Key Takeaways

• Discovery has been commoditized. Frontier AI models like Mythos and GPT 5.5 are making vulnerability discovery cheap, fast, and broadly accessible.
• The defender's job is to match the speed. Manual triage has lost the throughput race.
• Threat intelligence is the prioritization layer at machine speed. Recorded Future Intelligence observed only 446 actively exploited CVEs in 2025 against approximately 50,000 disclosed — less than 1%.
• Recorded Future's agentic processing plus Autonomous Threat Operations can be the answer. It offers detection signatures in just 31 minutes and automated action across more than 100 integrations, with third-party reach coming soon. Attackers are operating at this speed. Your defenses have to match them.

It’s now a question I get daily: “What is Recorded Future doing about Mythos?”

It's a fair question. Anthropic's Project Glasswing announcement, paired with the vulnerability research benchmarks coming out of OpenAI's GPT 5.5, has made AI-driven vulnerability discovery a board-level topic in a matter of weeks.

To answer that question, first we need to discuss the operational problem defenders actually face and why threat intelligence can be the best way to counter it at machine speed. Then we'll get into what Recorded Future is already deploying to solve it: our agentic processing.

The problem: drowning in signal, starving for context

Even before AI and the news of Mythos’ capabilities and speed, defenders were struggling. Signal volume was outpacing analyst capacity. Coverage gaps widened daily as long-tail vendors and niche platforms went unmonitored. Raw findings arrived without root cause, threat-actor relevance, or vetted remediation paths. Producing one analyst-grade enrichment took hours of senior researcher time. The math didn't work at enterprise scale.

The reality check: 50,000 disclosed, 446 actually exploited

The data point that should anchor any conversation about the AI vulnerability surge: The NVD disclosed approximately 50,000 CVEs in 2025. Recorded Future Intelligence observed only 446 actively exploited in the wild — less than 1%.

Finding vulnerabilities is one thing, but knowing which ones matter, to which environments, against which adversaries, and with which compensating controls already in place is a whole different matter. Forrester put it directly: “The limiting factor in security is no longer the ability and knowledge to find problems — it's the ability to absorb, prioritize, and act on them before adversaries do.” The bottleneck has always been on the absorb-prioritize-act side. The find side was never the problem.

Frontier AI models accelerate the finding side. Threat intelligence is what helps close the prioritization gap on the fixing side.

The prioritization filter: what turns 50,000 into 446

Threat intelligence is operational, not philosophical. It comes down to four signals that distinguish the small fraction of CVEs adversaries actually weaponize from the overwhelming majority that they don't. These four signals are non-negotiable to be able to get to the prioritizing at speed and scale:

1. A live risk score. A composite index of exploitation likelihood and impact, recalculated continuously as evidence shifts. Not a static CVSS rating; a live measure of which vulnerabilities are weaponizable, exploitable in modern environments, and likely to be picked up by threat actors.
2. Active exploitation in the wild. Observed exploitation evidence — not theoretical PoC availability, but documented use against real systems by real actors. Sources include open and dark web telemetry, vendor disclosures, government advisories (CISA KEV catalog and equivalents), and primary research like what Insikt Group® produces.
3. Ransomware actor association. Mapping CVEs to specific ransomware operators and access broker activity. The same vulnerability used by a financially motivated ransomware affiliate against your sector is a different incident than the same CVE in a state-actor toolkit targeting a different region.
4. Sector and campaign targeting. Which threat actors are targeting your industry, which TTPs they're using, which exposures map to known tooling.

Together, these four signals are how you prioritize what actually matters for any given defender.

Recorded Future's answer: agentic processing plus Autonomous Threat Operations

If attackers are moving at Mythos speed, your defenses need to keep up using agentic processing and Autonomous Threat Operations. This is my answer to the question we started with about what Recorded Future is doing about the new world we live in.

Agentic processing is the production system that turns exposure signals into deployable intelligence. The pipeline reads descriptions, vendor advisories, and patch diffs the moment they appear. It produces production-ready detection signatures — documented detection logic, evidence specification, passive fingerprinting strategy. It writes analyst-grade enrichment for every finding — root cause, exploit mechanics, threat-actor associations, prioritized defensive controls with deploy-time and false-positive estimates, validated remediation tasks with acceptance criteria and rollback plans.

It’s end-to-end target: identification to deployment in customer environments in only 31 minutes. Internal averages run lower. No security team operating manual triage workflows is matching that throughput.

That content can reach every relevant control point in your environment through Autonomous Threat Operations (ATO).

ATO turns agentic-processing outputs and correlated intelligence into operational action across over 100 integrations spanning SIEM, SOAR, EDR/XDR, NGFW, vulnerability management, threat intelligence platforms, identity and access management, email and cloud security, GRC, and threat-informed defense. It continuously deploys priority intelligence, runs autonomous threat hunts, pushes detection rules, and takes preventive action without analyst hours spent on manual correlation. The 8-to-12 hours of weekly correlation work most analyst teams perform manually is almost entirely eliminated. The hunting cadence becomes 24/7.

Soon, ATO will do this across your attack surface and third parties, as vendor exposure has been the most common path to breach for the past three years.

The five-stage pipeline that produces all of this — threat signals, intelligent enrichment, validation and verification, structured output, and customer workflow — runs continuously. Production-ready content is in customer environments within minutes of the originating disclosure across every category of threat the platform detects.

Why agentic processing is different, and why your organization needs it

Four things distinguish agentic processing from anything a security team can build manually:

1. Hours → minutes. A complete enriched finding can be produced in minutes, not the hours of manual research the same output used to require.
2. Order-of-magnitude efficiency. Based on Recorded Future R&D findings, per-vulnerability triage runs at 40x the efficiency of manual research effort, enabling coverage at scale your team cannot achieve by hand.
3. Long-tail coverage. Localized vendors, niche platforms, and legacy systems become economically viable to cover at breadth.
4. Always current. Continuous refresh cycles keep intelligence accurate as threats evolve.

These benefits represent the difference between preventing threats pre-attack and absorbing the damage after.

Let’s look at an example of what agentic processing does at machine speed.

React2Shell with agentic processing

Take CVE-2025-55182 — React2Shell, a pre-authentication remote code execution vulnerability in React Server Components. Within minutes of disclosure, agentic processing produced:

1. An Attack Surface Intelligence (ASI) detection signature with documented detection logic, evidence specification, and passive fingerprinting strategy
2. Root cause and exploit mechanics down to the specific code path
3. Active campaigns, threat-actor associations, observed exploitation evidence
4. Confidence-graded indicators of compromise with detection commands
5. Prioritized defensive controls with deploy-time and false-positive estimates
6. Manual validation procedures, remediation tasks with acceptance criteria and rollback plans, and post-remediation verification commands

In this new Mythos age, this type of agentic processing and speed is going to be required as the new baseline.

Beyond vulnerabilities: the same playbook generalizes

Vulnerability disclosure is the most visible trigger for the intelligence-at-speed pattern, but it isn't the only one. The same operational logic applies wherever a new threat signal surfaces and a defender needs to act on it before the adversary monetizes it.

When a brand impersonation site is stood up, the defensive sequence is the same: detection, intelligence enrichment (registrant, registrar, hosting infrastructure, historical campaign association), prioritized defensive controls (takedown coordination, blocking at email and web layers, alerting affected employees), and verification that the takedown landed. Recorded Future's Digital Risk Protection runs this loop continuously across the open, deep, and dark web.

When a stolen credential surfaces in an infostealer log market, Identity Intelligence runs the same pattern: detection of credentials tied to your environment, enrichment with infection context (malware family, device, other credentials in the same log, MFA cookie capture status), prioritized response (force password reset, revoke active sessions, alert the user), and verification.

The pattern is the posture. Apply intelligence at machine speed wherever the adversary is acting, across every category of threat surface. Vulnerabilities are one trigger. The work generalizes. Recorded Future is operationalizing intelligence at machine speed across our four solutions, Cyber Operations, Digital Risk Protection, Third-Party Risk, and Payment Fraud Intelligence.

What this means for defenders

The operational response to AI-driven vulnerability discovery is what separates organizations that contain exposures from those that wake up to incident response calls.

We are seeing customers set up automation to move faster in response to this new reality. A large enterprise in the financial services sector used Recorded Future to transform their vulnerability management workflow. Following a major patching effort across the organization, the team built out automation between their vulnerability scanning and IT service management tools. The result: a streamlined, repeatable process and an estimated weekly time savings of over 20 hours for the team.

We recommend taking these five actions so you can respond as well:

1. Move to autonomous intelligence-led security. Asset inventories are no longer sufficient without knowing if a vulnerability exists, if it is a priority, and what the blast radius is.
2. Compress your disclosure-to-detection cycle to minutes. Manual signature creation runs in days. Adversaries are moving in hours. Whatever your current cycle time, halving it is now baseline.
3. Demand intelligence-led prioritization, not severity scores. CVSS and EPSS describe the universe of vulnerabilities, not which ones are being weaponized against your sector this quarter. Threat intelligence helps you prioritize.
4. Action across the full stack, not just the endpoint. AI-driven discovery surfaces flaws in app code, kernels, libraries, and cloud configurations. Defensive response requires reaching wherever the attacker might use the bug.
5. Apply the same posture across all four threat surfaces. Cyber Operations, Digital Risk Protection, Third-Party Risk, and Payment Fraud all face the same AI-augmented attacker clock speed.

AI-driven vulnerability discovery is here. The big question is whether your systems can operate at attacker speed, with a depth of intelligence that survives executive scrutiny. If the answer isn’t a confident yes, then Mythos and the category behind it have already shifted the math against you.

See it in production. Request a demo to see Recorded Future Intelligence and Autonomous Threat Operations turn a vulnerability disclosure into deployable detection and action across your stack within minutes.

In April 2026, Insikt Group® identified 37 high-impact vulnerabilities that should be prioritized for remediation, 35 of which had a Very Critical Recorded Future Risk Score. This represents a 19% increase from last month.

31 of the 37 were included in the US Cybersecurity and Infrastructure Security Agency (CISA)’s Known Exploited Vulnerabilities (KEV) catalog, and six were surfaced only through honeypot data. Those six CVEs associated with honeypots are available only to Recorded Future customers.

Those 37 vulnerabilities affected products from 23 vendors. Microsoft accounted for approximately 22%, while the remaining exposure was concentrated across a range of enterprise-facing vendors, particularly security and systems management tools, collaboration and server platforms, developer and application-delivery software, remote support tools, and network-edge infrastructure.

In April, Insikt Group created Nuclei templates for the missing authentication vulnerabilities in Nginx UI (CVE-2026-33032) and Marimo (CVE-2026-39987). These Nuclei templates are available to Recorded Future customers.

Quick Reference: April 2026 Vulnerability Table

All 31 vulnerabilities below were actively exploited in April 2026. This table does not include the 6 CVEs associated with honeypot activity. The table below also provides examples of public PoCs identified by Insikt Group®. These PoCs were not tested for accuracy or efficacy. Vulnerability management teams should exercise caution and verify the validity of PoCs before testing.

Table 1: List of vulnerabilities that were actively exploited in April based on Recorded Future data (excluding honeypot-sourced CVEs).

Key Trends: March 2026

• In April 2026, seven of the 37 vulnerabilities in this report were linked to ransomware activity.
  • Six are explicitly tied to Storm-1175's Medusa ransomware operations.
  • CISA has also linked CVE-2026-41940 with known ransomware use (Sorry Ransomware, per open source reporting).
  • Additionally, threat actors exploited CVE-2024-3721 in TBK DVR devices to deliver the Nexcorium botnet.
• Sixteen of the 37 vulnerabilities enabled remote code execution (RCE), affecting products from twelve vendors: Adobe, Apache, D-Link, Fortinet, Google, Ivanti, Kentico, Marimo, Microsoft, SimpleHelp, TrueConf, and Wazuh.
• Insikt Group® identified public proof-of-concept (PoC) exploits for 24 of the 37 vulnerabilities in this report.
• The most commonly observed flaws this month were CWE-22 (Path Traversal), followed by CWE-94 (Code Injection), CWE-20 (Improper Input Validation), and CWE-306 (Missing Authentication for Critical Function).
• Three of the 37 vulnerabilities are at least five years old, with the oldest approximately seventeen years old, reinforcing how attackers continue to exploit long-known weaknesses in environments where patching has lagged. Additionally, the fastest observed time from a vulnerability’s public disclosure to exploitation was two days.

Exploitation Analysis

This section highlights some of the highest-impact, actively exploited vulnerabilities this month, specifically those linked to known threat actor campaigns, that have public PoC exploits available, or for which Insikt Group® has created Nuclei templates to detect the vulnerability. Vulnerabilities with no meaningful public technical detail are summarized in the disclosures table only.

Threat Actors Exploit TBK DVR Vulnerability (CVE-2024-3721) to Deliver Nexcorium

On April 17, 2026, FortiGuard Labs (@FortiGuardLabs on X, formerly known as Twitter), associated with Fortinet (@Fortinet), published a technical analysis detailing a campaign that exploits TBK Digital Video Recorder (DVR) devices to deliver Nexcorium, a Mirai-based botnet. A TBK DVR device is a surveillance system recorder that captures, stores, and allows playback or remote viewing of video from connected security cameras. According to FortiGuard Labs, Nexcorium targets TBK DVR-4104 and DVR-4216 systems by exploiting CVE-2024-3721, an operating system (OS) command injection vulnerability that allows remote threat actors to execute arbitrary system commands.

Based on FortiGuard Labs’ analysis, the campaign begins with the exploitation of CVE-2024-3721 through crafted requests that manipulate the mdb and mdc arguments in TBK DVR devices, which delivers a downloader script named dvr. The exploit includes the HTTP header X-Hacked-By with the value Nexus Team - Exploited By Erratic. The dvr script retrieves Nexcorium binaries with filenames beginning with nexuscorp for architectures such as ARM, MIPS R3000, and x86-64. The dvr script then sets the Nexcorium binaries’ permissions to 777, and executes them with an argument that identifies the compromised system.

Further technical details associated with this activity, including sample analysis and IoCs, are available to Recorded Future customers via Insikt Group reporting.

Recorded Future customers can also access Malware Intelligence queries, which surface samples that connect to known network indicators.

As of April 15, 2026, NIST enriches only CVEs that appear in the CISA Known Exploited Vulnerabilities catalog, federal government software, or software designated critical under Executive Order 14028. Everything else carries a "Lowest Priority" status: no CVSS score, no affected product mappings, no weakness classification. NIST enriched roughly 42,000 CVEs in 2025, and submissions in early 2026 are running about a third higher year-over-year. Industry estimates suggest the prioritized categories will cover only 15–20% of anticipated CVE volume going forward.

For teams whose vulnerability management workflows depend on CVSS scores from NVD, this could create an operational gap. The CVEs in the unenriched backlog can signify real vulnerabilities affecting real software. They don't necessarily stop mattering because NIST didn't get to them.

Recorded Future does not believe that the solution is to source CVSS scores faster. Instead, Recorded Future endeavors to provide the signals that actually reflect attacker behavior. CVSS was designed to characterize the technical properties of a vulnerability — attack vector, complexity, required privileges, potential impact. CVSS was not designed with patch prioritization as a prime concern. This distinction has always existed; the growing gap in NVD enrichment increases the importance of the right intelligence and insights that can capture attacker behavior in real time.

Where vulnerability risk actually originates

Exploit code surfaces on GitHub. Proof-of-concept development gets discussed in offensive security forums and underground communities. Ransomware operators evaluate which vulnerabilities fit their deployment pipelines. Threat actors incorporate specific CVEs into their toolkits and begin scanning in search of exploitable targets.

At some point during or after that sequence, a CVE gets assigned and, under the previous policy, would eventually be enriched by NVD. By the time a practitioner sees a CVSS score in their scanner, the risk may already have materialized.

The delay between attacker use and the assignment of a CVE and CVSS score is not a new dynamic. For this reason, Recorded Future's vulnerability Risk Scores were never built to depend on NVD enrichment.

The intelligence that determines whether a vulnerability is dangerous originates in the technical communities, underground markets, exploit repositories, and malware ecosystems where attackers work. It does not come from institutional databases processing CVEs up to weeks or months post-assignment. NVD's policy change doesn't create a gap in Recorded Future's coverage because NVD is not the primary signal behind Recorded Future Vulnerability Intelligence.

What the model actually weighs

Recorded Future's risk scoring maps directly to the vulnerability weaponization lifecycle. Many of the signals fire based on where a CVE sits on that path, not on what NIST has or hasn't scored.

The signals that carry the most weight are those tied to active exploitation in the wild — malware samples observed by Recorded Future's collection infrastructure, ransomware operations validated by Insikt Group® analysts, and other direct evidence of attacker use. Confirmed exploitation activity carries the most weight in the model, regardless of a CVE's CVSS score. These are the signals that answer the question practitioners actually need answered: is someone using this right now?

Below active exploitation, the model tracks proof-of-concept availability, including the distinction between a verified and unverified PoC. Verified exploit code that demonstrates remote execution is a materially different signal from an unverified proof of concept of unknown reliability. As an example, exploit code on GitHub is not theoretical risk; it usually compresses the time between disclosure and weaponization. Recorded Future Risk Scores treat it accordingly.

In addition to these collection and analytic capabilities, Recorded Future tracks web reporting about a CVE before NVD has published enrichment data. For the majority of new CVEs going forward, this pre-NVD signal may be the earliest structured intelligence available anywhere. A CVE that NIST has marked Lowest Priority can still accumulate signals across many dimensions. As a result, the absence of a CVSS score in NVD doesn't create a blind spot in Recorded Future's assessment.

CVSS still matters. It just isn't the foundation.

CVSS scores flow into the model from multiple sources. Many CVE numbering authorities (CNAs) supply CVSS scores at the point of submission, and CVSS coverage across published CVEs remained above 90% in 2025 even as NVD's independent enrichment narrowed. That doesn't mean CNA-supplied scores are interchangeable with NVD's. Academic analyses of dual-scored CVEs have documented divergence rates above 50% throughout the past decade, reaching 70% in 2023, with disagreements sometimes large enough to move a vulnerability across severity tiers. For CVEs where neither NVD nor a CNA has provided scoring, Recorded Future independently assigns scores through its own analysis. CVSS occupies one position in the model, alongside signals grounded in observable attacker behavior, and those signals operate independently of whether a CVSS score exists at all.

What to do with this

Audit where your prioritization signals come from. If your program is relying entirely or primarily on CVSS scores pulled from NVD, you may have exposure, not just from the existing backlog, but from every new CVE entering the ecosystem under the new policy.

