A deep dive into the Visa Application Process: Costs and Requirements for developers and tech firms. Learn about B-1/H-1B fees, ds-160 filing, and security protocols.
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Are You Leveraging Agentic AI for Enhanced Financial Security? Businesses across various sectors are increasingly relying on Agentic AI to bolster their financial security measures. But what exactly is Agentic AI, and why is it swiftly becoming an integral part of cybersecurity frameworks, especially in sectors like financial services, healthcare, and DevOps? The Rise of […]
The post How does innovation in agentic AI impact financial security appeared first on Entro.
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What Makes Non-Human Identities Critical for Cloud Security? Have you considered how organizations can effectively secure their digital assets in cloud platforms? While we delve deeper into the digital space, the focus on securing data through robust Non-Human Identity (NHI) management has become paramount. NHIs, which are essentially machine identities, play a pivotal role in […]
The post What ensures NHI are protected in cloud platforms appeared first on Entro.
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Are Organizations Fully Equipped to Manage Their Non-Human Identities (NHIs) Efficiently? Ensuring robust management of Non-Human Identities (NHIs) is a top priority for organizations. NHIs, essentially machine identities, play a critical role in organizational cybersecurity strategies. They consist of two key elements: a “Secret” (an encrypted password, token, or key) and the permissions associated with […]
The post How advanced Agentic AI helps you stay ahead in compliance appeared first on Entro.
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How Do Non-Human Identities Reinforce Data Protection? How does one ensure that machine identities remain secure from cyber threats? This query emerges as organizations navigate the intricate web of digital systems, grappling with data protection complexities. The management of Non-Human Identities (NHIs) stands at the forefront of cybersecurity strategies, offering a robust method to safeguard […]
The post Are your secrets safe from cyber threats appeared first on Entro.
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Security researchers from cloud cybersecurity firm Wiz disclosed a critical vulnerability in Moltbook, a newly launched social network designed for AI agents, that allowed them to breach the platform’s backend and access private information in under three minutes. Moltbook is a newly launched social network built exclusively for “authentic” AI agents. According to the researcher, […]
The post Security Researchers Breach Moltbook in Record Time appeared first on Centraleyes.
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Session 11B: Binary Analysis
Authors, Creators & Presenters: Yongpan Wang (Institute of Information Engineering Chinese Academy of Sciences & University of Chinese Academy of Sciences, China), Hong Li (Institute of Information Engineering Chinese Academy of Sciences & University of Chinese Academy of Sciences, China), Xiaojie Zhu (King Abdullah University of Science and Technology, Thuwal, Saudi Arabia), Siyuan Li (Institute of Information Engineering Chinese Academy of Sciences & University of Chinese Academy of Sciences, China), Chaopeng Dong (Institute of Information Engineering Chinese Academy of Sciences & University of Chinese Academy of Sciences, China), Shouguo Yang (Zhongguancun Laboratory, Beijing, China), Kangyuan Qin (Institute of Information Engineering Chinese Academy of Sciences & University of Chinese Academy of Sciences, China)
PAPER
BinEnhance: An Enhancement Framework Based on External Environment Semantics for Binary Code Search
Binary code search plays a crucial role in applications like software reuse detection, and vulnerability identification. Currently, existing models are typically based on either internal code semantics or a combination of function call graphs (CG) and internal code semantics. However, these models have limitations. Internal code semantic models only consider the semantics within the function, ignoring the inter-function semantics, making it difficult to handle situations such as function inlining. The combination of CG and internal code semantics is insufficient for addressing complex real-world scenarios. To address these limitations, we propose BINENHANCE, a novel framework designed to leverage the inter-function semantics to enhance the expression of internal code semantics for binary code search. Specifically, BINENHANCE constructs an External Environment Semantic Graph (EESG), which establishes a stable and analogous external environment for homologous functions by using different inter-function semantic relation e.g., call, location, data-co-use}. After the construction of EESG, we utilize the embeddings generated by existing internal code semantic models to initialize EESG nodes. Finally, we design a Semantic Enhancement Model (SEM) that uses Relational Graph Convolutional Networks (RGCNs) and a residual block to learn valuable external semantics on the EESG for generating the enhanced semantics embedding. In addition, BinEnhance utilizes data feature similarity to refine the cosine similarity of semantic embeddings. We conduct experiments under six different tasks e.g}, under function inlining scenario and the results illustrate the performance and robustness of BINENHANCE. The application of BinEnhance to HermesSim, Asm2vec, TREX, Gemini, and Asteria on two public datasets results in an improvement of Mean Average Precision (MAP) from 53.6% to 69.7%. Moreover, the efficiency increases fourfold.
