是什么样子。当你拿着一张真正的画布照片出现时，你就是在告诉人工智能：“嘿，看，我只是一个拿着普通设备的无聊用户。” 这就像不费吹灰之力就找到了一个确凿的不在场证明。
另一方面，使用噪音（你的反检测工具会随机化画布指纹）实际上可能会引起警觉。为什么？因为你生成的画布指纹很可能与他们庞大的数据库中的任何指纹都不匹配。你不是在融入其中，而是在脱颖而出。
但棘手的是：如果你反复访问同一个网站，规则就会改变。在这种情况下，
随机画布就成了你的新朋友。让我来给你分析一下：
假设你正在
亚马逊上刷单。第一笔订单使用的是真实画布——你的欺诈评分只有20分。使用生成的画布可能会让你的评分上升到45分（虽然有点粗略，但仍然可行）。按理说，你会坚持使用真实画布，对吧？
错误的。
每次使用那块真实的画布，你都会留下相同的数字指纹。这就像每次工作都穿着同一双独特的鞋子去犯罪。日复一日，周复一周，你都在建立自己的个人资料。最初的欺诈评分是20分？它会慢慢升到30分、40分、50分。不知不觉中，你的所有交易都失败了。
这时噪音就能帮你一把了。当然，你可能一开始的欺诈评分可能更高，但问题是——每次评分都不一样。你实际上就像为每项工作都穿一双新鞋。人工智能无法构建一致的个人资料，因为你每次都不一样。
View attachment 6291
那么，要点是什么？如果你把业务分散到多个地点，或者只是试水，那就选择真正的“画布”。融入人群。但如果你反复在一个地点发力？那就制造声势。你用略高的初始风险换取长期的可持续性。
结束语
人工智能欺诈检测领域正在快速发展。昨天奏效的方法，明天可能就会让你吃亏。保持领先的关键是什么？
时刻保持警惕。
每一次交易，每一次鼠标点击，都可能给这些系统提供更多攻击你的弹药。你的工作不仅仅是刷卡——而是要像一条
数字变色龙，不断变换，永不落入俗套。
这不再仅仅关乎赚钱。而是要智胜那些专门用来抓取像你这样的人的交易的系统。这是一场高风险的猫捉老鼠游戏，而且猫咪们的智商一天比一天高。
下课了。现在出去刷卡，就像你的自由取决于它一样——因为它他妈的确实如此。
Thank you ..,