Recorded Future Vulnerability Intelligence, as a part of the Cyber Operations solution, scores every CVE against the full signal set — exploitation activity, malware and ransomware associations, proof-of-concept availability, threat actor targeting, and analyst-validated intelligence. All independent of NVD's enrichment pipeline. See this prioritization and automation in action with this click-through tour.

See how Vulnerability Intelligence integrates with your existing vulnerability management workflow — request a demo.

Executive Summary

Artificial intelligence is often discussed as a tool for automating and accelerating existing cybersecurity workflows. While that framing is accurate, it is incomplete. The most consequential shift occurs when AI is combined with threat intelligence — both intelligence about attacker capabilities and TTPs, and intelligence about our own defensive weaknesses and exposure. This combination produces qualitatively new defensive capabilities that may, for the first time, begin to structurally narrow the long-standing asymmetry between attackers and defenders.

This memo examines what is genuinely new about AI-enabled defense, with particular emphasis on how the fusion of threat intelligence and AI reasoning changes the strategic calculus. It also argues that in the end, it is a question of who can most efficiently use scarce resources (compute and energy) to get the upper hand. Intelligence guides defenders in how to best use these resources to defend, thereby changing the balance of power against adversaries.

The Traditional Defender’s Dilemma

The core asymmetry in cybersecurity is well understood: defenders must protect every possible attack surface, while attackers only need to find one exploitable weakness. Defenders operate under constraints — budgets, compliance mandates, uptime requirements — while attackers can be patient, selective, and asymmetric.

Traditionally, threat intelligence has been consumed by defenders as a feed: indicators of compromise, malware signatures, and published advisories. This intelligence was valuable but largely reactive and disconnected from the defender’s own environment. Knowing that a threat group uses a particular technique is only useful if you can rapidly assess whether that technique works against your infrastructure. That assessment has historically required scarce human expertise, time, and tooling — precisely the resources defenders lack.

The Automation Layer: Real But Evolutionary

A significant portion of AI’s current impact on defense is best described as automation of existing processes: faster alert triage, automated enrichment, accelerated patch prioritisation, and AI-assisted Tier 1 SOC analysis. These improvements are valuable — they compress response times, reduce analyst fatigue, and address chronic staffing shortages — but they are conceptually extensions of workflows that already existed.

Similarly, AI can automate the ingestion and normalisation of threat intelligence feeds, reducing the manual work of parsing reports and extracting indicators. This is useful, but it does not change what defenders can fundamentally do with that intelligence. The real transformation lies elsewhere.

The Convergence: Where Threat Intelligence Meets AI Reasoning

The most significant shift is not AI applied to defense in isolation, nor threat intelligence consumed as a feed. It is the convergence of the two: AI systems that can reason simultaneously over what attackers are doing and what defenders are exposed to, in real time, at scale. This convergence produces capabilities that did not previously exist.

1. Connecting Attacker TTPs to Your Actual Exposure

Traditionally, a threat intelligence report might tell you that a particular adversary group is exploiting a vulnerability in a specific product, or is targeting your sector using a known technique chain. Acting on that information used to require an analyst to manually map those TTPs against your environment: do we run that product? Is the vulnerable version deployed? Are the relevant network paths open? Are our detection rules adequate for that technique?

AI can perform this mapping continuously and at scale. When a new threat report lands, an AI system can immediately cross-reference the described TTPs against a live model of your infrastructure, your patching state, your detection coverage, and your segmentation — and surface a prioritised assessment of actual risk, not theoretical risk. This transforms threat intelligence from awareness into actionable, environment-specific defense guidance.

2. Fusing Offensive Intelligence With Defensive Weakness Data

Defenders have long maintained two separate bodies of knowledge: external threat intelligence (what adversaries are capable of and likely to do) and internal vulnerability and exposure data (what weaknesses exist in our own environment). These have typically lived in different systems, managed by different teams, and reconciled manually and infrequently.

AI enables continuous fusion of these two streams. A model can hold both the attacker’s perspective — known TTPs, targeting patterns, tooling, and objectives — and the defender’s perspective — unpatched systems, misconfigured controls, overprivileged accounts, and detection gaps — and reason about the intersection. The result is not a vulnerability list or a threat report, but an integrated picture of where the attacker’s capabilities meet our specific weaknesses. This is the analysis that the best red teams produce during an engagement, except it can now run continuously rather than quarterly.

3. Predictive Prioritisation Based on Adversary Behaviour

Patch prioritisation has traditionally been driven by CVSS scores — a measure of theoretical severity that ignores both attacker intent and environmental context. AI models trained on threat intelligence can reorder priorities based on which vulnerabilities are actually being exploited in the wild, by which adversary groups, against which sectors, using which delivery mechanisms. Combined with internal exposure data, this enables prioritisation that better reflects real-world risk rather than abstract severity.

The same logic applies to detection engineering. Rather than building detections for every possible technique, AI can identify the techniques most likely to be used against your specific environment — based on who is targeting your sector, what tools they use, and where your coverage gaps are — and focus engineering effort where it matters most. In fact, in most cases AI will be able to build those detectors for you!

4. Reasoning Over Context at Scale

Traditional detection systems correlate events against rules. AI models can reason about events holistically, synthesising partial logs, ambiguous telemetry, and unusual configuration changes into a judgment that approximates what a senior analyst would conclude. Crucially, this reasoning can be informed by threat intelligence: not just “is this anomalous?” but “is this consistent with the tradecraft of groups known to target us?” That contextual layer makes detection both more accurate and more relevant.

5. Continuous Attack-Path Modelling

Historically, understanding one’s own exposure was a periodic exercise: run a penetration test, receive a report, remediate, repeat. AI enables a living model of the environment that continuously re-evaluates exploitable paths to critical assets as conditions change. When this model is enriched with threat intelligence — particularly information about which attack paths adversaries actually favour, and which tools they use to traverse them — the result is a dynamic, threat-informed view of exposure that stays up to date automatically, not only when your manual pen testers or red team have time to update it.

6. Adversarial Prediction During Active Incidents

During an active incident, experienced responders draw on their knowledge of attacker behaviour to anticipate likely next moves. AI models trained on threat intelligence and historical incident data can encode this reasoning and make it available to any response team. If the model recognises that the observed initial access technique and lateral movement pattern are consistent with a known adversary group, it can predict likely next steps — which credentials they will target, which persistence mechanisms they prefer, which data they are likely to exfiltrate — and help defenders get ahead of the intrusion rather than simply reacting to each new indicator.

Turning the Tables: AI-Enabled Deception

The capabilities described above are fundamentally defensive: detecting, predicting, and prioritising. But the convergence of AI and threat intelligence also opens a qualitatively different category of action — using intelligence about the attacker to actively mislead them.

From Static Honeypots to Adaptive Deception

Deception technologies such as honeypots and honeytokens have existed for decades, but they have always been constrained by how static and labour-intensive they are to deploy convincingly. A skilled attacker can often identify a honeypot by its lack of realistic activity, stale data, or inconsistencies with the surrounding environment. AI removes these constraints. AI-generated deception environments can include realistic-looking decoy infrastructure — fake services, plausible file shares, synthetic credentials, even simulated user activity patterns — that adapts dynamically in response to attacker behaviour. Rather than a static trap that a competent adversary recognises and avoids, the defender can maintain a deception layer that evolves to stay convincing.

Intelligence-Informed Decoy Placement

This capability ties directly into the threat intelligence fusion described above. If you know which TTPs a likely adversary uses, which attack paths they favour, and where your real weaknesses are, AI can place decoys precisely along the routes those adversaries are most likely to take. The deception is no longer generic; it is tailored to the specific threat. A decoy credential can mimic the type of service account the adversary’s tooling is known to target. A fake file share can contain documents plausible enough to absorb attacker time and attention, and simultaneously provide new intelligence about the adversary. The threat intelligence that informs your defensive posture simultaneously informs your deception strategy. This is “Machine Counter Intelligence”!

Imposing Costs and Eroding Attacker Confidence

AI-generated deception at scale inverts a piece of the traditional asymmetry. Attackers who encounter a pervasive deception layer must spend significant time and effort distinguishing real assets from fake ones. Every interaction with a decoy wastes their resources, degrades their confidence in the intelligence they have gathered, and increases the risk that they will trigger an alert. In effect, the attacker now faces a version of the defender’s dilemma: they must verify everything, while the defender only needs one decoy to succeed.

Active Intelligence Collection Through Engagement

Perhaps most significantly, AI can interact with attackers inside deception environments in ways that feel plausible, drawing out more of their tooling, techniques, and objectives. This turns deception from a passive tripwire into an active intelligence-gathering operation. The tradecraft revealed through these engagements feeds back into the threat intelligence cycle, improving the defender’s understanding of the adversary and refining future defensive and deceptive measures. The result is a virtuous loop: intelligence informs deception, deception generates new intelligence.

There is an inherent tension in active deception engagement: traditional incident response doctrine prioritises minimising dwell time, while deception-based intelligence collection deliberately extends it. The risks are real — containment failure if the deception boundary isn't airtight, resource cost of sustained monitoring, potential legal and regulatory questions about why an attacker was permitted to remain active, and the possibility that a sophisticated adversary recognises the deception and feeds false signals back to poison your intelligence. These risks do not invalidate the approach, but they define the conditions under which it works. Active engagement requires genuinely isolated deception infrastructure, and clear decision frameworks for when to engage.

Democratising Access to Intelligence-Driven Defense

A less obvious but structurally significant change is that AI lowers the barrier to performing intelligence-driven defense. When an analyst can query in plain language — “which of our externally-facing systems are vulnerable to techniques used by a certain threat group in the last 90 days?” — and receive an accurate, contextualised answer, the skill requirement for effective threat-informed defense drops substantially. This is not doing an old thing faster; it is enabling a different operating model in which threat intelligence becomes a working tool for the entire security team, not just the analysts who specialise in it.

Strategic Implications

The most profound implication is that defenders have historically been reactive because they lacked the cognitive bandwidth to continuously fuse offensive intelligence with their own exposure data. AI makes this fusion not only possible but economically viable for organisations that could never previously afford dedicated threat intelligence teams, red teams, and continuous assessment programmes.

This changes the nature of the defender’s dilemma. The traditional framing — “defenders must protect everything; attackers only need one way in” — assumed that defenders could not know, in real time, which parts of their attack surface are most likely to be targeted. AI-enabled threat intelligence fusion challenges that assumption. If defenders can continuously identify the most probable attack paths based on current adversary behaviour and their own specific weaknesses, they can concentrate resources where they matter most. The dilemma does not disappear, but the defender is no longer operating blindly, but can take control.

The key asymmetry is therefore shifting from “attacker versus defender” to “AI-augmented versus non-augmented.” Organisations that integrate AI with robust threat intelligence programmes may find themselves closer to parity with attackers than at any point in the history of the field. Those that do not will face an even steeper version of the traditional dilemma, as AI-empowered adversaries exploit the widening gap.

Final Words

The emergence of fully autonomous AI agents on both sides raises unresolved questions. If attackers deploy autonomous offensive agents that can chain exploits and adapt to defenses without human guidance, defenders will need equally autonomous systems — systems that consume threat intelligence, assess exposure, and act on the results without waiting for human approval. The governance, trust, and control challenges this creates are substantial, but the journey towards this goal must begin now.

There is also a risk that the intelligence-AI feedback loop becomes adversarial in new ways. Sophisticated attackers who understand that defenders are using AI to map TTPs against exposure may deliberately vary their tradecraft to evade predictive models, or generate false signals to misdirect AI-driven defense. The quality and provenance of threat intelligence will become even more critical as AI amplifies both its value and the consequences of acting on flawed data — we need automation-grade intelligence!

We have not changed the basic equation: defenders must still know and mitigate every weakness, while the attacker needs only one. AI does not abolish that asymmetry, and claiming otherwise would be dishonest. What AI fused with threat intelligence does is change the terms of the contest. Instead of defending blind — treating every weakness as equally likely to be exploited — defenders can now continuously map attacker capabilities against their own specific exposure, concentrate resources on the paths adversaries actually use, and impose real friction through deception that degrades the attacker's speed advantage. The attacker still only needs one weakness, but they are now searching for it in an environment that fights back: one that predicts where they will look, places convincing traps along those paths, and learns from every encounter.

The defender may never achieve dominance, but the era of structural helplessness — of knowing that the asymmetry is permanent and unmanageable — is ending for organisations willing to invest in these capabilities. Parity in an adversarial contest is not a consolation prize; it is the condition under which skill, preparation, and operational discipline start to matter more than structural advantage.

Intro

There’s a certain energy you can only find at Recorded Future. Take that energy and bring it to London’s “Silicon Roundabout” and you get the perfect spot for Futurists to build and innovate.

Across the globe, Recorded Future is 1000+ employees working towards the same mission: Securing Our World With Intelligence.

Our London office – one of our most storied hubs – hosts a range of departments supporting both local, regional, and global operations. The office brings together 100+ cross-functional professionals from People & Talent Acquisition, Finance, Sales, Marketing, Global Services, Research, and more!

Looking back: From the Attic to The Bower

Our story in London didn’t start in the high-rise, but in a converted attic with just a handful of people and a big mission.

This passion for building something great fueled incredible growth. Sam Pullen, Director of Intelligence Services, remembers when the entire EMEA team was just about 20 people. Since 2018, we’ve gone from service a few dozen customers in the region to ~700 now.

On the left: First Recorded Future office in London. On the right: Recorded Future's newest office

On the left: First Recorded Future office in London. On the right: Recorded Future's newest office

Inside the Office

This modern high-rise building’s open-plan layout offers quite a few collaboration spaces across our office, where the team likes to have small team meetings, breaks, or even lunch.

Like all Recorded Future offices, our meeting rooms follow a unique naming convention. While Boston uses countries, and Sweden volcanoes - London chose islands. Rumors say we picked islands following a 95-day rain streak – we can neither confirm nor deny. So, in our London office, you’ll find Futurists collaborating in rooms like Bora Bora, Crete, and even San Andres.

Our Culture

What truly defines our London office is the sense of camaraderie – whether that’s competing in a friendly team padel game, testing your dartboard skills, or truly memorable summer & end of year celebrations.

Whether over summer picnics and pedalos in Hyde Park years, playing 5-a-side football in the pouring rain, or at the most recent Christmas party at the Savoy - our Futurists celebrate wins together.

Onwards & Upwards: Why Recorded Future

We asked Sam and Joe what has been the highlight of their long tenure at Recorded Future: the opportunity to build. For Sam, it has been the opportunity to build great relationships with clients over nearly a decade. For Joe, it has been the opportunity to build new solutions and new ways to work towards our mission.

Ready for your next move? Join the team!

Cybersecurity is a cornerstone of our modern world, but its roots stretch back long before the internet. Far from a recent phenomenon, the field began in university labs and evolved through decades of innovation and conflict. For professionals and everyday users alike, tracing this history reveals why today's defenses exist and why vigilance remains our most critical tool.

The 1940s: Theoretical Seeds and Massive Machines

Long before the first hack, pioneers were already contemplating the risks of digital intelligence. In 1945, the Electronic Numerical Integrator and Computer (ENIAC) - the first general-purpose electronic computer - showcased the power of computing, though it was a room-sized giant reserved for military use. While the idea of a "cybercriminal" was still science fiction, the theoretical groundwork for future threats was being laid.

Mathematician John von Neumann began developing his "Theory of Self-Reproducing Automata" during this era. He proposed that a machine-based organism could replicate itself across systems - the conceptual birth of the computer virus.

Key Characteristics of This Era:

• Physical Isolation: Security meant locking the door to a room-sized machine.
• Government Monopoly: Computers were exclusive to the military and the academic elite.
• Conceptual Threats: Risks were purely mathematical theories rather than practical realities.
• The Virus Blueprint: The foundational logic for self-replicating code was established.

By understanding these early foundations, we can appreciate how a field born in the realm of theory has become the frontline of global stability.

• The ENIAC: America's Pioneering Electronic Computer
• The Life, Work, and Legacy of John von Neumann

The 1950s: Mainframes, Physical Security, and Phone Phreaking

Governments, universities, and major businesses started using large, centralized machines known as mainframes. As these computers grew more powerful, the definition of "security" still remained grounded in the physical world. During this era, data protection simply meant controlling access to the room where the hardware sat. However, a new kind of technical subculture was beginning to emerge on the fringes of the telecommunications industry.

The 1950s saw the rise of phone phreaking, where enthusiasts exploited telephone signaling frequencies to make unauthorized long-distance calls. While not yet digital hacking, this movement introduced the concept of manipulating infrastructure for unintended purposes. This culture of curiosity and boundary-pushing would eventually produce industry titans; notably, both Steve Jobs and Steve Wozniak experimented with phreaking technology before the birth of Apple.

Key Characteristics of This Era:

• Physical Perimeter: Security was defined by locks and restricted personnel access.
• Phone Phreaking: The first widespread exploitation of a technological network.
• Nascent Authentication: Password-based systems began to appear in informal, non-standardized forms.
• Fragmented Protocols: Without a connected internet, every institution developed its own isolated security rules.

These early exploits proved that even the most robust physical defenses could be bypassed by those who understood the hidden language of the systems within.

• Phone Phreaking: Hacking Before The Internet
• Year 1971: Early Days Of Phone Phreaking With Steve Wozniak & Steve Jobs

The 1960s: The First Hackers and Growing Vulnerabilities

While known primarily for its social shifts, the 1960s also marked the birth of "hacking" as a technical practice. As computers became more prevalent in universities and large institutions, a new generation of users began exploring the limits of these systems. This era shifted the focus from purely physical security to the inherent vulnerabilities within the software itself.

In 1967, IBM invited students to test a new system, only to be surprised that their probing caused system crashes and revealed weaknesses. This informal "penetration test" proved that any system accessible to users was inherently open to exploitation. It was a wake-up call that sparked the transition of cybersecurity from a passive state to an active, intellectual discipline.

Key Characteristics of This Era:

• Intentional Probing: The birth of deliberate vulnerability testing and "white hat" exploration.
• Curiosity-Driven Hacking: Hacking emerged as a way to explore system boundaries, generally motivated by academic interest rather than malice.
• Access vs. Security: Institutions realized that providing user access created inevitable security risks.
• Beyond the Lock: The realization that cybersecurity required ongoing digital strategy, not just physical barriers.

This decade transformed the computer from a mysterious black box into a challenge to be solved, proving that human ingenuity would always be the greatest threat - and defense - to any system.

• The History of Hacking
• The Evolution of Ethical Hacking: From Curiosity to Cybersecurity

The 1970s: Networking and the First "Worm"

The 1970s transformed cybersecurity from a localized concern into a networked reality. The launch of ARPANET, the precursor to the modern internet, enabled researchers to share resources across distances but also opened a doorway for autonomous software to travel between systems.