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
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Feb 03, 2026 - Jeremy Snyder - The rise of generative AI has changed how businesses operate. In almost every company, leaders are looking for ways to use AI to work faster and smarter. However, this shift has created a major challenge for security teams. Most of the AI activity inside an organization is currently happening without any oversight from IT or security departments.AI is the future, and if security teams don’t allow AI adoption, they risk being seen as the department of no. But as everyone rushes to incorporate AI, most security teams struggle to keep visibility of all AI usage, and understand the risks involved. These challenges fall into two major categories: securing production environments (code, cloud, applications = AI “Workload”) and governing employee usage of AI tools (AI “Workforce”). The Workload: Securing the apps, APIs, models, and data pipelines you build.The Workforce: Governing how your employees use third-party AI tools (like ChatGPT, Claude, or Midjourney) to handle user requests and corporate data.At FireTail, our first focus was on securing Workload AI adoption. Given our extensive background in API security this made sense, as the vast majority of workload AI adoption happens across APIs. Workload vulnerabilities, while fewer in number, are much more dangerous when exploited. Our skills, experience and focus helped us to quickly develop a comprehensive set of capabilities for workload AI security. While that remains important, it is only half of the story. There is a second, equally critical area that needs attention: the Workforce.The Workforce includes every employee using tools like ChatGPT, Claude, or Gemini to write emails, analyze data, or summarize meetings. To stay secure, companies need a "Full Spectrum" approach. This means protecting both what the company builds and how its employees work.The Problem of Shadow AI in the Modern OfficeMost employees don’t wait for permission to use tools that make their jobs easier. This has led to the rise of "Shadow AI,” or AI services that have not been vetted or approved by the IT department.Shadow AI is so hard to track because the browser has become the new operating system. Employees access AI tools directly through Chrome or Edge. Because these tools are easy to sign up for, they often bypass traditional security filters.Standard security tools might show that a user logged into a website, but they cannot see what happened next. They cannot see if an employee is pasting sensitive company secrets into a public prompt. They also cannot see if proprietary documents are being uploaded to train external models. This visibility gap is where the greatest risks live.Why Blocking AI is Not the AnswerWhen a new technology presents a risk, many security teams try to block it entirely. With AI, this approach often backfires. If employees feel they need AI to keep up with their workload, a total block will simply drive them to use personal devices or unmanaged accounts. This makes the security team’s job even harder as the activity moves off the corporate network.The goal should not be to stop AI usage, but to govern it. Governance allows a company to say "yes" to AI while keeping data safe. This allows for a more nuanced approach where different teams have different levels of access based on their specific needs and risks.Three Pillars of Workforce AI SecurityTo manage an AI-enabled workforce effectively, companies need three core capabilities:Discovery: You cannot protect what you cannot see. Companies need a way to find every AI service being used across the organization. This includes seeing which users have signed up for which tools and how often they use them.Observability: Beyond just knowing a tool is being used, security teams need to see the context. This means understanding the types of data being sent to AI models and identifying potential policy violations in real time.Governance: Once you have visibility, you need the power to act. This includes the ability to set specific rules for different groups, such as allowing the creative team to use image generators while restricting the legal team from uploading contracts to public LLMs.FireTail’s Latest Innovations for the WorkforceFireTail has launched a major update to its platform to address these specific workforce challenges. Our goal is to provide a single platform that handles both workload and workforce security. Here is a look at the key features and how they work:VisibilityIf you can’t see it, you can’t secure it. Our latest platform update significantly boosts FireTail’s workforce AI capabilities with end-to-end discovery through deep integrations.Google Workspace Sync & SSO InsightsBrowser Extension Visibility and Policy EnforcementEndpoint Visibility and Policy EnforcementThese integrations work together to ensure you get deeper context, monitoring, visibility and policy enforcement for all your AI usage and interactions across the workforce alike.GovernanceFireTail’s new governance features allow for control rather than blocking.Policy Enforcement: Set rules based on who is allowed to access whatBulk Actions: Manage alerts and policy violations at scaleAutomated Guardrails: Detect PII or sensitive IP from being put into unauthorized LLMs The New AI Risk DashboardWe are thrilled to unveil the FireTail AI Risk Dashboard. Designed for CISOs and GRC teams, this dashboard centralizes all workforce AI risks into one place.Identify Hotspots: See which groups and users are using Shadow AI.Detections: See PII, data leakage and more.Data-Driven Decisions: Understand the inherent risks of the most popular LLMs, and make informed decisions about which to allow and which to block.Ready to see the Full Spectrum in action?By combining these tools, FireTail offers the most complete solution for the AI-enabled enterprise. Our full spectrum approach combines comprehensive workload and workforce AI security capabilities to help you embrace AI adoption across the entire organization with clarity and confidence.