In 1971, this potential was realized with Creeper, the world's first self-replicating network program. While harmless, its ability to move across the network and display messages was a revolutionary proof of concept. In response, programmer Ray Tomlinson created Reaper - the first antivirus program - specifically designed to hunt and delete Creeper. This decade also saw the rise of Kevin Mitnick, whose exploits in the 1980s showed that psychological manipulation, or social engineering, could bypass even the strongest technical barriers.

Key Characteristics of This Era:

• Network Connectivity: ARPANET's birth created the first interconnected digital landscape.
• The First Worm: Creeper demonstrated that programs could self-propagate autonomously.
• The First Antivirus: Reaper established the "detect and delete" model of digital defense.
• Social Engineering: Early hacks highlighted that human error is often the weakest link in the security chain.

This era proved that once computers started talking to each other, the "locked door" was no longer enough to keep an intruder out.

• The Creeper Virus
• Creeper and Reaper: The First Virus and Anti-Virus

The 1980s: Personal Computers and the Birth of an Industry

The 1980s shifted computing from sterile labs to homes and offices. This explosion of connectivity via modems and floppy disks turned theoretical threats into a global reality, giving rise to the first commercial antivirus software and formal incident response teams like CERT.

Key Characteristics of This Era:

• Wild Malware: Viruses like Elk Cloner and the Brain Virus moved beyond labs to infect personal computers worldwide.
• The Morris Worm (1988): The first major network-wide disruption, leading to the first conviction under the Computer Fraud and Abuse Act (Robert Tappan Morris).
• Cyber Espionage: Marcus Hess's breach of military systems for Soviet intelligence proved that digital networks had massive geopolitical stakes.
• Ransomware Roots: The AIDS Trojan introduced the world to the concept of holding digital files hostage for payment.

The 1980s proved that as computers became personal, the threats against them became universal.

• What is Malware?
• Computer Viruses of the 80s

The 1990s: The Public Internet and Exploding Threats

As the World Wide Web went mainstream, the attack surface grew exponentially. This was the era of the "Macro Virus," where malicious code hid in everyday documents, and the dominance of Windows made it a universal target for hackers.

Key Characteristics of This Era:

• Mass-Mailers: The Melissa virus demonstrated how email could be weaponized to clog global servers in hours.
• The Encryption Standard: Netscape's SSL (1995) laid the foundation for secure online commerce and HTTPS.
• Network Fortification: Firewalls became standard equipment as businesses scrambled to block external intrusions.
• Legal Frameworks: Organizations like the EFF began fighting for digital privacy and standardized cybercrime laws.

This decade transformed cybersecurity services from a technical niche into a vital pillar of global commerce and law.

• The Melissa Virus: FAQs
• The History and Impact of Netscape's SSL

The 2000s: Professionalized Crime and Mature Defenses

The 2000s saw cybercrime scale into a high-profit industry. High-speed broadband and the rise of e-commerce meant that a single breach could compromise tens of millions of records, forcing the industry to develop more sophisticated authentication and monitoring tools.

Key Characteristics of This Era:

• Massive DDoS Attacks: "Mafiaboy" proved that even giants like Amazon and eBay could be paralyzed by flooded traffic.
• Social Engineering at Scale: The ILOVEYOU virus infected millions by exploiting human curiosity and trust.
• Data Breach Epidemics: The TJX breach accelerated the adoption of strict data security standards like PCI DSS.
• Encrypted Ransomware: In 2006, ransomware began using RSA encryption, making it nearly impossible to recover files without a key.

As attacks became more lucrative, the defensive industry responded with the first generation of modern security standards and behavioral analysis.

• 25 Years Ago: The ILOVEYOU Worm
• How a Computer Science Student Created One of the First Email Viruses That Spread by Preying on Human Nature

The 2010s: Nation-States and Digital Weapons

The 2010s shifted the focus from criminal profit to national security. Cybersecurity became a theater of war, with governments deploying digital weapons to destroy physical infrastructure and influence global politics.

Key Characteristics of This Era:

• The Stuxnet Worm: The first acknowledged cyberweapon designed to cause physical destruction to industrial equipment.
• The Snowden Leaks: Exposed the massive scale of global surveillance, sparking a decade-long debate on privacy.
• Automation and AI: Machine learning began appearing on both sides - defenders used it for detection, while attackers used it to find flaws.
• Global Ransomware: WannaCry and NotPetya showed how automated exploits could cripple hospitals and shipping lines across 150 countries.

By the end of the decade, it was clear that a line of code could be just as impactful as a physical weapon.

• Top 12 Worst Computer Viruses in History (& What They Taught Us About Cybersecurity)
• Wannacry: How the Widespread Ransomware Changed Cybersecurity

The 2020s: AI Threats and Modern Threat Intelligence

Today, the line between the physical and digital worlds has vanished. With remote work and cloud-native businesses, security is now a proactive game of "Threat Intelligence", which involves predicting and neutralizing an adversary's move before they even make it.

Key Characteristics of This Era:

• Targeting Infrastructure: Attacks on power grids and water systems have raised the stakes from financial loss to public safety.
• AI-Powered Attacks: Adversaries use AI to create deepfakes and hyper-personalized phishing at speeds humans can't match.
• Predictive Defense: Modern strategy relies on Threat Intelligence, using AI to analyze patterns and stop attacks in their tracks.
• Cloud & Remote Security: The shift away from traditional offices has forced a move toward "Zero Trust" security models.

The ongoing battle between human ingenuity and artificial intelligence now defines the frontlines of our digital existence.

• What is a "Zero Trust" Security Model?
• AI and the Future of Cybersecurity
• The Impact of AI on Cyber Threat From Now to 2027
• What Is the Future of Cybersecurity?

Payment fraud is growing in scale and sophistication, affecting businesses across every industry, and as digital payments expand, so do the opportunities for bad actors to exploit vulnerabilities. Understanding how fraud works and how to prevent it is essential for protecting revenue, maintaining trust, and staying resilient in an increasingly complex threat landscape.

What Is Payment Fraud?

Payment fraud refers to the theft of money from businesses or individuals through unauthorized transactions or deceptive purchases. Fraudsters may act using their own accounts or by gaining unauthorized access to someone else's account.

While payment fraud can happen in person, online transactions are especially vulnerable. According to Juniper Research, global business losses from online payment fraud are projected to surpass $362 billion between 2023 and 2028. A business's fraud risk depends largely on its industry, the sensitivity of the data it handles, and the payment methods it accepts. The more ways customers can interact with accounts and complete purchases, the more entry points exist for bad actors to exploit.

Different Types of Payment Fraud

Fraudsters use many tactics, and below we list 14 of the most common. Given the large number of threats, businesses must prepare their teams to recognize a variety of warning signs. Strong internal communication policies, clear escalation procedures, and knowledge of the landscape are foundational to any fraud prevention strategy.

1. Phishing

Phishing is a social engineering tactic in which criminals attempt to trick people into revealing sensitive information such as account credentials or payment details. These attacks often come in the form of malicious links sent via email or text, but they can also occur over the phone. Attackers may pose as trusted figures - a friend, a bank representative, or a government official - to manipulate victims.

Prevention tips:

• Let customers know exactly how your business will contact them, including phone numbers and email addresses.
• Be transparent about what information your staff will and will not ask for.
• Alert customers to any known phishing attempts targeting your brand.
• Train employees on information security protocols and how to identify suspicious communications.

2. Credit and Debit Card Fraud

This type of fraud involves obtaining card information - either physically or digitally - and using it to make unauthorized purchases. Cards may be stolen directly, or details may be harvested through card skimming devices installed on ATMs or point-of-sale terminals. Attackers also acquire card data through phishing schemes or by purchasing stolen credentials on the dark web.

Prevention tips:

• Restrict POS system access to authorized personnel and regularly inspect payment hardware for tampering.
• Build secure, encrypted payment pages that comply with data protection standards.
• Offer customers multiple notification options for purchases and account activity.
• Warn customers never to share account or confirmation numbers with unverified sources.

3. Wire Transfer Fraud

In wire transfer fraud, criminals convince victims to send money directly to them. Because wire transfers are difficult to reverse, they are a preferred method among scammers. Attackers commonly impersonate someone the victim trusts - a family member, a company executive, or a business vendor. The use of a convincing back-story is often referred to as "social engineering." For example, an attacker may text employees pretending to be their CEO, claiming an emergency and requesting an urgent fund transfer.

Prevention tips:

• Train employees to spot the signs of social engineering and impersonation.
• Establish official communication channels and avoid conducting financial business over easily spoofed channels like text messages.
• Report and share all phishing attempts with the entire team.

4. Check Fraud

Check fraud involves using counterfeit or altered checks to make payments or writing checks from accounts that lack sufficient funds. Fake checks may be digitally printed or modified versions of real checks. In some cases, the check is genuine but drawn from a closed account.

Prevention tips:

• Implement software that verifies the authenticity of checks.
• Train staff to recognize the visual and physical signs of fraudulent checks.

5. Chargeback and Refund Fraud

Also known as "friendly fraud," chargeback fraud occurs when a customer makes a legitimate purchase and then falsely claims a refund - either directly from the business or through their credit card company. This type of fraud is particularly tricky because it can be hard to distinguish from genuine disputes, especially when delivery or service quality is involved.

Prevention tips:

• Validate customer information, including billing addresses and card security codes.
• Use payment platforms that include fraud protection and dispute automation tools.
• Respond to refund and chargeback requests quickly.
• Minimize legitimate chargebacks by fulfilling orders accurately and on time.

6. Identity Theft

Identity theft happens when a criminal obtains someone's personal information and uses it for financial gain or to make purchases in someone else's name. For businesses, a common result is having to deal with chargebacks after customers discover fraudulent charges on their accounts. Although the primary victim is the customer, businesses have a responsibility to prevent data breaches that expose customer information in the first place.

Prevention tips:

• Train employees to recognize phishing and follow secure information handling practices.
• Ensure your payment systems comply with PCI DSS (Payment Card Industry Data Security Standard) requirements.

7. Account Takeover Fraud

Account takeover (ATO) fraud typically follows identity theft. Once attackers obtain a user's credentials, they change the password and contact information to lock the real owner out. From there, they may use the account for fraudulent purchases or sell it to other bad actors.

Prevention tips:

• Enforce strong password requirements for all accounts.
• Require two-factor authentication (2FA) and send confirmation alerts for any significant account changes.
• Notify customers of purchases and account modifications in real time.

8. New Account Fraud

New account fraud (NAF) occurs when someone uses stolen or fabricated identities to open new lines of credit or accounts. These fraudulent accounts can then be used to make purchases or commit further fraud down the line.

Prevention tips:

• Require multi-factor authentication (MFA) - not just email verification - during account creation.
• Verify address details and card security information during transactions.
• Use fraud protection tools that leverage machine learning to detect unusual account creation patterns.

9. Gift Card Fraud

Gift card fraud is a social engineering scam where criminals pressure victims into purchasing gift cards and handing over the card numbers. Once the numbers are given, the funds are essentially unrecoverable, making this a popular method among scammers.

Prevention tips:

• Display warnings about gift card scams during the checkout process.
• Remind customers never to share gift card numbers with people they don't personally know.
• Educate in-store staff to recognize signs of gift card fraud and when to escalate the situation.

10. Merchant Identity Theft

In merchant identity theft, attackers impersonate legitimate businesses or vendors to defraud customers or partner organizations. They may use phishing to extract employee credentials and gain access to business systems, or they may pose as a trusted vendor and redirect payments to themselves.

Prevention tips:

• Train staff to identify phishing attempts and follow secure communication practices.
• Establish verification procedures when communicating with vendors and business partners.
• Report phishing attempts to employees and partners promptly.

11. Pagejacking and Domain Spoofing

Pagejacking involves cloning an existing webpage and redirecting users to the fake version to steal login credentials or payment information. Domain spoofing follows a similar concept - attackers build an identical-looking site under a slightly different URL. Users are typically directed to these fraudulent pages through malicious emails or texts.

Prevention tips:

• Run plagiarism detection tools to identify duplicate versions of your pages online.
• Pay attention to unusual customer service complaints that might signal a spoofed site.
• Submit takedown requests to search engines if you discover a duplicate site, and notify affected customers.

12. Mobile Payment Fraud

As mobile payments become more prevalent, they've also become a target for fraud. Attackers can exploit mobile apps through malware installation, stolen app credentials, or interception of 2FA codes. For example, a scammer may call a customer pretending to represent a business and ask them to read back a verification code - which is actually a 2FA code the attacker has triggered on the victim's account.

Prevention tips:

• Authenticate customers over the phone carefully to reduce the risk of impersonation-based fraud.
• Monitor for unusual spending or refund activity in mobile transactions.
• Educate customers about the risks of clicking on unknown links, QR codes, or visiting unfamiliar websites.

13. Push Payment Fraud

Unlike unauthorized transaction fraud, push payment fraud involves tricking the victim into willingly sending money to a fraudster. This can take many forms, including phishing, blackmail, or deceptive scenarios like fake emergencies. The key distinction is that the victim actively initiates the transfer.

Prevention tips:

• Clearly communicate to customers what your staff can and cannot ask them to do or pay.
• Make it easy for customers to report anyone impersonating your business.
• Issue proactive alerts about ongoing scam attempts tied to your brand.

14. ACH Payment Fraud

ACH (Automated Clearing House) payment fraud involves criminals gaining unauthorized access to a victim's bank account details and using them to initiate fraudulent transfers. For businesses, this risk can come from both outside attackers and malicious insiders.

Prevention tips:

• Strictly limit and monitor employee access to business bank accounts.
• Educate all staff with account access about phishing tactics and establish firm security policies.

Which Businesses Have the Highest Fraud Risk?

Not all businesses face the same level of exposure. Fraud risk is generally highest in sectors that process online payments, handle sensitive personal data, or still accept paper checks.

E-Commerce Businesses

E-Commerce businesses are particularly vulnerable. Online retail involves accepting payments from a wide range of locations, often with multiple payment methods. Features like peer-to-peer payment integrations or international checkout add more potential points of failure. The more accounts and payment methods a customer has linked, the more attractive a target they become for data breaches.

Healthcare, Banking, and Data-Sensitive Industries

These sectors are at elevated risk because of the high value of the information they store. A breach in these sectors doesn't just expose financial data - it can compromise identity information used to commit fraud across many platforms simultaneously.

Businesses Still Accepting Checks

These kinds of businesses face unique challenges. As check usage declines, employees may become less experienced at identifying fakes, which makes training and verification systems all the more important. According to the Association for Financial Professionals, check fraud remains one of the most common forms of payment fraud.

How to Mitigate Risk

A variety of tools and strategies are available to help businesses identify and reduce fraud exposure. Conducting a security risk assessment is a strong starting point, helping teams understand which vulnerabilities are most critical and where to prioritize investment.

From there, organizations should focus on establishing a solid operational and security foundation before layering in more advanced fraud detection capabilities.

Foundational Controls

These measures create a baseline level of protection by securing systems, safeguarding data, and reducing avoidable losses:

• Strong network and password security: Establish internal policies governing account access, password requirements, and physical access to devices and systems.
• Network tokenization: Ensure payment systems encrypt and tokenize customer data to protect sensitive information.
• PCI standards compliance: Build payment workflows that meet Payment Card Industry (PCI) standards to safeguard cardholder data.
• 3D Secure (3DS) authentication: Use the latest 3DS protocols to validate transactions and verify user identity before completing purchases.
• Chargeback protection: Work with your payment processor to implement tools that help minimize financial losses from disputed transactions.

Once these core protections are in place, businesses can enhance their fraud prevention strategies with more dynamic, data-driven approaches.

Advanced Detection & Optimization

These techniques improve visibility, adaptability, and long-term resilience against evolving fraud tactics:

• Fraud KPI tracking: Monitor key metrics such as dispute rates, authorization rates, and approval/decline ratios to identify trends and respond proactively.
• Rules-based systems: Implement rule-based detection as a reliable operational backbone. While rules require ongoing maintenance, they are especially useful in early stages and can be refined over time.
• Machine learning algorithms: Leverage ML-powered systems to analyze large, complex datasets and uncover patterns that are difficult to detect manually. These models continuously improve as they adapt to new fraud behaviors.

Staying Ahead of Payment Fraud

Payment fraud is an ongoing challenge, but a proactive, layered approach can significantly reduce risk. By combining strong foundational controls with data-driven detection and continuous monitoring, businesses can stay ahead of evolving threats.

Ultimately, effective fraud prevention requires regular review, employee awareness, and a commitment to adapting as tactics change.

Additional Resources

• 2026 AFP Payments Fraud and Control Survey Report
• Learn How Federal Reserve Financial Services Can Help Against Check Fraud
• 47 Cybersecurity Statistics and Facts for 2026
• Beyond Phishing: Exploring the Rise of AI-enabled Cybercrime
• Preventing Fraud
• Top Ten Internal Controls to Prevent And Detect Fraud
• The Escalating Threat of Authorised Push Payment Fraud
• Payment Fraud and How To Fight Back
• A Short Guide for Spotting Phishing Attempts
• Eight Ways to Combat Fraud in The AI Age

The internet is basically a giant digital city, and you need to be just as streetwise here as outside your front door. Most people go online every day - scrolling through TikTok, finishing a research paper, or making purchases - but they don't always know the "rules of the road" or the vocabulary that tech experts use to describe our digital lives. Here's a breakdown of essential digital citizenship terms to help you navigate the web and mobile apps like a pro:

Authority - Authority refers to how trustworthy a source is based on who created it. If information comes from a qualified expert or a well-known organization, it's more likely to be reliable than something posted by an unknown user.

Bystander - A bystander is someone who sees harmful behavior online, like cyberbullying, but chooses not to get involved or take action.

Cookies - Cookies are small files that websites store on your device to remember information about you, like login details or browsing habits. They make websites easier to use, but they also allow service providers to track your activity.

Cyberbullying - Cyberbullying is when someone uses digital platforms to repeatedly harass, threaten, or embarrass another person. Unlike trolling, it usually targets a specific individual.

Data Breach - A data breach happens when private or sensitive information is accessed or stolen without permission, often from companies or large platforms.

Digital Citizen - A digital citizen is anyone who uses technology to interact with others online. Being a good digital citizen means using the internet responsibly, respectfully, and safely.

Digital Footprint - A digital footprint is the trail of information you leave behind online through posts, searches, and interactions. The more you share, the greater your exposure to privacy issues or misuse of personal information. Also, once something is online, it can be very difficult to remove.