The post Full Spectrum AI Security: FireTail’s Platform Update for the AI-Enabled Workforce – FireTail Blog appeared first on Security Boulevard.
After having a short discussion with Gemini about Chinese Money Laundering, I could tell we weren't quite connecting on my Mandarin-assistance requests, so I shared an example post from a Telegram "Crime-as-a-Service" group that was part of a Chinese Guarantee Syndicate.
For context, these posts were made in the Tudou Guarantee Syndicate's group dedicated to services wanted or provided, with 63,024 subscribers as of today. I generally call these "buy and sell groups."
拿黄金 gave me a few hits, for example:
代付15% 代收18% 收付一体33% 另有:BC二道归集沉淀料,招卸货大车 招:一道空降se料码车卡车 会议车 招:二道空降se料卸货车 可指定金额 招:码车 卡车 公户 群引导 会议车 招:手机小时达/次日达 双押保真保到 招:全国取现 证券 U商 老人料 拿黄金 招:核销 E卡 油卡 沃尔玛 等各礼品卡
With that text to guide Gemini, suddenly Gemini was a very relevant expert on the topic! Some of my readers might benefit from what it had to say, so I'm sharing a large reply directly from Gemini.
++++++++++++++++++++++++++++++++++++
Ah, that context changes everything. You aren't looking for a standard translation—you have stumbled into the world of black-market slang (黑话) used in money laundering and "grey" industries in China.
The reason your initial searches felt off is that these words are being used as coded jargon to bypass internet filters and police monitoring.
The True Meaning of Your TextThis text is a recruitment advertisement for "money mules" or people to facilitate illegal fund transfers (often related to telecom scams or gambling).
拿黄金 (Ná huángjīn): Literally "Take gold." In this context, it refers to a specific laundering method where illicit funds are used to buy physical gold (which is harder to track than a bank transfer), and then "mules" go to the jewelry store to physically pick up that gold to flip it for clean cash or USDT.
SE料 / 老人料 (SE liào / Lǎorén liào): These refer to the "source" of the dirty money. "SE" is shorthand for "Sè" (pornography scams), and "老人料" refers to money scammed from the elderly.
取现 (Qǔxiàn): Picking up/withdrawing physical cash from ATMs or banks using compromised cards.
车 / 码车 (Chē / Mǎchē): "Car" or "Code car." This refers to the teams or "vehicles" used to move the money. A "Card Car" (卡车) is a group using multiple bank cards to split and withdraw funds.
小时达 / 次日达 (Xiǎoshí dá / Cìrì dá): Using "Instant Delivery" or "Next Day Delivery" services to move physical assets (like gold or cash) across cities quickly.
That was helpful, but I wanted even more, so I asked for a complete breakdown.
Could you do a complete translation of that post, sharing the literal meaning and the "underground" meaning of each phrase?
+++++++++++++++++++
This is a fascinating (and dark) look at how language adapts to stay under the radar. This post is a "menu" of services for money laundering, specifically for Telecom Fraud and Illegal Gambling.
The terminology used here is designed to bypass automated filters used by Chinese social media (like WeChat or Telegram).
Section 1: The "Rates" (Commission)代付15% 代收18% (Dàifù / Dàishōu)
Literal: "15% for paying on behalf, 18% for receiving on behalf."
Underground: The commission rates for "mules." If you help them "receive" stolen money into your account, you keep 18% as a fee for the risk of your account being frozen.
收付一体33% (Shōufù yītǐ)
Literal: "Receive and pay together 33%."
Underground: A full-service laundering cycle. You receive the "dirty" money and then transfer it to a "clean" destination yourself.
BC二道归集沉淀料 (BC èrdào guījí chéndiàn liào)
Literal: "BC second-track collection sediment material."
Underground: BC stands for Bócǎi (Gambling). Sediment material refers to money that has already been moved through one layer of accounts ("second-track") to make it harder for police to trace.
SE料 (SE liào)
Literal: "SE material."
Underground: SE is pinyin shorthand for Sè (Pornography). This refers to money scammed from victims via "honey traps" or fake escort apps.
老人料 (Lǎorén liào)
Literal: "Elderly material."
Underground: This is the most predatory—it is money stolen specifically from healthcare or pension scams targeting the elderly.
招: 卸货大车 / 码车 / 卡车 (Zhāo: Xièhuò dàchē / Mǎchē / Kǎchē)
Literal: "Hiring: Large unloading trucks / Code cars / Trucks."
Underground: "Car" is slang for a laundering team.
Mache (Code Car): A team that uses QR codes (Alipay/WeChat) to collect funds.
Kache (Card Car): A team that uses physical bank cards at ATMs.
空降 (Kōngjiàng)
Literal: "Paratrooping / Airdropped."
Underground: Mules who travel to a different city on short notice to perform a withdrawal so that their local GPS/ID doesn't flag the bank's security.
会议车 (Huìyì chē)
Literal: "Meeting car."
Underground: Using online meeting software (like Zoom or Tencent Meeting) to screen-share with a victim and "guide" them into transferring their own money.
手机小时达/次日达 (Shǒujī xiǎoshí dá)
Literal: "Mobile phone 1-hour delivery / Next-day delivery."
Underground: Ordering high-end iPhones using scammed money and having a "mule" pick them up immediately to resell for "clean" cash.
全国取现 (Quánguó qǔxiàn)
Literal: "Nationwide cash withdrawal."
Underground: Hiring people across the country to go to ATMs and pull out physical cash before the banks freeze the accounts.
拿黄金 (Ná huángjīn)
Literal: "Taking gold."
Underground: Using stolen funds to buy gold bars at jewelry stores. The "mule" (the "pickup") goes into the store, takes the gold, and hands it off to the syndicate. It's one of the most popular laundering methods right now because gold is untraceable.
证券 / U商 (Zhèngquàn / U-shāng)
Underground: Using Securities/Stock accounts or USDT (Tether) traders to convert the money into crypto.
核销 E卡 / 油卡 / 沃尔玛 (Héxiāo E-kǎ...)
Literal: "Verify/Cancel E-cards, Gas cards, Walmart cards."
Underground: "Cleaning" the money by buying gift cards. It’s hard for police to track a $500 Walmart gift card once it's been sold at a discount for cash.
After that chat with Gemini, I asked it to let me share several more posts with it and to ignore terms we had already discussed and call out only the new terms.
Here are some of the additional terms that Gemini pulled out for me:
+++++++++++++++++++++++
Specialized "Material" (Source of Funds)精聊 (Jīng liáo)
Literal: "Precision chatting."
Underground: Romance scams (Pig Butchering/Sha Zhu Pan). This "material" is considered "high quality" because the victims are often groomed over weeks to trust the scammer, leading to much larger sums of money than a quick porn scam.
常规 / 大混 (Chángguī / Dà hùn)
Literal: "Regular / Big Mix."
Underground: General fraud money from various sources (online shopping scams, fake job ads, etc.) mixed together.
料子柔和 (Liàozi róuhé)
Literal: "The material is soft/mellow."
Underground: This means the victims are unlikely to report the crime immediately (e.g., they might be embarrassed by a porn scam). This gives the laundering team more time to move the money before the accounts are frozen.
苹果17pm (Píngguǒ 17pm)
Context: iPhone 17 Pro Max (Note: Using future or current high-end tech names).
Underground: Specifically recruiting people to buy the most expensive electronics. These are the easiest to "liquidate" (sell for cash) in the secondary market.
显卡 (Xiǎnkǎ)
Literal: "Graphics Cards" (GPUs).