Digital Identity Theft - Digital identity theft occurs when someone steals your personal information, like passwords or account details, to pretend to be you or access your accounts.

Digital Divide - The digital divide refers to the gap between people who have access to modern technology and the internet and those who do not.

Encryption - Encryption is a method of protecting data by turning it into a coded format that only authorized users can read. It helps keep sensitive information secure.

Firewall - A firewall is a security system that monitors and controls incoming and outgoing network traffic, blocking anything that looks suspicious or harmful.

Imaginary Audience - The imaginary audience is the feeling that people are constantly watching and judging you. Social media can make this feeling stronger by showing likes, views, and comments.

Invisible Audience - The invisible audience refers to the unknown people who may see your online content, including strangers, future employers, or others outside your immediate circle. It pays to assess your security blind spots because you may not realize who is viewing your posts.

Malware - Malware is any type of harmful software designed to damage devices, steal information, or disrupt normal operations. It is often installed as part of a package or application that otherwise appears innocent.

Password Hygiene - Password hygiene refers to the practice of creating strong, unique passwords and keeping them secure instead of reusing the same one across multiple accounts.

Phishing - Phishing is a scam where attackers pretend to be a trusted source to trick you into giving away personal information, often through fake emails, texts, or websites.

Public Wi-Fi Risk - Public Wi-Fi risk refers to the potential dangers of using unsecured networks, where hackers may be able to intercept your data.

Reliability - Reliability refers to whether information is accurate and dependable. Just because something looks professional online doesn't mean it's true.

Social Comparison - Social comparison is the act of comparing your life to what you see online. Since people often share only their best moments, it can create unrealistic expectations.

Targeted Advertising - Targeted advertising uses your online behavior, location, and personal data to show ads that are specifically tailored to you.

Trolling - Trolling is when someone posts deliberately annoying or provocative content online to get attention or start arguments.

Two-Factor Authentication (2FA) - Two-factor authentication is a security feature that requires a second form of verification, like a code sent to your phone, in addition to your password.

Upstander - An upstander is someone who takes action when they see harmful behavior online, such as supporting the victim or reporting the issue.

VPN (Virtual Private Network) - A VPN is a tool that creates a secure, encrypted connection to the internet, helping protect your data and privacy, especially on public networks.

Additional Resources to Learn More

• What is a Digital Citizen?
• The Global Tech Divide: How the Digital Revolution is Leaving Some of Us in the Digital Dark Ages
• What is a Digital Footprint?
• What is Digital Identity Theft?
• Research About The "Imaginary Audience"
• What is Spam, Phishing, and Malware?
• Authority and Value of Information
• Dealing with Social Comparison in the Age of Social Media
• What is Targeted Advertising?
• What Makes a Troll? The Dangers of Online Narcissism

Summary

• Quantum computing is moving from theory toward early practical use, with direct implications for encryption, authentication, and long-term data confidentiality.
• The primary risk is the eventual emergence of cryptographically relevant quantum computers (CRQCs), which would break today’s public-key cryptography and undermine encryption, digital identity, and software trust at scale.
• Quantum risk is already present: “harvest now, decrypt later” activity exposes long-lived sensitive data today, regardless of when CRQCs ultimately arrive.
• Regulatory mandates and procurement standards are accelerating post-quantum cryptography (PQC) adoption, making quantum readiness a multi-year compliance and resilience priority.
• Organizations that delay preparation beyond 2026 are likely to face compressed migration timelines, higher transition costs, and increased operational disruption.

Quantum Computing Explained

Quantum computing applies principles of physics to solve certain complex problems far more efficiently than classical computers. Its security relevance lies primarily in cryptanalysis and optimization: A sufficiently powerful quantum computer will reduce the calculations required to protect today's public-key encryption from thousands of years to hours or less. Researchers have used the term “Q-Day” to refer to the hypothetical point at which quantum computers will be powerful enough to break encryption.

Quantum computing is now moving from theory toward early practical use, bringing “Q-Day” closer to reality. Industry estimates suggest quantum computing alone could generate up to $1.3 trillion in value by 2035. Major cloud providers, including IBM, Google, and Microsoft, are expanding their quantum services, while specialised firms such as Quantinuum and PsiQuantum continue to improve system stability and error correction. While these advances are not yet transformative, they are consistent with the early stages of commercial adoption.

Alongside its potential benefits across finance, pharmaceuticals, defense, and other sectors, quantum computing introduces four key security risks.

Risk 1: Breaking Public-Key Encryption

The most critical risk is the eventual arrival of cryptographically relevant quantum computers (CRQCs), systems capable of breaking widely used public-key algorithms such as RSA, Elliptic Curve Cryptography (ECC), and Diffie-Hellman. These algorithms underpin internet communications (Transport Layer Security [TLS], virtual private networks [VPNs], Secure Shell [SSH]), identity and access management, industrial and internet-of-things (IoT) systems, and the integrity of software supply chains.

If broken, threat actors could decrypt sensitive data, impersonate trusted systems, and undermine digital authentication. This could enable:

• Forged digital signatures
• Compromised code-signing pipelines
• Spoofed websites, identities, and certificates
• Manipulated financial transactions and legal documents

Risk 2: Harvest Now, Decrypt Later (HNDL)

Although cryptographically relevant quantum computers (CRQCs) may still be years away, the risk is already materializing through “harvest now, decrypt later” (HNDL) activity. State-sponsored threat actors are likely collecting and storing encrypted data today with the intent to decrypt it once quantum capabilities mature. A 2021 Booz Allen Hamilton assessment found that Chinese economic espionage operations are likely targeting encrypted data with long-term intelligence value, including biometric identifiers, covert source identities, and weapons designs.

Large-scale routing manipulation offers one method for intercepting such data. Researchers at the US Naval War College and Tel Aviv University documented systematic Border Gateway Protocol (BGP) hijacking by China Telecom between 2016 and 2019, which redirected traffic from US, Canadian, and Scandinavian networks through Chinese infrastructure. These types of operations align with a long-term HNDL collection strategy.

Under the HNDL model, exposure occurs at the moment data is transmitted or stored, not when it is eventually decrypted. The primary risk, therefore, centers on long-lived data: information that must remain confidential for a decade or more, or whose sensitivity does not diminish over time, such as government and national security records, intellectual property and trade secrets, personal identifiers, financial data, biometric templates, healthcare records, and legal archives. For these data classes, compromise may not be immediately visible, but once decrypted, the consequences are irreversible. As a result, organizations holding long-lived sensitive data face near-term strategic risk regardless of when CRQCs become operational.

Large-scale routing manipulation offers one method for intercepting such data. Researchers at the US Naval War College and Tel Aviv University documented systematic Border Gateway Protocol (BGP) hijacking by China Telecom between 2016 and 2019, which redirected traffic from US, Canadian, and Scandinavian networks through Chinese infrastructure. These types of operations align with a long-term HNDL collection strategy.

Under the HNDL model, exposure occurs at the moment data is transmitted or stored, not when it is eventually decrypted. The primary risk, therefore, centers on long-lived data: information that must remain confidential for a decade or more, or whose sensitivity does not diminish over time, such as government and national security records, intellectual property and trade secrets, personal identifiers, financial data, biometric templates, healthcare records, and legal archives. For these data classes, compromise may not be immediately visible, but once decrypted, the consequences are irreversible. As a result, organizations holding long-lived sensitive data face near-term strategic risk regardless of when CRQCs become operational.

Risk 3: Quantum-Accelerated Brute-Force Attacks (Grover’s Algorithm)

Quantum computing does not break modern symmetric encryption outright, but it can accelerate search-intensive tasks through techniques such as Grover’s algorithm. This reduces defender reaction time and increases the effectiveness of weak or legacy cryptographic implementations. In practice, this could enable faster brute-force attempts against outdated encryption, quicker identification of exposed secrets or misconfigurations, and more efficient malware tuning and exploit development.

Recent demonstrations, such as Silicon Quantum Computing’s high-accuracy implementation on a four-qubit processor, remain limited in scale but reflect steady progress toward these capabilities. However, Grover’s algorithm is constrained by high hardware requirements and limited parallelization. As a result, modern symmetric algorithms such as AES-128/192/256 are expected to remain secure for the foreseeable future, while environments with poor cryptographic hygiene will be affected first.

Risk 4: Quantum- and AI-Enhanced Vulnerability Discovery

Quantum capability will not develop in isolation. As quantum systems improve optimization and search performance, and AI automates reconnaissance, exploit development, and lateral movement, adversaries are likely to operate at unprecedented speed and scale. Rather than identifying isolated weaknesses, attackers could rapidly map entire attack surfaces, chain misconfigurations, and deploy optimized malware variants in near real time. Research from 2024 demonstrates that machine-learning classifiers can already recover full cryptographic keys from PQC implementations using only a few hundred power traces, underscoring that even post-quantum algorithms will require hardened deployment.

This convergence of AI and quantum technologies could significantly increase an attacker's operational tempo and amplify the impact of individual security lapses. The risk is compounded by the fact that a rising number of organizations carry substantial security debt, with many reporting slow remediation cycles that leave vulnerabilities exposed for extended periods.

When Will CRQCs Arrive?

There is no definitive timeline for CRQCs. Most projections place their arrival in the mid-to-late 2030s, with credible breakthroughs possible earlier in the decade. These estimates should be treated with caution: forecasting is inherently uncertain because progress in quantum error correction and qubit scaling occurs in uneven advances rather than linear progression.

For security leaders, the precise date of “Q-Day” is less important than the lifecycle of deployed systems. Infrastructure implemented today may remain operational when CRQCs emerge. Current cryptographic decisions are therefore future-binding.

Under the HNDL model, quantum risk is already material for long-lived data. Preparedness, visibility, and cryptographic agility matter more than timeline prediction.

How Should Organizations Prepare?

The transition to post-quantum cryptography (PQC) is no longer a theoretical exercise. It is increasingly driven by regulation, procurement requirements, and emerging industry norms. These developments should be interpreted as operational signals necessitating forward planning.

In the US, the Quantum Computing Cybersecurity Preparedness Act requires federal agencies to inventory quantum-vulnerable cryptography and develop migration plans. NIST’s 2024 PQC standards now set the baseline for federal procurement and are rapidly becoming global reference points. In parallel, Commercial National Security Algorithm (CNSA) 2.0 defines approved algorithms and transition timelines for national security systems, with full migration targeted by 2035. Similar momentum is building in Europe. The EU Cybersecurity Act and national quantum-preparedness strategies are accelerating early adoption, particularly across critical infrastructure sectors such as energy and transportation.

Although many of these mandates formally apply to public-sector systems, their practical impact extends well beyond government. Procurement requirements and supply-chain expectations are translating policy into commercial pressure. As a result, cryptographic inventory, structured migration planning, vendor alignment, and crypto-agility are likely to become baseline governance expectations rather than optional best practices. Boards are beginning to treat quantum risk as a strategic planning issue, not a distant technical concern, with some sectors allocating dedicated quantum-security budgets approaching 5% of total cybersecurity spend to support preparation.

Industry coordination further reinforces this direction of travel. Financial institutions, payment networks, and telecommunications providers are forming quantum-readiness working groups to align migration timelines and manage shared dependencies. SWIFT is developing PQC migration guidance for its global messaging network, and Mastercard has released a PQC migration white paper outlining practical transition steps.

As the HNDL risk window narrows, organizations that begin structured preparation now are likely to manage transition risk deliberately and cost-effectively. Security leaders should ensure they understand where quantum-vulnerable cryptography resides, how regulatory obligations may cascade through customers and partners, and whether critical suppliers have credible PQC transition roadmaps. Those that delay risk compressed timelines, regulatory pressure, and materially higher transition costs later in the decade. Specific technical and governance steps are detailed in the Mitigations section.

Outlook

HNDL activity will continue to expand.
State-sponsored threat actors are highly likely to increase long-term interception and storage of encrypted data, particularly from sectors handling information with long confidentiality lifetimes. Even as storage economics fluctuate, scalable interception infrastructure and economically sustainable long-term storage models enable continued accumulation of high-value encrypted material. Demonstrated routing manipulation capabilities further support persistent collection at scale, ensuring exposure continues to build regardless of when CRQCs ultimately arrive.

Attacker operational tempo will increase.
The convergence of AI-enabled automation with quantum-accelerated search and optimization is likely to compress defender response windows and amplify the impact of existing security debt. Organizations reliant on legacy cryptography and slow remediation cycles will feel this pressure first.

Regulatory and procurement pressure will intensify.
Post-quantum readiness is increasingly likely to become a baseline requirement for regulated markets, government contracts, and high-trust supply chains. US and European initiatives are formalizing transition timelines, and these mandates will propagate through vendor ecosystems, reframing quantum preparedness as a competitive requirement rather than a discretionary control.

Migration risk will become a primary enterprise challenge.
Organizations that delay cryptographic inventories and crypto-agility investments are likely to face compressed transition timelines, higher costs, and greater operational disruption as standards mature and vendor dependencies shift.

Mitigations

Organizations should treat quantum resilience as a phased program aligned to visibility, flexibility, and systemic risk reduction, with leaders actively testing assumptions at each stage.

Short-term (2026): Establish visibility and prioritization

Security teams should maintain a comprehensive cryptographic inventory, identifying quantum-vulnerable algorithms across applications, infrastructure, and third-party dependencies, as well as public key infrastructure (PKI), operational technology, and IoT environments, and mapping them to data sensitivity and confidentiality requirements.

Leaders should be asking:

• Do we have an enterprise-wide inventory of where quantum-vulnerable cryptography is embedded, including in legacy and third-party systems?
• Which data assets must remain confidential for a decade or more, and are they currently protected by algorithms likely to be broken by CRQCs?

Medium-term (2026–2028): Enable flexibility

Organizations should design for cryptographic agility, ensuring that new systems and major upgrades allow algorithm replacement without architectural redesign. Vendors supporting long-lived products should provide credible PQC transition roadmaps aligned to emerging standards.

Leaders should be asking:

• Are we continuing to deploy systems that hard-code cryptographic algorithms, thereby increasing future migration risk?
• Do our critical suppliers have credible, time-bound PQC transition plans, and how exposed would we be if they fell behind?

Long-term (2028-onwards): Reduce systemic exposure

Migration should prioritize long-lived data and high-trust functions, including identity infrastructure, code signing, certificate management, secure build pipelines, and critical third-party software. Strengthening software and supply-chain integrity will be essential to minimizing cascading risk during transition.

CISOs should be asking:

• Which enterprise trust anchors (for example, certificate authorities, signing keys, or hardware security modules) would create systemic impact if rendered vulnerable in a post-quantum scenario?
• Can we rotate and replace cryptographic components at scale without operational disruption if migration timelines compress unexpectedly?

Recorded Future intelligence can support these efforts by tracking emerging cryptographic risks through our Threat Intelligence Module, identifying exposed dependencies through our Attack Surface Intelligence, and assessing third-party quantum readiness as standards and vendor capabilities evolve through our Third-Party Intelligence Module.

Risk Scenario

GridCore Systems is a US-based provider of industrial control systems (ICS) and grid-management software for electric utilities nationwide. The firm relies on quantum-vulnerable public-key cryptography (RSA/ECC) for remote access, software signing, and secure data exchange with utilities and regulators, and has not yet completed a post-quantum cryptographic transition.

First-Order Implications

Second-Order Implications

Third-Order Implications

For security professionals evaluating threat intelligence vendors, the Gartner Magic Quadrant offers an indispensable perspective. Gartner analysts’ thorough and nuanced analysis cuts through the noise, making it easier for teams to understand each platform’s approach, strengths, and considerations—and helping them determine whether a particular vendor fits their organization’s unique needs.

That’s why we’re honored to share that Gartner has named Recorded Future a Leader in the first-ever Magic Quadrant™ for Cyberthreat Intelligence Technologies. This new report evaluated 17 vendors in the space, providing a comprehensive look at the competitive landscape.

“In our view, being recognized as a Leader means something specific to us: we feel it reflects our ability to help our customers with the outcomes they depend on. These include stopping threats pre-attack, running intelligence autonomously at a scale no human team can match, and making every security control they own more effective," said Colin Mahony, CEO, Recorded Future. “We believe this recognition reflects both the trust our customers place in us and the strength of the outcomes we help them achieve.”

A research methodology that prioritizes customer voice

A Gartner Magic Quadrant is a culmination of research in a specific market, giving you a wide-angle view of the relative positions of the market’s competitors. By applying a graphical treatment and a uniform set of evaluation criteria, a Magic Quadrant helps you quickly ascertain how well technology providers are executing their stated visions and how well they are performing against Gartner’s market view.

For Recorded Future, this meant that Gartner analysts spoke directly with our customers about their real-world experiences—the challenges they face, how they use our Platform, and the outcomes they've realized. We feel their voices shaped our position in the Magic Quadrant, just as they’ve always shaped our product offerings and roadmap.

The new Gartner report offers a snapshot of what the analysts heard from customers. We haven’t stopped working since then and there’s much to talk about.

There’s more… the next phase of threat intelligence

In conversations throughout 2025, our customers gave us their thoughts about product complexity, pricing models, and the challenges of scaling intelligence across their teams. As a result of their input, we’ve fundamentally changed how they can access and make the most of Recorded Future threat intelligence.

Here are the highlights of our continued commitment to simplicity and innovation to provide better experiences for our customers in 2026:

1. Goodbye, modules. Hello, simplicity. Meet our four new solutions.
Our four new solution areas cover the four major attack surfaces—an organization’s systems, brand, supply chain, and payment methods:

• Cyber Operations—This foundational solution empowers security teams with the intelligence to monitor and prioritize threats and vulnerabilities, get in-depth malware insights, triage alerts and detect threats, and stand up an intelligence-driven defense.
• Digital Risk Protection—Also foundational, this solution allows teams to monitor malicious sites, code repositories, and the dark web to detect brand abuse, employee credential compromise, and other threats to digital trust.
• Third-Party Risk—This solution enables teams to continuously assess supplier security posture with real-time intelligence, accurate risk ratings, vendor action plans, and more.
• Payment Fraud—With this solution, teams can detect and prevent card-not-present fraud with intelligence that identifies compromised payment data before it's used.

The solutions are built on a unified intelligence foundation to provide consistency, accuracy, and alignment around shared security outcomes. And they integrate with other security solutions like CrowdStrike Falcon and Google SecOps, bringing the benefits of Recorded Future intelligence and rich context directly into common SIEM and EDR workflows.

2. New pricing packages for less friction, more intelligence
We’re offering the four solutions in new pricing packages designed to fit customer needs:

• Simplicity—Customers can purchase one package instead of juggling multiple modules
• End-to-end workflows—Packages cover full use cases, complete with the key capabilities to get the job done
• Wider access—Higher tiers offer unlimited seats, so everyone now can be intelligence-led.