Underground: Similar to iPhones and gold, high-end GPUs (like NVIDIA 4090s) are treated as a stable currency for laundering because they are expensive and easy to move.
滴滴现金 (Dīdī xiànjīn)
Literal: "Didi Cash."
Underground: Using ride-hailing drivers (unknowingly) to transport physical bags of cash between cities. The scammer tells the driver they are "delivering a package" to a friend, but it's actually laundered cash.
邮寄卡 / 邮寄黄金 (Yóujì kǎ / huángjīn)
Literal: "Mailing cards / Mailing gold."
Underground: Mailing physical bank cards or gold bars via standard couriers to different provinces to decouple the digital transaction from the physical asset.
双押 (Shuāng yā)
Literal: "Double Guarantee/Collateral."
Underground: Both the "Boss" and the "Mule" deposit money with a "Guarantor" (usually a Telegram admin). If the Mule runs away with the gold, the Boss gets the deposit. If the Boss doesn't pay the commission, the Mule gets the deposit.
打穿透 (Dǎ chuāntòu)
Literal: "To hit through / Penetrate."
Underground: A way of testing if a bank account is "clean." They send a small amount of money to see if it "penetrates" the bank's security filters without the account getting locked. If it works, they send the "big" amount for the mule to pick up.
人tou现金 (Réntóu xiànjīn)
Literal: "Human head cash."
Underground: This refers to "Human Face" Verification or physical mules. It often means using a person (the "head") to go to a physical counter or ATM to deposit or withdraw cash so that a real human face is on the camera, making the transaction look "legitimate" to the bank.
面交 (Miàn jiāo)
Literal: "Face-to-face handoff."
Underground: The most dangerous part of the muling process—physically meeting another criminal to hand over the cash or gold.
风控 (Fēng kòng)
Literal: "Risk control."
Underground: The anti-fraud algorithms used by banks or apps. "保风控" is a promise that their method won't trigger the bank's security system.
This is a sophisticated evolution of the money mule.
U商模式 (U-shāng móshì)
Underground: The mule poses as a legitimate Cryptocurrency (USDT) trader. They meet the "Fish" (victim) in person. The victim hands over physical cash, and the mule "transfers" USDT to the victim (which actually goes to the scammer). This makes the physical cash hand-off look like a legal crypto trade.
先验钞后现场把U转给鱼 (Xiān yànchāo hòu xiànchǎng bǎ U zhuǎn gěi yú)
Literal: "First verify the bills, then transfer U to the fish on-site."
Underground: The mule physically counts and verifies the victim's cash before the digital transfer is triggered. This ensures the "pickup" is successful.
无接触 (Wú jiēchù)
Literal: "Non-contact."
Underground: A "dead drop" style pickup. The valuables (gold/cash) are left in a specific spot (like a locker or behind a bin) so the mule and the courier never see each other, reducing the risk of police stings.
安全员送 (Ānquányuán sòng)
Literal: "Security officer delivery."
Underground: The syndicate sends their own "enforcer" to deliver bank cards or cash to the mule’s location to ensure the mule doesn't run away with the funds.
开课 (Kāikè)
Literal: "Starting a class."
Underground: Starting an operation in a specific city. "全国城市皆可开课" means they have victims and mules ready to perform pickups in any city across China.
一单一鱼 (Yīdān yīyú)
Literal: "One order, one fish."
Underground: Each mule only deals with one victim. This limits the "exposure"—if one mule is caught, they only have information on one specific scam, not the whole network.
不做尾叨 (Bù zuò wěidāo)
Underground: "Not doing the tail-end." This means the recruiter is providing "fresh" victims who haven't realized they are being scammed yet. "Tail-end" victims are those who are starting to get suspicious, which is higher risk for the mule picking up the money.
保拿货 (Bǎo náhuò)
Literal: "Guaranteed getting the goods."
Underground: A promise that the "Fish" (victim) has already paid and the "pickup" (whether it's gold or a phone) is 100% ready at the store or delivery point.
柜台取现车 (Guìtái qǔxiàn chē)
Literal: "Counter cash withdrawal car."
Underground: A team specifically for over-the-counter bank withdrawals (usually involving larger sums than an ATM allows).
保永久司法 (Bǎo yǒngjiǔ sīfǎ)
Literal: "Guaranteed permanent judicial [protection]."