In addition, integrations are included. Now your tools in the security stack—SIEM, SOAR, firewall, endpoint protection, ticketing system, and more—can leverage Recorded Future intelligence without integration fees or limitations.

3. Expansion into Latin America
The threat landscape knows no geographical borders, and neither do we. We’ve expanded Recorded Future’s operations into Latin America, giving security teams in the region better access to the expertise and support they need to mount a successful proactive defense.

4. Autonomous Threat Operations for autonomous defense
In February, we launched Autonomous Threat Operations to help customers move from isolated threat intelligence insights and manual workflows to automated and continuous defensive actions across the entire security ecosystem. Complete with AI-powered, 24/7 autonomous threat hunting and multi-source correlation in the Intelligence Graph®.

As we continue to build on our vision of moving from automated to autonomous operations, we’re developing Recorded Future AI and agentic experiences to help our customers reduce alert fatigue, save time on research, and run threat hunts faster so they can detect and defend at scale.

Explore the Gartner Magic Quadrant report today

We’re proud to be recognized by Gartner as a Leader in Cyberthreat Intelligence Technology, and we’ll continue innovating for our customers to help them mitigate risk and stay ahead of evolving threats.

Get the report to review Gartner analysis and see how Recorded Future fits your CTI program needs.

____________________________________________________________________________________________________________________________________

Gartner, Magic Quadrant for Cyberthreat Intelligence Technologies, By Jonathan Nunez, Carlos De Sola Caraballo, Jaime Anderson, 04 May 2026.

Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates.

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

Behind every ransomware demand, botnet, or threat activity group is a server sitting in a data center. While most legitimate hosting providers evict threat actors once identified, a specific class of providers does the opposite. Recorded Future^® calls these providers threat activity enablers(TAEs).

What Is a Threat Activity Enabler?

A threat activity enabler (TAE) is an individual, organization, or service provider that supports malicious cyber activity by providing infrastructure or services leveraged by threat actors. More commonly, this includes providers that lack a formal physical or virtual storefront, conduct business only via email or messaging platforms, and do not enforce know-your-customer (KYC) policies. It also includes hosting providers that selectively respond to abuse reports or law enforcement inquiries to maintain plausible deniability, as well as more traditional self-proclaimed “bulletproof” providers that openly ignore oversight or advertise non-cooperation.

TAE networks serve as the backbone for ransomware groups, infostealer campaigns, botnets, and even state-sponsored threat actor operations. What distinguishes TAE networks is the sustained concentration of malicious infrastructure within their networks.

How TAEs Operate

TAEs are masters of obfuscation and are highly resilient, hiding behind layers of decoy companies to evade accountability. They use several core tactics:

• Corporate Shell Games: They establish front companies across multiple jurisdictions to create legal distance between the infrastructure and the operators.
• Strategic Resource Control: They often operate as local internet registries (LIRs). This gives them direct control over IP resources and autonomous systems (ASNs), allowing them to manipulate network resources at will.
• Rapid Rebranding: When a network becomes too "hot" due to scrutiny, TAEs rapidly transfer IP address prefixes to a newly registered, clean-looking entity.

Identifying High-Risk TAE Networks

Recorded Future actively identifies high-risk TAE networks through its Network Threat Density List. These networks are ranked by their Threat Density Score, calculated from the concentration of validated malicious activity relative to the total number of IP address prefixes a network announces.

This approach cuts through the noise to quickly expose infrastructure that is disproportionately associated with threat activity, a core characteristic of TAEs, allowing network defenders to prioritize the infrastructure most likely to pose material risk.

From Insight to Action

Tracking TAE networks allows security teams to move from reacting to individual threats to proactively managing infrastructure risk. In practice, this means applying TAE intelligence across three core areas: prevention, detection, and exposure.

Operationalize TAE Intelligence

Figure 3: Three steps for operationalizing TAE intelligence

TAEs are persistent and continuously evolving, adapting quickly in response to sanctions, enforcement actions, and exposure. While their identities may change, their underlying infrastructure patterns often remain consistent.

The "metaspinner" Case Study

In April 2025, a TAE tracked by Recorded Future, Virtualine Technologies, shifted its IPv4 resources to a newly registered network that fraudulently impersonated a legitimate German software firm, metaspinner net GmbH. Because this provider’s historical infrastructure patterns were already being tracked, the newly created network was immediately identified as a front. Within weeks, this network became a primary distribution hub for malware families such as Latrodectus and AsyncRAT. When the operation was eventually exposed, Virtualine Technologies simply pivoted the infrastructure to a new identity within one of its existing autonomous systems to maintain its operations.

This case underscores the reality of TAE networks: while identities, ownership records, and corporate fronts may change, the underlying infrastructure and its associated risk persist, making continuous tracking essential to identifying and prioritizing the networks that will drive future threat activity, as demonstrated by Virtualine subsequently emerging as the highest-risk TAE network in 2025.

The Stark Industries Case Study

In May 2025, the European Union sanctioned UK-registered hosting provider Stark Industries Solutions and its executives for enabling Russian state-sponsored cyber operations. However, enforcement did not halt Stark Industries’ operations. In the weeks leading up to the sanctions announcement, Stark Industries began transferring IP resources, modifying RIPE registrations, and shifting infrastructure to affiliated entities.

Despite the sanctions, the underlying infrastructure, routing relationships, and operational patterns remained traceable across these new fronts. Continuous monitoring of TAE ecosystems enables defenders to detect these pivots in near real time, revealing continuity beneath corporate rebrands and legal restructurings. This case underscores a broader reality: sanctions may change names and ownership records, but without infrastructure-level visibility, the enabling networks behind malicious activity often persist.

What This Means for Security Leaders

TAEs represent an ongoing challenge. While individual campaigns and threat actors may come and go, the infrastructure that supports them remains adaptive and deliberately resilient.

For security leaders, this requires an additional shift from solely reacting to individual indicators to understanding and prioritizing the infrastructure that enables threat activity at scale. By identifying and tracking high-risk networks, organizations can reduce investigative noise, focus resources on the most impactful threats, and take proactive steps to limit exposure before attacks materialize.

Ultimately, addressing TAEs is not just about detection; it’s also about disrupting the conditions that enable modern cyber threats to operate.

Questions You Should Be Asking

• How much of your network communicates with high-risk infrastructure?
• Are you prioritizing alerts involving high-risk networks?
• Is TAE or ASN risk intelligence integrated into your detection and triage workflows to ensure the highest-risk activity is addressed first?
• Do any of your third-party providers rely on TAE-linked infrastructure?
• Do you have hidden exposure to TAE networks?
• Are your controls dynamically adjusting to infrastructure risk?
• Can you proactively restrict or challenge traffic to and from high-risk networks?

Summary

Embodied AI has arrived.. Humanoid and quadruped robots are moving off factory floors and into everyday operations, military deployments, and critical infrastructure. Technological advances in large language models LLMs and robotics are enabling robots to perform complex tasks autonomously.

Security has not kept pace. Researchers have demonstrated that commercially available robots can be hijacked over Bluetooth, covertly exfiltrate audio, video, and spatial data to servers in China, and even infect neighboring robots wirelessly, forming physical botnets. If unaddressed, these security weaknesses are set to scale massively once humanoid robots are fully integrated into critical workflows.

The risks need to be taken extremely seriously. A robot should be treated less like a machine on the balance sheet and more like a cyber-physical endpoint with cameras, microphones, radios, cloud dependencies, and motors. That means tougher procurement, tighter network controls, continuous vulnerability monitoring, and a credible plan for operational continuity if a fleet has to be pulled offline.

Analysis

Market Drivers of Embodied AI Adoption

Embodied AI, intelligent systems in physical forms such as humanoid and quadruped robots, is moving from spectacle to staffing plans.

The shift is being driven as much by demographics as by technological progress. There are growing reports that the working-age population worldwide has begun to decline. China, an economic success story, has seen its population also decline again in 2025 as births hit a record low. These trends do not make large-scale automation inevitable, but they seriously strengthen the economic case for it in both corporate and government decision-making.

The International Federation of Robotics identifies labor shortages, real-world testing of humanoid robots, and increasing attention to safety and cybersecurity as defining trends for 2026. Some early deployments of embodied AI reinforce this trajectory. BMW reports that the Figure 02 humanoid robot has assisted in the production of more than 30,000 X3 vehicles, while GXO and Agility Robotics describe their partnership (established in 2024) as “the first formal commercial deployment of humanoid robots.” In high-risk environments, Sellafield is deploying quadruped robots to reduce human exposure in nuclear decommissioning.

Capital markets are also responding. Unitree filed for a reported $610 million initial public offering (IPO) in Shanghai in March 2026. Taken together, these signals suggest that robots are leaving pilot programs and becoming operational.

That transition makes the security question immediate rather than theoretical.

Expanding Attack Surface in Embodied AI Systems

Unlike traditional IT assets, embodied AI systems combine multiple high-risk components in a single platform: cameras, microphones, sensors, wireless radios, cloud connectivity, and physical actuation. This convergence creates a broad and under-secured attack surface.

A compromised robot can exfiltrate sensitive environmental and operational data, provide persistent remote access to internal networks, and interact physically with its environment, potentially causing unintended physical effects. This elevates robots from conventional endpoints to cyber-physical systems with both digital and real-world consequences.

The risk is compounded by architectural choices. Many platforms rely on cloud-dependent telemetry, wireless provisioning interfaces, and centralized control mechanisms. These design decisions create multiple entry points for attackers and increase the likelihood of compromise across entire fleets of embodied AI systems.

Demonstrated Vulnerabilities and Exploits

The risks are no longer theoretical. Documented vulnerabilities show that commercially available robots can be compromised with relative ease. Unlike traditional cyber threats, which mostly affect the digital world, exploiting robots enables attackers to manipulate the physical world, maximizing the potential for harm.

In 2025, researchers discovered an undocumented backdoor in Unitree’s Go1 quadruped robot that enabled remote access via the CloudSail service. Axios reported that an exposed web application programming interface (API) could allow attackers to locate devices globally and, if a robot was online, view live camera feeds without authentication. Where default credentials remained unchanged, full device control was possible. Whether described as a backdoor or a design failure, the implication is the same: robots may be reachable in ways operators do not anticipate, just like any other Internet of Things (IoT) device.

Further research disclosed a critical vulnerability in the Bluetooth Low Energy and Wi-Fi provisioning interface used by multiple Unitree models, including the Go2, B2, G1, R1, and H1 robots. According to both the UniPwn research and IEEE Spectrum, the flaw combined hard-coded cryptographic keys, trivial authentication bypass, and command injection in the Wi-Fi setup process. An attacker within radio range could obtain root-level access without physical contact, giving them control over the robot.

Because the exploit propagates wirelessly, a single compromised device can enable lateral movement across nearby robots. This creates a fleet-level compromise scenario in which multiple units can be controlled simultaneously. The result resembles a physical botnet capable of both digital and physical actions.

Surveillance risks are equally significant. Researchers wrote that the Unitree G1 robot continuously exfiltrated multimodal sensor and service-state telemetry every 300 seconds without the operator’s knowledge. This included streaming data to external servers, potentially including audio, video, and spatial mapping. A robot operating inside a plant or laboratory may therefore be mapping the environment in real time.

The attack surface extends beyond firmware and networking layers. Researchers showed they could take control of a Unitree humanoid in about a minute, bypass its normal controller, and trigger physical actions. Demonstrations at GEEKCon in Shanghai indicated that both voice commands and short-range wireless exploits could hijack robots and propagate attacks to nearby units, including those not actively in use.

At the software layer, embodied AI systems introduce additional risks due to their reliance on large vision-language models. Researchers demonstrated that physical-world text can influence system behavior, as injected visual prompts were shown to steer autonomous driving, drone landing, and tracking tasks without compromising the underlying software. This would enable threat actors to take control of a self-driving car or turn a drone into their own surveillance feed by embedding a visual prompt in the environment, such as hiding a message on a stop sign.

Systemic and Operational Risk Implications

The implications extend beyond individual devices to organizational and systemic risk. Embodied AI systems are already being deployed in environments where compromise has consequences beyond data loss. Manipulation or malfunction of robots during critical operations would have outsized economic or public safety consequences. Militaries are also experimenting with robotic systems (see Figure 4).

In 2024, the Golden Dragon exercise between Cambodia and China featured robot dogs among the systems on display. Meanwhile, in the US, politicians have begun pushing for Unitree to be designated as a federal supply-chain risk, reflecting national security concerns about commercial robotics platforms. This is a very similar move to Poland’s ban on sensor-rich vehicles accessing military sites to limit surveillance risk. Ukraine has successfully deployed ground-based robots and drones in combat operations, marking a significant shift in modern warfare. In a landmark operation in April 2026, Ukrainian forces captured a Russian position using only unmanned systems — the first recorded instance of a robot-only assault in the conflict.

As adoption scales, these risks become interconnected. A vulnerability affecting one platform or vendor could propagate across fleets, sites, or sectors, creating systemic exposure.

At the same time, the pace of commercial development is outstripping regulatory oversight. Bank of America estimates that as many as three billion humanoid robots could be in operation by 2060. This convergence of demographic pressure, advancing AI capabilities, and falling production costs suggests that large-scale human-machine coexistence is highly probable.

Securing embodied AI systems is therefore not a peripheral technical issue. It is a strategic requirement that must be addressed before widespread deployment locks in insecure architectures at scale.

Summary

The United States (US) is shifting toward a more force-driven security strategy primarily relying on military operations and economic pressure to counter transnational criminal organizations and limit Chinese, Russian, and Iranian influence in the Western Hemisphere.

Regional outcomes diverge across three core scenarios:

• US-aligned authoritarian cooperation with fragile stability
• Political fragmentation enabling criminal expansion and governance breakdown
• A strategic realignment toward BRICS that reduces US influence and increases great power competition

Each scenario increases the risks of political instability, regulatory fragmentation, and cyber threats, including increased surveillance, cybercrime, and targeting of critical infrastructure and multinational businesses.

Analysis

The US 2025 National Security Strategy formalized a shift toward hemispheric priorities and narrower strategic objectives. This shift had been building throughout President Donald Trump’s first term:

• January 2025: An executive order formally designates cartels as foreign terrorist organizations.
• August 2025: The president signed a classified order directing military action against cartels beyond traditional law-enforcement frameworks.
• September 2025: US forces carried out the first strike on alleged drug-trafficking vessels. Since then, more than two dozen kinetic strikes in the Caribbean and Eastern Pacific have resulted in over 100 fatalities.
• December 2025: The US begins seizing oil tankers accused of sanctions evasion.
• January 2026: The US launches a special operation to capture and extract Venezuelan President Nicolás Maduro to face drug trafficking charges in court.
• March 2026: The US launches the “Shield of the Americas” initiative, intended to counter drug trafficking, transnational criminal networks, and illegal migration in the Western Hemisphere. In an address to Congress two weeks later, the commander of US Southern Command reinforced a greater military role in countering foreign terrorist organizations (FTOs) and managing other security priorities in the region.

Taken together, these moves suggest a shift from a law-enforcement-led regional security model toward more overt coercion driven by military intervention.

At a strategic level, US objectives remain centered on limiting transnational criminal activity and countering external competitors. Transnational criminal organizations are framed as a primary threat vector due to their role in narcotics trafficking and financial crime. China’s growing economic presence, anchored in trade and Belt and Road Initiative (BRI) infrastructure, is also seen as a threat to US interests. Russia and Iran maintain more targeted but persistent footholds, particularly through surveillance coordination in Nicaragua, Cuba, and Venezuela. US policy is oriented toward constraining adversary influence while reinforcing its own economic and security partnerships. The US is pursuing these objectives through a combination of expanded military operations, law enforcement activity, and coercive economic measures, including tariffs and sanctions tied to political alignment.

Scenarios

The shift toward prioritizing US influence in the Western Hemisphere over other national security objectives will likely reshape the regional risk landscape. To assess the potential medium-term outcomes, Recorded Future identified key drivers and established baseline assumptions that underpin scenario development.

The following scenarios explore potential outcomes as the US reorients its security strategy toward the Western Hemisphere:

Scenario 1: Initial Authoritarian Stability

In this scenario, the US successfully asserts influence over historically adversarial authoritarian regimes, notably Venezuela and Cuba. These governments pivot toward cooperation with the US on trade, energy, and security, while maintaining repressive political systems domestically. US intervention has already reshaped Venezuela’s leadership and opened pathways for Western energy investment, while Cuba has responded to continued pressure by showing openness to economic reforms. Meanwhile, democracies like Colombia and Ecuador may adopt more coercive internal security postures, particularly in states facing cartel violence, in response to US pressure.

The US takes more aggressive measures to deter and counter non-Western infrastructure investments, leading to a relative diminishment in the influence of China and Russia as US engagement deepens. However, both powers will likely retain significant hemispheric influence and may pursue limited, asymmetric responses rather than direct confrontation.

Scenario 2: Fragmentation and Criminal Expansion

US intervention produces a political backlash, weakening democracies and fueling the collapse of transitional regimes. Inconsistent or heavy-handed military actions against alleged criminals increase public outrage, leading to electoral turnover and instability. As governments escalate repression to maintain control, resistance movements and localized violence intensify, further eroding state authority. This dynamic creates governance vacuums that strengthen TCOs, particularly in border regions. In this environment, cartels and armed groups re-emerge as dominant power brokers, reversing gains in regional security and leading to a resurgence in criminal activity and violence.

Scenario 3: Accelerated Pivot to China

The US’s overreliance on military solutions at the expense of soft power enables China to position itself as an appealing alternative partner by offering positive incentives and stable, long-term policy-making. As a result, LATAM governments across the ideological spectrum quietly accelerate their pivot toward China, building on existing trade and investment ties. As this trend continues, LATAM governments feel emboldened to adopt more overt mechanisms to resist US influence, including legal challenges to military operations and regulations targeting US companies. Both China and Russia are able to increase their economic footprint and political influence in the region, especially if the US becomes less willing to maintain a consistent security presence.