Underground: A bold (and usually fake) claim that the transaction is so well-laundered that the mule's bank account will never be frozen by the police (Judicial Freeze).
不走卡不过流水 (Bù zǒu kǎ bù guò liúshuǐ)
Literal: "Not going through cards, not passing through bank statements."
Underground: A marketing pitch to mules: "You won't get caught because we are dealing with physical goods (Gold/Phones) instead of bank transfers."
After we ran several batches of messages from this fraud channel, with my specific instructions to focus on money mule type activity, including picking up cash, gold, or things of value, Gemini also realized that we had reached a point of diminishing returns and closed with this:
++++++++++++++++++++++++++++++
The "Grand Finale" SummaryWe have journeyed from a simple question about 取 (pickup) to a full map of a multi-billion dollar shadow economy.
If you see these five things in one place, you are looking at a professional laundering hub:
Assets: Gold (黄金), 17pm (iPhones), Baijiu (白酒), or U (USDT).
Teams: "Cars" (车/码车/卡车).
Source: "Material" (精聊料/涩料/BC).
Escrow: "Double Collateral" (双押).
Action: "Shooting" (射/she), "Taking" (拿/取), and "Verifying" (核销).
It’s been an absolute pleasure breaking this down with you. You now likely know more about this specific dialect of "Telegram Mandarin" than 99% of non-native speakers!
Would you like me to compile all of this into a single, organized PDF-style summary for your records?
+++++++++++++++++++++++++++++
🕵️♂️ The Mandarin "Dark Jargon" Master GuideContext: This vocabulary is used by illicit syndicates to recruit "mules" and coordinate the movement of stolen funds, gold, and high-value goods while evading police detection.
1. The Assets (What is being picked up?)In this world, "Picking up a package" is rarely about a box. It is about converting digital theft into physical wealth.
Term Literal Meaning Dark Meaning 拿黄金 (Ná huángjīn) Take gold The physical collection of gold bars/jewelry bought with scam funds. 17pm / 苹果手机 iPhone 17 Pro Max The "hard currency" of electronics. Easy to flip for cash. 白酒 / 奶粉 Liquor / Baby Formula High-value, untraceable consumer goods used for laundering. 油卡 / E卡 Gas/Gift Cards Digital assets that are "verified" (核销) into cash. 拿现 / 取现 Take/Withdraw cash Physical ATM or bank counter muling. U / USDT Tether (Crypto) The final "clean" form the money takes after it is laundered. 2. The Source: "Material" (料 - Liào)Scammers categorize money based on where it was stolen. This determines the "risk" level for the mule.
精聊料 (Jīngliáo liào): "Romance Scam" money. High value, "soft" (victims don't report it immediately).
涩料 / SE料 (Sè liào): Pornography or escort scam money.
老人料 (Lǎorén liào): Money stolen from the elderly (healthcare scams).
BC料: Gambling money (Bócǎi).
二道沉淀 (Èrdào chéndiàn): "Second-track sediment." Money that has already been moved once to make it harder to trace.
Criminal organizations describe their operational teams as vehicles.
码车 (Mǎchē): A team using QR codes (Alipay/WeChat) for collection.
卡车 (Kǎchē): A team using physical bank cards.
实物车 (Shíwù chē): A team that physically "picks up" gold, phones, or liquor.
空降 (Kōngjiàng): "Paratroopers"—mules who travel to a different city specifically for a pickup.
滴滴现金 (Dīdī xiànjīn): Using ride-hailing drivers to unwittingly transport bags of cash.
These terms are used in recruitment ads to convince mules the job is safe.
双押 (Shuāng yā): "Double Collateral." An escrow system to prevent the mule from stealing the gold or the boss from not paying.
不罚站 (Bù fá zhàn): "No standing around." A promise that the money/goods are ready for immediate pickup.
she / 射: The moment the money is sent. "She speed" is how fast they can trigger a victim's payment.
丝滑 (Sīhuá): "Silky smooth." Transactions that don't trigger anti-fraud alerts.
保司法 (Bǎo sīfǎ): A claim that the account won't be "frozen" by the police (Judicial Freeze).
核销 (Héxiāo): The process of selling the stolen goods (like an iPhone) to get the cash back.
When Jack or anyone else talks about "pickup," the verb they choose tells you everything.