Outlook

The scenarios are not mutually exclusive: multiple outcomes can play out in different countries or regions across Latin America. Below are key indicators to monitor to anticipate which outcome is more likely to emerge:

• Election Outcomes: Colombia, Peru, and Brazil all have elections in the next year; a change in leadership may reflect popular dissatisfaction with the current government’s foreign policy, precipitating a policy shift. Furthermore, a decisive Republican defeat in the US midterms may reduce appetite for foreign intervention, leading to inconsistent policy.
• US Intervention in Cuba: The US government is strongly signaling its intention to replace or significantly reform Cuba’s long-standing Communist regime. The success of the operation and the willingness of the US to back a transitional or reform government will determine which scenario described above plays out.
• LATAM Security Cooperations: Criminal groups and militias thrive in contested or under-governed regions, such as along borders. Look for signed agreements and joint operations as signs of cooperation — or the lack thereof signalling potential breakdown in security coordination and a greater likelihood of criminal expansion.
• The China Alternative: While China is likely to want to avoid direct confrontation over influence in the Western Hemisphere, the CCP may seek to offer more positive incentives to increase its economic footprint in the region, such as continued investments in ports, telecommunications, and other critical infrastructure.
• The War in Iran: Even though it’s happening on the other side of the world, the Iran war is likely to shape how the US pursues military operations in the Western Hemisphere. Battlefield setbacks could decrease appetite for military intervention, or energy security pressures could increase the imperative to ensure influence.

Mitigations

• Strengthen cyber resilience and third-party risk management: Enhance monitoring and defenses for critical infrastructure, telecommunications, and cloud environments. Use Recorded Future’s Geopolitical Intelligence module to understand the surveillance risk in countries where you operate. Conduct regular assessments of vendors and partners to reduce exposure to espionage, surveillance, and cybercrime.
• Prepare for regulatory fragmentation and data localization requirements: Develop flexible compliance frameworks that can adapt to diverging data sovereignty laws, sanctions regimes, and trade restrictions. This includes establishing localized data storage where necessary and maintaining legal contingency plans for rapid policy changes.
• Enhance crisis response and continuity planning: Build scenario-based contingency plans for political instability, violence, or infrastructure disruption (such as internet outages or supply-chain interruptions), which are routinely monitored in the Geopolitical Intelligence module. Contingency planning should include evacuation preparation, alternative logistics routes, and redundant communications systems to ensure operational continuity across volatile environments.

Further Reading

Last week’s reporting on unauthorized access to Claude Mythos reads as an AI security story. It is also, structurally, a North Korea (DPRK) story. Even if the current suspects turn out to be Discord hobbyists.

Mythos was meant to be contained. Within hours of the public Project Glasswing announcement, a third-party contractor environment became the access vector. Not because Anthropic did something wrong. Because controlled release, at the scale modern enterprise software operates, is a goal rather than a guarantee.

The interesting question isn’t who got in this time. It’s who gets in next, and their economics.

What happened?

The group accessed Mythos the same day it was announced, guessing the endpoint based on Anthropic’s naming conventions for prior models. The vector was an individual employed at a third-party contractor, not Anthropic’s core infrastructure. Source characterizations point to a research community “not wreaking havoc” with the model.

The misread

If the coverage only centers on Anthropic’s security posture or the AI safety debate, we’re missing an important angle.

The structural signal is that any preview or controlled-access model release has porous boundaries by design. Access controls on paper (contracts, NDAs, approved vendor lists) differ from those in practice. Every partner brings their own contractors, endpoints, and people with legitimate credentials and uneven security hygiene. That is the real control surface, not the cryptographic perimeter around the model itself. Which makes this a supply chain problem that happens to be about AI, not an AI problem that happens to involve vendors.

The blind spot

AI policy discourse is locked on US versus China, including energy, chip controls, export rules, sovereign AI posture, and who wins the race.

Structurally missing from the larger conversation is the one state actor whose entire foreign currency revenue stream is cyber-enabled theft. DPRK doesn’t need to win any race. They need a 20-30% productivity gain in existing operations.

The pipeline is documented. Insikt Group’s Crypto Country estimated that regime-linked cryptocurrency theft reached roughly $3 billion through 2023. The Multilateral Sanctions Monitoring Team (successor to the UN Panel of Experts after Russia’s 2024 veto) has since done the harder primary work. MSMT’s October 2025 report documents $2.8 billion stolen from cryptocurrency companies between January 2024 and September 2025 across more than 40 heists, with proceeds explicitly tied to WMD and ballistic missile program funding. The State Department updated the tally in January 2026: another $400 million stolen in the three months since publication, bringing the 2025 totals above $2 billion.

Every successful crypto exchange intrusion ends up on a launch pad.

Why North Korea wants the next model

Crypto exchange intrusions are labor-intensive at every phase. Recon, social engineering at scale (fake developer personas on GitHub and LinkedIn, spear-phishing of individual engineers at wallet providers), credential harvesting, post-exploit lateral movement, key extraction, and laundering.

Agentic capability compresses the cycle to include the same operator-hours, more successful intrusions, and more stolen $$$ per operator.

Bybit is an easy example. The FBI attributed approximately $1.5 billion in stolen virtual assets to TraderTraitor in February 2025. The intrusion chain ran months of patient targeting against a single Safe{Wallet} system administrator via phishing, followed by post-compromise operational patience. These types of attacks are expensive, time-intensive, and still extraordinarily productive.

Lazarus and TraderTraitor don’t need AGI. They need the productivity lift that turns a junior operator into a senior one and shaves weeks off the planning phase. It doesn’t have to be Mythos specifically. Any comparable capability through a comparable vector does the job.

Better tools mean more successful intrusions. More successful intrusions mean more stolen crypto. More stolen crypto means more missiles.

Three access patterns

Three different tradecraft patterns keep getting conflated in media coverage. They are not the same TTP, and treating them as one weakens the response on all three.

1. Contractor misuse. A legitimately credentialed employee at a third-party vendor uses their access for unauthorized purposes. This is the Mythos story. The credentials and access are real, though the intent is variable. Defenses (easy to say, hard to do well): telemetry, behavioral monitoring, and least-privilege scoping at the vendor tier.

2. Fraudulent hiring. An adversary places its own operatives inside the target through stolen or synthetic identities, often via remote IT contracting. This is the DPRK IT worker scheme. Insikt’s Inside the Scam documents PurpleBravo’s infrastructure: front companies in China spoofing legitimate IT firms, and a malware ecosystem (BeaverTail, InvisibleFerret, OtterCookie) targeting the cryptocurrency industry. The credentials are real, but the identities are fake. Defenses: identity verification at hire (in-person interviews to avoid AI tricks), ongoing personnel vetting, geographic and behavioral baselining.

3. Supply chain compromise. A trusted vendor’s systems get breached, and the attacker uses that vendor’s legitimate distribution channel to reach the real target. TeamPCP’s March 2026 LiteLLM compromise hit the AI toolchain directly, poisoning Trivy (a defensive security scanner) to reach a package with 95 million monthly downloads. Defenses: build-pipeline integrity, dependency monitoring, signed artifacts.

These three attack vectors converge on the same truth. Any preview or limited-release AI program that depends on third parties is exposed to all three vectors simultaneously. DPRK is the actor most motivated across the full triangle because the revenue case is specific, measurable, and directly beneficial for the regime. They are incentivized to be “AI native.”

So what?

In the security industry, we need to stop thinking about AI access as purely a lab problem when it’s also a sanctions problem. The great-power competition framing obscures the actor already online, with a rich history of monetizing cyber heists to fund missiles.

“Limited release” is a wonderful bumper sticker. The AI reality, from a threat-modeling perspective, is a countdown to turbo-charging adversarial capabilities.

Now what?

The honest conversation is that perimeter-style AI “controlled access” is less effective against State-sponsored adversaries. A productive security path is a distinct preview infrastructure, aggressive telemetry, canaries, and third-party access tied to personnel-level vetting rather than contractual attestation. (Guessable endpoints should be the first thing dead.)

Crypto exchanges and custodians: your threat model needs to anticipate what Lazarus can do 3 to 6 months from now, not what they did last quarter. Assume they improve faster than your defenses do.

Policymakers: DPRK is a first-class entity in AI access governance. The Multilateral Sanctions Monitoring Team framework already documents cyber-enabled sanctions evasion thoroughly. What it doesn’t yet do is name AI capability access as a sanctions-relevant category. Dual-use export controls have governed the transfer of semiconductor and missile technology for decades. AI capability is the obvious next category.

Corporate CISOs (outside the AI-lab orbit): your third-party contractor environments are now inside the AI capability threat surface, whether you opted in or not. Inventory accordingly.

Close

Mythos is a preview of an access pattern. Any actor whose business model is stealing money to build weapons will find the third-party seam. This time, it was hobbyists. DPRK has spent two decades proving why nonproliferation is the right frame here.

Key Takeaways

• The real challenge in cybersecurity isn’t intelligence or visibility, it’s speed. Attackers operate at machine speed, while most organizations are still constrained by manual, human-driven workflows.
• Traditional threat intelligence falls short because it stops at insight. To reduce risk effectively, intelligence must not only inform decisions but also actively drive response.
• Fragmentation across cyber, fraud, and third-party risk creates exploitable gaps. A unified, intelligence-driven approach is essential to understanding and addressing modern threats holistically.
• Autonomous defense is the path forward. By enabling continuous, real-time action across the attack surface, organizations can close the speed gap and move from reactive security to proactive risk reduction.

For most security teams today, volume and access to intelligence isn’t the problem. It’s the speed at which they can turn that intelligence into action.

Over the last decade, organizations have invested heavily in threat intelligence and cybersecurity. Global security spending has surged past $200 billion annually, growing double digits year over year, while security’s share of IT budgets has climbed from under 9% to more than 13%. Most CISOs report continued budget increases, and enterprises are making billion-dollar investments in intelligence capabilities.

And yet, breaches still happen. Fraud still slips through. Third-party risk still catches teams off guard. The issue isn’t visibility. It’s the growing gap between how fast threats move and how fast organizations can respond.

Attackers now operate at machine speed, leveraging automation and AI to identify vulnerabilities, launch campaigns, and exploit opportunities in real time. Most security teams, however, are still constrained by manual workflows, fragmented systems, and processes that require human intervention at every step. That mismatch is where risk can accumulate—and where even well-resourced teams fall behind.

The Hidden Cost of Human-Speed Security

For many organizations, this gap shows up in subtle but compounding ways. Analysts spend hours triaging alerts, trying to determine which signals actually matter. Security teams often discover incidents after damage has already occurred, not because the data wasn’t there, but because it couldn’t be acted on quickly enough. Across the organization, teams responsible for cyber operations, fraud, and third-party risk operate in silos, each with their own tools and workflows, rarely sharing a unified view of risk.

At the same time, expectations from leadership have shifted. Executives and boards no longer want activity metrics—they want clear evidence that security investments are reducing business risk. But when intelligence is not clearly connected to action from security teams, that proof becomes difficult to deliver.

Traditional threat intelligence was designed to inform decisions made by humans, at human speed. In today’s environment, that model introduces delay. And delay, in cybersecurity, is increasingly indistinguishable from exposure.

Intelligence That Acts, Not Just Informs

Closing the speed gap requires more than incremental improvements. It requires a shift in how organizations think about intelligence altogether. Moving forward, the future of cybersecurity must be more than just intelligence-led—it must be intelligence-acted.

In this model, intelligence doesn’t sit in dashboards waiting for analysts to interpret it. It continuously correlates signals, prioritizes what matters, and drives action across the security environment automatically. Instead of asking teams to move faster, it enables the entire system to operate at the speed of the threat.

This is the foundation of autonomous defense, and it’s the future of effective, machine-speed cybersecurity.

From Reactive to Autonomous: A New Operating Model

Autonomous defense fundamentally changes the role of the security team. Rather than serving as the bottleneck between detection and response, analysts become decision-makers operating on top of continuously running intelligence.

Recorded Future’s Autonomous Threat Operations brings this model to life by eliminating the manual steps that slow teams down. It ingests and correlates intelligence from multiple sources, applies context in real time, and triggers actions across existing security tools—all without requiring constant human input.

The impact of such a dramatic shift is immediate and measurable. Threat hunting becomes continuous instead of periodic. Alerts arrive enriched with context, reducing the time needed to investigate and respond. Detection and remediation workflows execute automatically, freeing analysts to focus on strategic threats rather than routine triage.

Just as importantly, this approach transforms how organizations measure success. Instead of tracking activity—alerts processed, queries written, incidents reviewed—teams can demonstrate real outcomes: faster response times, reduced exposure, and a clearer connection between intelligence and risk reduction; the latter of which is becoming increasingly necessary for organizational buy-in.

This is so much more than just adding another tool to the stack. Instead, it’s about making every existing control smarter, faster, and more effective. And it’s paying off. On average, security teams using Recorded Future save up to 100 hours per week through improved analyst productivity, allowing teams to redirect effort toward threat hunting and proactive defense instead of repetitive manual analysis.

The Bigger Challenge: Fragmented Visibility Across the Attack Surface

Speed alone, however, is only part of the equation. Many organizations are also limited by how they view risk. Threats today don’t respect organizational boundaries. A phishing campaign can lead to credential theft, which can then be used to access systems, exploit third-party relationships, or enable fraudulent transactions. These events are connected, but still far too many organizations manage them in isolation.

Cyber operations teams focus on internal threats. Fraud teams monitor transactions. Risk teams assess vendors. Each group has visibility into part of the problem, but no one has a complete picture. This fragmentation creates blind spots, and attackers are increasingly skilled at navigating between them.

A Unified Approach to Risk

To effectively reduce risk, organizations need more than faster response times. They need a connected understanding of their entire attack surface, along with the ability to act across it in a coordinated way.

Recorded Future addresses this through four core solution areas—Cyber Operations, Digital Risk Protection, Third-Party Risk, and Payment Fraud Intelligence—all built on a single, integrated intelligence foundation.

In cyber operations, this means moving beyond alert overload to real-time prioritization. Instead of forcing analysts to sift through volumes of data, intelligence surfaces the threats that are most relevant to the organization’s environment and enables immediate action. The combination of prioritization and automation allows teams to reduce noise while improving both detection speed and response quality.

In digital risk protection, the focus shifts beyond the traditional perimeter. Today’s attackers target brands, customers, and executives just as frequently as they target infrastructure. By monitoring the open, deep, and dark web, Recorded Future provides visibility into impersonation campaigns, credential exposure, and emerging threats long before they impact the organization. More importantly, it enables rapid response, whether that means taking down fraudulent domains or preventing account takeover attempts.

Third-party risk represents another growing challenge. As organizations expand their ecosystems, they inherit risk from vendors and partners, often without real-time visibility. Third-party involvement in breaches has reached a staggering 30%, up from just 15% a year ago. Static assessments and periodic reviews can’t keep pace with how quickly vendor risk evolves today. Continuous monitoring, grounded in real-world intelligence, allows organizations to detect issues earlier, respond faster, and maintain a more accurate understanding of their exposure.

In the realm of payment fraud intelligence, the shift is equally significant. There were some 269 million records posted across dark and clear web platforms in 2024, and a tripling of certain e-skimmer infections. It’s important to keep in mind that fraud doesn’t begin at the moment of transaction. Rather, it begins much earlier, in the environments where stolen data is exchanged and tested. Recorded Future provides comprehensive coverage across the complete payment fraud lifecycle. Sophisticated cleanup and normalization techniques result in better data quality and richer data sets, reducing manual research and enabling high confidence mitigation actions. By identifying these signals upstream and intervening, organizations can stop fraud before it’s executed, reducing both financial loss and customer impact.

One Intelligence Foundation. Total Visibility.

What makes this approach fundamentally different is that these capabilities are not delivered as isolated solutions. They are unified through the Recorded Future Intelligence Platform, which correlates data across millions of sources and billions of entities to provide a single, coherent view of risk.

This unified foundation enables organizations to connect signals that would otherwise remain siloed. Threat actors, infrastructure, vulnerabilities, and campaigns are all linked, allowing teams to understand not just what is happening, but what is likely to happen next.

That level of visibility is what makes autonomous defense possible. And not just within a single domain, but across the entire attack surface.

The urgency behind this shift cannot be overstated. Attackers are already operating at machine speed, using automation to scale their efforts and reduce the time between discovery and exploitation. At the same time, organizations that rely on manual processes are finding it increasingly difficult to keep up.

The consequences of this gap are significant. Longer dwell times allow attackers to entrench themselves more deeply. Delayed responses increase the cost and impact of incidents. And as breaches and fraud events become more visible, customer trust becomes harder to maintain.

This is no longer a question of optimization. It’s a question of whether existing operating models can keep pace with the reality of modern threats.

Rethinking What Threat Intelligence Should Do

As organizations evaluate their approach to cybersecurity, the role of threat intelligence needs to be reconsidered. It is no longer enough for intelligence to provide visibility. It must enable action. It must operate in real time. And it must extend across the full scope of organizational risk—not just one domain at a time.

Equally important, it must deliver outcomes that matter to the business. Faster detection, reduced exposure, and measurable risk reduction are no longer aspirational. They are essential for enterprise security in the modern, AI-powered threat landscape.

The goal for most organizations isn’t to replace their security stack. It’s to make it work better. By enabling intelligence to act autonomously, connecting visibility across domains, and aligning security operations with the speed of modern threats, organizations can close the gap that has long existed between insight and action. Recorded Future is built to make that possible.

If your team is still struggling with alert fatigue, delayed responses, or fragmented visibility, the issue may not be a lack of resources. It may be a limitation in how intelligence is being applied.

Now is the time to rethink that model.

Connect with Recorded Future to see how autonomous defense can help your organization move at the speed of today’s threats—and stay ahead of what comes next.

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Summary

Critical elements and rare earth elements REEs are no longer commodities; they are strategic dependencies. Chinaʼs dominance in processing and refining provides it with enormous geopolitical leverage over other industrialized economies.

Geopolitical competition over mining and refining critical elements and REEs is accelerating. Competition to mine them will almost certainly expand into the Arctic, Greenland, Antarctica, the seabed, and space. These emerging arenas introduce legal ambiguity, environmental tension, and strategic rivalry, creating new geopolitical flashpoints.

Cyber operations are increasingly intertwined with resource competition. Insikt Group has identified state-sponsored and criminally aligned cyber threat actors targeting mining organizations to gain a strategic advantage. As critical mineral supply chains grow in importance, cyber activity targeting the sector is expected to increase, with criminal groups potentially serving as proxies or access brokers for state-backed operations.

Analysis

What Are Rare Earth Elements and Critical Elements?

Rare earth elements (REEs) are a group of seventeen metals that are essential to modern technologies. REEs are vital to the Fourth Industrial Revolution, a term for the current era of connectivity, advanced analytics, automation, and advanced manufacturing technology. REEs are used in small but essential quantities; they significantly impact the efficiency, precision, and reliability of equipment. They also differ from most other critical elements because they are difficult to process and refine. The refining process requires complex separation, making supply chains slow to build and capital-intensive.