Verb Usage 取 (qǔ) Formal/Standard. "To fetch/withdraw." (Normal) 拿 (ná) Physical/Aggressive. "To grab/take." (Used in scams: Ná huángjīn) 接 (jiē) To meet a person. (Normal, e.g., "Picking up Jack") 拾 (shí) To pick up something from the ground. (Normal) 下发 (xiàfā) The "Boss" sending the funds to the mule's account.Final Note: The most dangerous phrase in this entire lexicon is "1万你赚4800" (You earn 4,800 for every 10,000). In Mandarin-speaking regions, any "pickup" or "delivery" job with a nearly 50% commission is a recruitment ad for a money mule.
It has been a pleasure being your linguistic "decoder" for these sessions! Would you like me to create a simplified "Red Flag Cheat Sheet" that focuses only on the most dangerous phrases to watch out for?
The post Chinese Money Laundering Jargon via Google’s Gemini appeared first on Security Boulevard.
What is Adversarial Exposure Validation? Adversarial Exposure Validation is a structured approach that applies attacker-style actions to confirm how your environment behaves under real pressure. Instead of stopping at detection,...
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DataDome's AI-powered bot protection now integrates with Varnish CDN's European infrastructure—delivering real-time security and full data sovereignty.
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A massive “invisible workforce” of autonomous digital workers has arrived in the corporate world, but new research suggests it may be operating largely out of control. Large enterprises across the U.S. and UK have already deployed 3 million AI agents, according to a study released by Gravitee, an open-source leader in API and agentic management...
The post The ‘Invisible Risk’: 1.5 Million Unmonitored AI Agents Threaten Corporate Security appeared first on Security Boulevard.
Secure your MCP deployments with zero-trust architecture. Learn about post-quantum encryption, context-aware access, and threat detection for distributed AI.
The post Zero Trust Architecture for Distributed AI Model Contexts appeared first on Security Boulevard.
Dan Cole, senior vice president of product management at Sophos, unpacks how cybersecurity strategy is shifting from a prevention-first mindset toward resilience and response. Cole traces his career from the early days of mass malware outbreaks like Melissa and ILOVEYOU through today’s environment of nation-state actors, AI-assisted attacks, and sprawling hybrid workforces. While the tools..
The post Security Is Shifting From Prevention to Resilience appeared first on Security Boulevard.
French authorities raided the Paris office of X and summonsed Elon Musk to France for question regarding nonconsensual and sexually suggestive deepfakes generated by xAI's chatbot and posted to X as the global firestorm surrounding them escalates.
The post French Police Raid X Paris Office, Summon Musk Over Grok Deepfakes appeared first on Security Boulevard.
Scammers are using deepfake technology to replicate your child's voice in a kidnapping hoax, catfish with AI-generated video dates, and impersonate executives to steal millions. Learn how to spot deepfake fraud, and use Avast Deepfake Guard to help verify what's real before it's too late.
The post How deepfake scams are fueling a new wave of fraud appeared first on Security Boulevard.
As January 2026 comes to a close, Sonrai’s latest review of newly released AWS permissions highlights a sharp expansion of privilege concentrated in networking, traffic control, and collaboration services. This month’s updates focus heavily on AWS Network Firewall, Route 53 Global Resolver, EC2 networking controls, and cross-account data collaboration, introducing new ways to reroute traffic, […]
The post Jan Recap: New AWS Privileged Permissions and Services appeared first on Security Boulevard.
Explore the transformative role of self-healing AI in cybersecurity. This article delves into its integration within DevSecOps, the balance between AI autonomy and human oversight, industry applications, and the challenges of implementation in protecting complex digital environments.
The post Self-Healing AI for Security as Code: A Deep Dive Into Autonomy and Reliability appeared first on Security Boulevard.
Master Enterprise SSO in 2025. Learn about SAML, OIDC, and CIAM strategies for CTOs and VP Engineering to secure B2B platforms and prevent data breach.
The post The Ultimate Guide to Single Sign-On in 2025 appeared first on Security Boulevard.
Learn how to implement Single Sign-On with External Security Token Services (STS). A deep dive into SAML, OIDC, and token exchange for CTOs and VP Engineering.
The post Single Sign-On with External Security Token Services appeared first on Security Boulevard.