Critical elements such as lithium, copper, nickel, cobalt, and graphite are primarily used as structural, conductive, or energy-storage materials and are consumed in much larger quantities. These elements form the physical backbone of products like batteries, wiring, and digital infrastructure. In simple terms, critical elements build the systems, and REEs enable the systems to perform at high levels.

Where Are REEs and Critical Elements Located?

On land, critical elements are unevenly distributed globally, with mining concentrated in a few countries. REEs are primarily mined in China, with significant deposits in Australia and the United States (US).

The seabed is an emerging arena for mining due to vast critical mineral reserves that are believed to lie on the ocean floor. On the seabed, minerals are packed into potato-sized nodules, form hard crusts, accumulate in sediment layers, and are emitted from hydrothermal vents. In April 2025, the Trump administration issued an executive order directing the US to rapidly scale its capability to mine and process seabed critical elements. Meanwhile, China continues to expand its deep-sea mining capabilities. Japan is also accelerating its deep-sea mining program and, in February 2026, recovered REEs from 6,000 meters below the surface of the Pacific Ocean.

Arctic ice volume has declined by more than 70% since the 1980s, opening new shipping routes and exposing vast natural resources. As ice retreats, significant deposits of critical elements such as cobalt, tin, and REEs are becoming accessible, alongside oil and gas reserves. Mineral-rich seabed nodules are also being uncovered, attracting increasing interest from both nation-states and private investors.

Greenland contains 25 of the European Commission’s 34 designated critical raw materials as well as substantial oil and gas potential. Mining remains difficult due to harsh conditions and limited infrastructure, but continued ice retreat combined with sufficient capital investment could unlock resources of major economic and geopolitical importance.

Antarctica is currently off-limits to mining until at least 2048 under a 1991 environmental agreement that designated the continent as a natural reserve. Antarctica is believed to hold significant reserves of oil, coal, and iron ore, which are already attracting growing interest for the future. China and Russia have announced plans to expand their presence in Antarctica. China’s intentions appear to be focused on resource exploitation, which could open up a new geopolitical fault line, this time in the South Pole.

Space is quickly becoming the next frontier for critical resource extraction. Critical elements are abundant on asteroids and on the Moon. As companies move toward space mining, the US and China are simultaneously racing to establish a permanent presence in space by the 2030s, intensifying an already highly competitive astropolitical environment.

What Is the Geopolitical Importance of REEs and Critical Elements?

Because industrialized nations need critical elements and REEs to manufacture advanced technologies, global demand is rapidly accelerating. China’s control over critical elements and REEs stems primarily from its dominance of processing and refining rather than extraction. By controlling much of the world’s REE separation and refining capacity, China holds significant leverage over global supply chains and strategic technologies.

This reliance has heightened anxiety in the US over access to critical and rare earth elements. In 2025, China demonstrated its leverage by threatening to suspend REE exports to the US, which compelled Washington to back away from plans to restrict the transfer of critical semiconductor technology.

The US government has since accelerated international critical minerals deals and begun investing in US mining operations to minimize its reliance on China, where over 90% of the world’s REEs are processed. Furthermore, we are now seeing the US strategically stockpiling critical minerals and seeking to form “critical minerals trade blocs.”

Have Any Cyberattacks Been Linked to REEs and Critical Elements?

State-sponsored cyber capabilities are deployed to support national objectives linked to mining operations and the exploration of new critical minerals.

In 2021, Insikt Group identified infrastructure previously linked to APT15, a Chinese state-sponsored threat actor targeting a Canada-based mining company focused on mining zinc, copper, and lead. While there is no public record of Chinese investment in that specific mining company, Chinese firms invested approximately CAD 40 million (USD $30 million) in other Canadian lithium miners during the same period. Ottawa later forced those companies to divest on national security grounds.

In 2025, Insikt Group identified several Chinese state-sponsored threat actors targeting an organization focused on monitoring and regulating seabed mining. These cyberattacks occurred around the same time that China entered into seabed exploration and mining partnerships with nations such as the Cook Islands, Kiribati, and Tonga. This campaign was almost certainly driven by a desire to gain advanced insight into deep-sea mining rules and rival nations' positions, helping it protect its critical minerals dominance and secure strategic seabed access ahead of its competitors.

Between January 2021 and January 2026, Insikt Group identified multiple sophisticated cyber operations targeting Indonesia. While not every intrusion can be conclusively attributed to mining activity, these attacks align with China’s strategic interest in Indonesia’s natural resources; for example, Chinese companies control about 75% of Indonesia’s nickel refining capacity. Furthermore, Indonesia holds approximately 55 million metric tons of nickel reserves, which is over 40% of global reserves.

In 2025, a hacker group known as Silent Lynx (or YoroTrooper) was reported to be targeting Russia's mining sector. Security researchers assessed that Silent Lynx is likely Kazakhstan-based, due to its language fluency, use of local currency, and regional targeting.

Ransomware and criminal cyber groups frequently target the mining sector, primarily for financial gain. As the sector’s global economic importance grows, it may attract increased extortion efforts. Insikt Group has previously identified ransomware groups operating in close coordination with state actors, effectively using ransomware as a smokescreen; as a result, we cannot rule out criminal groups increasingly providing access to mining organizations for state-sponsored cyber operations.

In 2024, Northern Minerals, an Australian rare earths producer, was compromised by the ransomware group BianLian. They published stolen data on the dark web shortly after Northern Minerals ordered Chinese-linked investors to divest their 10.4% stake. BianLian is a financially motivated group that opportunistically targets multiple sectors and is believed to be operated by Russia-based threat actors. While this leak was likely financially driven, state collusion cannot be ruled out, as state-sponsored threat actors increasingly hide operations behind criminal activity.

Outlook

The US and its allies will almost certainly intensify efforts to reduce strategic dependence on China for critical minerals. This is because control of mineral supply chains will be a decisive factor in determining leadership in the Fourth Industrial Revolution.

Mining activity will almost certainly expand into new frontiers, including the deep sea, the Arctic, and Antarctica, permanently reshaping both economic competition and geopolitical risk.

Space will very likely emerge as the final frontier for resource extraction. The US and China will accelerate competition to secure access to lunar and asteroid-based minerals, extending terrestrial resource rivalries beyond Earth’s orbit.

State-sponsored cyber threat actors operating on behalf of industrialized nations will almost certainly increase their focus on targeting mining companies and governments operating in strategically significant mining regions.

Criminal cyber activity will very likely increasingly serve as a smokescreen or initial access vector for state-sponsored operations targeting critical mineral mining companies.

Recommended D3FEND Actions

Further Reading

Mitigations

Know your exposure to changes in critical mineral supplies: Map the locations of critical minerals in your products and suppliers, and identify potential single points of failure.
Resilience question: Are there any single points of failure in critical products or business lines if China were to restrict the supply of REEs?

Build a fallback plan: Put backup suppliers, alternate materials, and realistic inventory buffers in place for the highest-risk supplies your organization relies on.
Resilience question: What is our Plan B for our top three critical electronic supplies, such as laptops?

Prepare for criminal and state-sponsored cyberattacks: If you operate in or supply the mining and critical minerals sector, treat criminal intrusions as potentially more than financially motivated. In some cases, they may serve as cover for espionage. Actively monitor the latest indicators of compromise (IoCs) and the tactics, techniques, and procedures (TTPs) associated with threat actors known to target the sector or government bodies responsible for nation-state mining interests. Use Recorded Future’s Threat Intelligence Module to monitor for dark web and closed-source mentions tied to mining targeting.
Resilience question: If we’re hit with ransomware, how quickly can we restore operations? Do we have backup systems and data?

Map out your supply-chain risks: If your organization operates in or near the mining industry, you might have robust security measures — but your suppliers might not. Use Recorded Future’s Third-Party Intelligence Module to identify risks in your supply chain.
Resilience question: Which supplier or contractor would cause us the most problems if they were hacked, and could they be easily hacked from what we can identify?

Monitor the new mining hotspots: Track developments in the Arctic, Greenland, Antarctica, deep-sea mining, and space, as rules and conflicts there can quickly affect supply and reputation. Use Recorded Future’s Geopolitical Intelligence Module to gain visibility into new mining contracts and potential geopolitical risks from new deals.
Resilience question: What early warning signs are we monitoring that could disrupt our supply chain in the next 6–12 months?

The paradoxes of today’s digital world are well-known to anyone with a smartphone.

Over the last decade, connectivity has expanded, yet the world has become more fragmented. Our everyday lives are more digital, but we spend more time parsing text messages for scams or deliberating the authenticity of potential deepfakes. Technology is delivering great productivity gains to small businesses while making them a larger target for cybercriminals.

In this environment, exposure becomes the default: Access points are growing, control is hard and reacting to change stops working. AI intensifies these dynamics because it compresses time for everyone, including adversaries.

Today, trust has become the most critical tool to move all businesses forward. Without trust, even the best ideas stall. People hesitate, adoption slows and growth stagnates.

Trust used to be something businesses tried to repair after a breach. Now it must be the starting point, and something to nurture and continuously prove in a world that has fundamentally changed.

It would be impossible to eliminate the risk entirely. Some estimates project cybercrime could cost the world $15.6 trillion annually before 2030, surpassing all but two of the world’s largest economies. Instead, the goal must be to build the ability to see sooner, decide faster and limit impact when, not if, something breaks. Trust today is all about bringing together speed, intelligence and collaboration, and that’s exactly what we’re developing across our teams.

Getting this right isn’t just good business sense, but the only way to ensure new technologies are embraced and economies can keep growing.

The advantage is intelligence

Real advantage comes from understanding context and connecting signals across systems. That’s what turns data into better decisions. This kind of intelligence increases speed, reduces risk and enables proactive action. With the right intelligence, teams can hunt for threats continuously, test assumptions and act before harm occurs, not just triage alerts after the fact.

You can see this shift in how the payments industry is evolving, including the work we’re doing by bringing Recorded Future’s threat intelligence together with Mastercard’s security capabilities, payments infrastructure and partnership models. We’re helping organizations understand where risk concentrates, how it propagates, and how quick, collective action can reduce the cost of cybercrime.

Faster insights mean earlier action, which minimizes impact — and deepens trust.

Trust is built through collaboration

Security doesn’t scale through isolated heroics. It scales through ecosystems: shared signals, shared standards and partners who can move together as new threats arise, attack vectors shift and failures spread.

Resilience is strongest when public and private sectors plan, exercise and respond together, rather than in parallel. Different players have different sightlines in the digital ecosystem. Startups look at the edges of innovation. Enterprises understand the realities of operating in today’s environment. Governments see where systemic risk concentrates. When those visions combine, our shields strengthen and expand, pushing cybercriminals out of the frame.

During our time here in Miami for the eMerge Americas conference, we’ve had the opportunity to speak to enterprises, startups, investors and government leaders about the need to accelerate resilience in Latin America, where the digital economy is booming but security hasn’t always kept pace. The region has the world’s fastest-growing rate of disclosed cyber incidents — in 2025 alone, Recorded Future tracked 452 ransomware incidents — but only seven countries have developed cybersecurity plans protecting critical infrastructure, and only 20 have formal computer security incident response teams.

That gap is where trust breaks, and where more collaboration can become a growth necessity. We can’t build sustainable economic growth in Latin America without building digital trust and cyber resilience. That’s why we are deepening our footprint here, enhancing regional threat intelligence and resilience and paving the way for stronger public-private collaboration to address these complex risks.

Secure digital access unlocks economic opportunity — and insecurity shuts it down fast. For a first-time digital user, one fraud incident can be enough to opt out for good. For a small business, one account takeover can wipe out months of progress. That’s why trust is inextricably linked to financial health. People can’t build stability on top of systems they’re afraid to use. At Mastercard, we’ve committed to connecting and protecting 500 million people and small businesses by 2030, because secure participation is foundational, not optional.

The bar for digital innovation today is not what we can deliver, but what people will trust enough to use, depend upon and harness for their own financial health. Because in the end, trust is the superpower.

Executive Summary

Chinese-language, Telegram-based “guarantee” marketplaces are increasingly popular among Chinese-speaking criminal groups despite the widely publicized shutdown of Huione Guarantee in 2025. Although these guarantee marketplaces operate similarly to Huione Guarantee, they differ in their focus on particular aspects of cybercrime and in their targeting of specific geographies. To better understand these Chinese-language guarantee marketplaces, Insikt Group observed and analyzed another increasingly popular guarantee marketplace, dubbed Dabai Guarantee (“大白担保”).

Given that guarantee marketplaces typically involve hundreds to thousands of public and private channels, this report outlines how Insikt Group analysts navigated through just one of the Telegram channels belonging to Dabai Guarantee’s large infrastructure. The channel is known as Dabai Guarantee Public Group 301 (@DBTM301), and its main objective is to conduct “sweeping” operations (using illicit techniques to make purchases of physical goods at retailers or to withdraw and transact at country-specific ATMs) in South Korea and Japan. This report also includes the visible organizational structure of Dabai Guarantee Public Group 301, key rules, staff, and customer service functions.

This report primarily serves as an introduction to understanding how Chinese-language, Telegram-based guarantee marketplaces work and how to navigate them. It also includes interpretations of multiple criminal terminologies used by Chinese-speaking criminals, which are pivotal to understanding how Chinese cybercrime evolves over time. The cyber and fraud campaigns being promoted and launched on Dabai Guarantee and other similar guarantee marketplaces can negatively impact retail, banking, contactless payment providers, insurance companies, and individuals vulnerable to scam-related campaigns.

Key Findings

• Dabai Guarantee is a platform that enables multiple Chinese-speaking threat groups with strong presences across multiple countries to coordinate and launch global-scale fraud and cyber campaigns.
• Chinese-speaking syndicates are using Dabai Guarantee as a platform to facilitate campaigns involving financial and retail fraud, such as ATM withdrawal and ghost-tapping.
• Criminal groups participating in campaigns are often siloed, acting independently, and restricting the sharing of information, resources, and goals, thereby creating barriers to tracking their activities.
• Unlike conventional ghost-tapping campaigns that mainly target luxury businesses, “sweeping teams” typically purchase goods that are less expensive but still considered valuable to criminal groups and are relatively easy to transport (such as women’s cosmetics and tobacco products), likely to avoid detection by law enforcement. The sweeping teams eventually resell them in other markets for cash.
• Dabai Guarantee’s bot search function makes it easy for Chinese-speaking criminals to enter specific search terms and be matched with existing public groups running those campaigns.

Background

Chinese-language guarantee marketplaces first emerged around 2021 with the launch of Huione Guarantee, serving as reliable alternatives to traditional dark web marketplaces accessible via the Tor network. Owners of traditional dark web marketplaces, such as Exchange Market and Chang’An Sleepless Night, have close to full control over advertisements and transactions. These guarantee marketplaces seek to eliminate distrust stemming from criminal groups scamming one another, dark web marketplaces shutting down, potential exit scams, and parties failing to honor terms that were previously agreed upon. Furthermore, guarantee marketplaces operate on publicly accessible Telegram channels by design; these public channels are meant to be found and appeal to a wider Chinese-speaking audience that uses Telegram, noting that most Chinese criminals still use Telegram rather than Tor for communication.

Guarantee marketplaces are often different from typical peer-to-peer (P2P) transactions between threat actors. Guarantee marketplaces are one-stop shops that handle and facilitate all cryptocurrency transactions (typically Tether/USDT) and mediation services between parties, whereas P2P transactions typically take place directly between users or through a third-party escrow service. The preferred cryptocurrency of Chinese-speaking threat actors is USDT, a stablecoin pegged to the US dollar that maintains anonymity. Stablecoins are a type of cryptocurrency designed to maintain a stable value by pegging themselves to reserve assets, most commonly the US dollar, to mitigate the volatility of cryptocurrencies like Bitcoin. According to Chainalysis’s 2026 Crypto Crime Report, stablecoins have come to dominate the landscape of illicit transactions, accounting for 84% of all illicit transaction volume in 2025. Chinese cybercriminals prefer using stablecoins such as USDT due to their combination of price stability, ease of border transfer, and relative anonymity. USDT also helps Chinese cybercriminals bypass China’s strict capital controls and traditional banking scrutiny to move money across borders.

In January 2025, Insikt Group published a report on the Chinese-language guarantee marketplace Huione Guarantee, “Huione Guarantee Serves as a One-Stop Shop for Chinese-Speaking Cybercriminals.” The report described the activities facilitated by Huione Guarantee, which include investment fraud, money laundering, and various online scams. Despite Huione Guarantee’s shutdown on May 13, 2025, Insikt Group observed that other guarantee marketplaces, such as Tudou and Xinbi, stepped in to fill the void left by Huione Guarantee's closure. According to Elliptic, Tudou Guarantee also shut down its operations in January 2026, after processing $12 billion in transactions. Even though Xinbi Guarantee was previously reported to have shut down, it has since been rebuilt and maintains a presence on Telegram as of this writing. Other, but not widely reported, active Chinese-language guarantee marketplaces operating on Telegram (besides Dabai Guarantee) are Yinuo, BoChuang, and Ouyi.

Guarantee marketplaces can also facilitate new attack vectors such as ghost-tapping. In July 2025, Insikt Group published a report titled “Ghost-Tapping and the Chinese Cybercriminal Retail Fraud Ecosystem,” which details how Chinese-speaking cybercriminals and syndicates work together to conduct retail fraud using near-field communications (NFC) relay tactics. As of February 2026, Insikt Group observed that Dabai Guarantee has emerged as a major player in Chinese-language cybercrime, with its Telegram-based infrastructure resembling that of Huione Guarantee and offering malicious services similar to those advertised on Huione Guarantee, which is now defunct.

Dabai Guarantee Overview

Dabai Guarantee is a Telegram-based marketplace, consisting of thousands of public and private Chinese-language Telegram groups, that operates in a manner similar to Huione, Tudou, and Xinbi guarantees; many of these services cater to “small to medium-sized clients.” However, the operators of Dabai Guarantee do not maintain a clearnet website; they operate solely on Telegram, likely due to operational security (OPSEC) concerns. Operators of Dabai Guarantee likely chose not to have a clearnet website in light of Huione’s “bad OPSEC” practices — Huione Guarantee’s clearnet website made tracking much easier for law enforcement officials and researchers, which likely contributed to FinCEN sanctioning the organization in May 2025. The Dabai platform is populated with third-party vendors providing various services that facilitate cybercriminal and fraud activities, including money laundering methods and services, compromised social media and e-commerce accounts, SIM cards, personally identifiable information (PII), malware-as-a-service (MaaS), deepfake technology, know-your-customer (KYC) bypass services, and more.

Dabai Guarantee was likely founded in December 2024, based on its Telegram Channel’s creation date. There are currently six known official main Telegram channels:

• “公群导航 @dabai” (@dabai_a): “Public Group for Navigation Purpose”, 15,372 subscribers, as of this writing
• “大白担保大群” (@dabai_c): “Dabai Guarantee Big Group”, 19,225 members, as of this writing
• “大白供需频道” (@dabaiyajing): “Dabai Supply and Demand Channel”, 17,085 subscribers, as of this writing
• “大白担保规则” (@dabai_e): “Dabai Guarantee rules”, 428 subscribers, as of this writing
• “大白担保客服人员名单” (@dabai_f): “Dabai customer service list”, 527 subscribers, as of this writing
• “大白担保 @dabai” (@dabai): “Dabai Guarantee bot channel”

Dabai Guarantee’s public navigation channel, 公群导航 @dabai, is used to direct threat actors to different private/public Telegram channels to coordinate and collaborate on campaigns targeting both Chinese-speaking and non-Chinese-speaking victims. Below is a list of the service categories offered on the public Telegram groups on Dabai Guarantee. Each category has subcategories for more specific services. Each public Telegram group has a unique group number, the amount of the deposit made to Dabai Guarantee in USDT, the handles of group administrators and customer service representatives, the transaction rules, and a dedicated cryptocurrency wallet. More information can be found in Figure 1. These specialized channels include the following:

• “海外钓鱼类” (“Overseas Phishing”) — Coordinate phishing campaigns against individuals residing outside of China
• “买卖类” (“Trading”) — Buy and sell gift cards, databases, SIM cards, social media burner accounts, IP addresses, and physical goods
• “引流类” (“Traffic generation methods”) — Overseas SMS blasts, Baidu promotions, chat scripts, and other services
• “承兑类” (“Acceptance methods”) — Payment methods accepted by merchants include Alipay, WeChat Pay, and cryptocurrencies
• “通道合作类” (“Cooperation Channels”) — Motorcade teams to conduct overseas operations such as collecting or making payments via cash and cryptocurrencies, and logistic operations to move physical goods
• “短视频类” (“Short Videos”) — Short Douyin videos for promotions
• “合作类” (“Cooperation”) — ID Loans, Apple IDs, courier delivery services, and burner mobile phones
• “服务类” (“Services”) — SMS verification, file lookup, and graphic design services
• “卡商类” (“Carding Merchants”) — Money laundering through bank cards and contactless cash withdrawal without cards
• “搭建类” (“Developers”) — Software and bot setup services, and Apple signing/server/VPN/domain setup services
• “其他类” (“Others”) — Other miscellaneous fraud services, social escort services, police impersonation, artificial intelligence (AI), and search engine optimization (SEO)-related services
• “游戏类公群” (“Gaming-related public groups”) — Online gambling and video games

Dabai Guarantee’s Rules (@dabai_e)

Dabai Guarantee’s rules channel (@dabai_e) has posted rules to prevent impersonation of the marketplace and to prevent users from creating their own “public groups” that are not officially regulated by Dabai Guarantee’s administrators. Some of the rules also showcase Dabai Guarantee’s OPSEC measures to prevent scamming and impersonation. The original Chinese text is in Appendix B. The following are some key rules:

• Members are not allowed to create their own public group channel without Dabai Guarantee`s approval.
• Members are not allowed to have private dealings with other parties or platforms, as Dabai Guarantee only guarantees transactions conducted on its platform. Dabai Guarantee also does not provide assurances for transactions with the Public Group “boss” or any other administrator. This means that no individual should have any transactions with the boss directly and should instead use Dabai Guarantee’s funds transfer mechanism.
• Individuals who initiate a chat session with you are 100% scammers; members are to block and refrain from chatting with them.
• The cryptocurrency address belonging to Dabai Guarantee is unique, and anyone sending other deposit addresses is a scammer.
• After members have staked their cryptocurrency as deposits, they are required to send Dabai Guarantee’s leadership screenshots of the deposit to @dabai for verification and confirmation. Any losses resulting from failure to contact @dabai will be the member’s responsibility.

Case Study: Public Group 301

Group Structure

For this report, we will use the Telegram channel “Public Group 301,” which belongs to Dabai Guarantee, as a case study. This is not meant to be a comprehensive analysis of Dabai Guarantee’s massive infrastructure and that of other Chinese-language guarantee marketplaces. It is difficult to accurately quantify how many “Public Group” channels and threat groups are on Dabai Guarantee, as the numbers tagged to Public Groups are not assigned in chronological order, resulting in a lack of visibility — unlike Huione Guarantee, which had a clearnet website that listed the Public Group channels to redirect threat actors. Although there are thousands of channels belonging to Dabai Guarantee alone, understanding Public Group 301’s structure can at least provide insight into how threat actors use Dabai Guarantee in their campaigns.

In guarantee marketplaces, threat actors looking to launch campaigns typically deposit USDT to start a public Telegram group approved by Dabai Guarantee. This model ensures that criminal syndicates do not have to deal with other threat actors directly, but have Dabai Guarantee as a mediator. In the case of Dabai Guarantee’s Public Group 301, affiliate threat groups do not have to engage directly with the group’s leader, @J0hnNo1, and instead receive payments from Dabai Guarantee after the completion of tasks required by @J0hnNo1. Guarantee marketplaces such as Huione, Tudou, Xinbi, and Dabai seek to eliminate the “lack of trust” among Chinese-speaking threat actors. These marketplaces are designed to become trusted platforms that foster coordination and cooperation between different Chinese-speaking criminal groups to achieve their objectives.

Insikt Group navigated through Public Group 301’s Telegram infrastructure in order to identify the redirection flow. As shown in Figure 1, each category contains a hyperlink that redirects to other channels. From Figure 1, selecting category 5, sub-category 2 (“海外扫货车队”, or “Overseas Goods Sweeping Team”) redirected to a pinned message as seen in Figure 2. This message lists four different public channels (“公群”) containing campaigns targeting the US, Canada, South Korea, and Japan.

As seen in Figure 2, “公群” refers to unique Public Group channels for specific purposes or operations. Each public channel here contains a numerical group identifier and a “U” deposit amount, where “U” refers to USDT. For example, “公群935已押2000U” refers to Public Group Number 935, with 2,000 USDT already being deposited in Dabai Guarantee to start the campaign. The naming convention for these Public Groups is ”dbtmxxx”; in this case, Public Group Number 935 will have the Telegram channel @dbtm935. When selecting the second option, “公群301已押1000U韩国，日本扫货组”, which means Public Group Number 301, with 1,000 USDT already deposited to “sweep goods” in South Korea and Japan, the corresponding Telegram channel is @dbtm301.

Upon further investigation and analysis of the channel, Insikt Group assesses that “sweeping goods” refers to the use of illicit means, such as ghost-tapping, to purchase physical goods at physical retail stores (in this case, in South Korea and Japan). This activity also includes ATM cash withdrawals at Japanese or South Korean ATMs.

Key Personnel Involved in Public Group 301

The following terms are important for understanding the operations of criminals involved in Public Group 301, and the entire Dabai Guarantee infrastructure more broadly:

• Boss (“群老板”): The main coordinator overseeing a group’s operations. These individuals are not directly related to Dabai Guarantee and operate more like customers, making use of Dabai Guarantee’s infrastructure to lay out tasks and promising payouts in USDT upon completion. The boss will typically start a campaign by placing significant deposits into Dabai Guarantee’s USDT cryptocurrency addresses (“上押地址”) in order to get Dabai Guarantee’s administrators to approve the creation of a Public Group channel. In Dabai Guarantee’s Public Group 301 (@dbtm301), @J0hnNo1 is the boss of the channel. We observed that this threat actor intends to conduct ghost-tapping and fraud campaigns in Japan and South Korea, with the key objective of obtaining physical goods, cash, and funds through unauthorized transactions. Once the boss confirms receipt of the items and is satisfied with the outcome, they can ask Dabai Guarantee to release the payment to the criminals who participated in the requested task.
• Channel Administrators (“管理员”): Dabai Guarantee’s personnel who act as intermediaries between the boss and other Chinese syndicates, ensuring that the boss gets the items and physical cash, while the Chinese syndicates are paid in USDT. These are the people who will process the payments. Channel administrators will also inspect video evidence provided by sweeping and “goods-receiving” teams and wait for confirmation from the boss that everything is satisfactory before releasing payments to the various Chinese-speaking criminal groups.
• Chinese Syndicates (“犯罪组织”): Teams in charge of providing the people (“mules”) to form sweeping and goods-receiving teams. These syndicates will coordinate with the boss and receive payment in USDT after completing the required jobs.
• Sweeping Teams (“扫货队”): Personnel tasked by the boss or other administrators with obtaining physical goods or conducting ATM cash withdrawals, typically through illegal methods such as ghost-tapping or financial fraud, and to eventually transfer the goods to “goods receiving” teams.
• Goods Receiving Teams (“收货队”): Personnel tasked by either the boss or their respective Chinese syndicates with receiving goods from sweeping teams; the items will eventually have to reach the “goods inspection teams.”
• Goods Inspection Teams (“检货队”): Personnel tasked with physically inspecting the goods and cash being delivered by the sweeping or goods-receiving teams, typically appointed by bosses. When the “goods receiving” team is appointed by the boss, it is also possible that the “goods receiving” and “goods inspection” teams are composed of the same personnel, each fulfilling multiple roles. These teams will inform the boss whether the physical goods are satisfactory, and the boss will proceed to ask Dabai Guarantee to release the payment to the sweeping and goods-receiving teams.

Insikt Group assesses that individuals in the sweeping, goods receiving, and goods inspection teams act as mules, and these teams likely consist of Chinese-speaking tourists who can amass large quantities of physical goods and cash and exit the targeted countries as soon as possible. It is also likely that Chinese-speaking groups have members who are long-term residents of the countries targeted by the operations, such as South Korea and Japan.

Figure 3 is a simplified illustration of Dabai Guarantee’s Public Group 301’s organizational structure. The barrier to entry for participating in “sweeping operations” is low, as participants just need to have the legal right to enter Japan or South Korea, pose as tourists, and follow the instructions given by the boss and other administrators. We estimate that there are likely more than a dozen sweeping teams linked to Dabai Guarantee operating in Japan and South Korea alone. Sweeping teams are likely assigned to obtain certain goods and cash in very specific areas and do not coordinate with one another because they are being deployed by different Chinese syndicates. This model suggests that operations are siloed, where teams act as independent, isolated units that restrict the sharing of information, resources, and goals.

Figure 4 shows the Telegram structure of Public Group 301, where @J0hnNo1 is the channel's boss. The channel is also composed of multiple Dabai Guarantee customer service staff, who serve as administrators. The original creator of the channel is @dbwb22; the Telegram account is no longer active, and @dbwb22 is no longer listed as one of Dabai Guarantee’s official customer service agents.

The distribution of these teams significantly complicates efforts by researchers and law enforcement agencies to track and deter such criminal activities. For example, if members of “Sweeping Team A” are arrested for retail or financial fraud, law enforcement agencies will still need to locate the members of the “Goods Receiving Teams” and “Goods Inspection Teams” before they can even get close to decoding the identity of the boss, who is most likely coordinating operations from a location outside Japan or South Korea’s jurisdiction, such as Cambodia or Myanmar. Additionally, these sweeping teams most likely consist of low-level mules who are considered “expendables” by their Chinese syndicate recruiters. The screenshots in Figures 6, 7, 8, 9, and 10 illustrate the siloed operations conducted by different sweeping teams.

Figure 5 shows Dabai Guarantee customer service personnel @dbtm9 helping to set up public Telegram channel 301 on March 21, 2025, and serving as the channel’s key administrator. This individual serves as a mediator to facilitate transactions and dealings between the boss and other threat actors. The total amount of USDT deposited on that date was 485 USDT; as of this writing, it has risen to 1,000 USDT. The purpose of this channel is to encourage other threat actors to cooperate by taking part in sweeping and goods-receiving operations in Japan and South Korea. In the conversation below, the boss stated that the deposit amount will increase in proportion to the transaction amount. Insikt Group assesses that this would mean the sum of deposit scales with the size of operations in Japan and South Korea.

Figure 6 shows that the boss is looking to recruit sweeping teams to conduct operations in Seoul, South Korea. The main objective is to purchase cosmetics, and once the goods have been delivered, the rewards will be “high.” The final sentence uses the term “速度快”, which means that the boss welcomes any sweeping team that can conduct and complete these operations quickly.

Figure 7 features a sweeping team involved in purchasing tobacco-related products from the Terea brand at a CU store, a South Korean convenience store chain in Seoul, South Korea. It is clear that the boss has goods from specific brands they wish to obtain, and such goods may be resold for cash in other foreign markets at a later date, likely at a lower price to obtain hard currency as soon as possible. Insikt Group assesses that the items are very likely purchased using the ghost-tapping attack vector or through stolen payment card information. This reflects a shift from targeting luxury retailers to smaller-sized businesses, likely to avoid arousing suspicion from law enforcement authorities

Figure 8 shows an Apple Store receipt listing unspecified Apple products totaling 499,600 yen (approximately $3,145.66, as of this writing). Public Group 301’s boss @J0hnNo1 also stated, “Who said there are no large transactions in Japan? Just a single receipt amounted to 500,000 Yen.” This is likely a post encouraging syndicates to send more sweeping teams to acquire as many Apple products as possible, while hinting that the rewards could be lucrative.

Figure 9 provides some evidence that Vietnamese individuals are also involved in sweeping operations. In the top-left corner of the iPhone in the image, the Vietnamese phrase "Không có SIM" means "No SIM card." This indicates that the person holding the phone is very likely a Vietnamese-speaking individual conducting unauthorized banking transactions using burner iPhones. Every single burner phone appears to be tagged with a label, which is very similar to the tactics, techniques, and procedures (TTPs) we documented in our Insikt Group report on ghost-tapping. It is also likely that this individual understands Japanese in addition to Chinese, as they were observed interacting with a Japanese banking application that displayed processed transactions. The transactions shown in the screenshot are dated between July 30, 2025, and August 28, 2025. The ability to use Japanese banking applications is an indicator that this individual is legally residing in Japan. In general, most Japanese banks require foreigners to close their bank accounts before leaving permanently; these regulations are implemented by major Japanese banks such as Shinsei Bank.

Figure 10 shows what appears to be an ATM cash withdrawal or transfer attempt at a Japanese ATM at an unspecified bank. This screenshot is also likely shown as an example of what sweeping teams in charge of withdrawing and transferring cash are expected and required to do.

Figure 11 shows a cryptocurrency transaction of 10,629 USDT via the Tron (TRX) network to a sweeping team for the successful completion of the “mission.” The boss @J0hnNo1 thanked the sweeping team coordinator without identifying them. The exact phrase used while posting the image was “感谢老板信任”, which translates from Chinese to “Thank you boss for trusting me.” Boss, in this context, refers to the Chinese syndicates that provide the sweeping teams for successful operations. In the entire Dabai Guarantee Public Group 301 channel, there were many screenshots of such cryptocurrency transactions being sent to teams that participated in sweeping operations. The boss redacts recipients' cryptocurrency wallet addresses to prevent law enforcement agencies from tracking them. The TRON wallet address used by Public Group 301 is TByDzGWCirpCABaUorkhz5eWhjyDdYWgSo, as shown in Figure 11; this wallet address has facilitated a total of 2,943 transactions as of this writing.

Dabai Guarantee’s Staff and Customer Service Functions (@dabai_f)

Dabai Guarantee maintains a list of its official staff and customer service agents on its Telegram channel @dabai_f to facilitate the creation of Public Group channels and transactions. This system also helps prevent impersonation and scamming. Members are to contact customer service agents directly for any queries or concerns. The staff and customer service teams usually provide the functions listed in Tables 1 and 2; the customer service agents are listed in Figure 12 by their functions and Telegram handles.

Table 1: List of Dabai Guarantee’s official staff and functions (Source: Telegram, Recorded Future)

Table 2: List of Dabai Guarantee’s customer service agents (Source: Telegram, Recorded Future)

Automated Bot System Directs Chinese Syndicates to Relevant Public Groups for Existing Campaigns

Insikt Group analyzed the public administrator bot @dbdbqg_bot to observe how a Dabai Guarantee user would be routed by the platform to participate in cybercriminal activities. To use this functionality, individuals must enter search terms in Mandarin. We used the terms 远程 (remote) and 数据 (data), which returned three and ten public channels, respectively. When querying for the term “远程” (remote), which typically refers to ghost-tapping campaigns involving NFC relay methods, three Public Group channels appeared as relevant results. When querying for the term “数据” (data), which typically refers to databases, ten Public Group channels specializing in datasets appeared in the results. In addition, using a country as a search term, such as 美国 (US), will also return results that show fraud or cyber campaigns targeting the US. This bot function demonstrates how easy it is for criminal groups to search for relevant groups, determine which campaigns they wish to participate in, and identify the types of datasets they are interested in procuring. Table 3 shows the number of Public Group channels involved in fraud or cyber campaigns for the search terms; specific details are not listed due to certain global entities named in the Public Group channels belonging to Dabai Guarantee.

Table 3: Search results of Dabai Guarantee’s Public Group channels using their bot function (Source: Telegram, Recorded Future)

Outlook

Even with guarantee marketplaces such as Huione Guarantee being shut down, many Chinese criminals are likely turning to these Telegram-based guarantee marketplaces to sell illicit goods and to offer their services. Guarantee marketplaces such as Dabai Guarantee have demonstrated their ability to coordinate operations in countries such as Japan, South Korea, Canada, and the US by using Chinese-speaking individuals who are traveling or residing in those geographies to conduct retail and financial fraud. Over time, Dabai Guarantee may be able to establish itself as a trusted escrow platform for Chinese syndicates to rely on, despite the growing competition from existing and new guarantee marketplaces. There is also a possibility that operators of other guarantee marketplaces could execute an exit scam, leading to a loss of trust in guarantee marketplaces as a whole among Chinese criminals.

Threat actors such as @J0hnNo1, the leader of Dabai Guarantee Public Group 301, seek to obtain physical goods and foreign currency through illegal means, giving specific instructions to different syndicates to complete their objectives. Such operations are scalable on demand and will become harder to track and disrupt over time due to the siloed nature of the sweeping and goods-receiving teams. This report showcases the activities and structure of a single group (Public Group 301), which is only one group among hundreds under Dabai Guarantee’s decentralized and growing infrastructure. Ghost-tapping and ATM withdrawals are commonly used by Chinese-speaking criminals for money laundering, and we will likely continue to see more threat actors facilitating such financial and retail-related crime on multiple guarantee marketplaces.

Insikt Group assesses that Chinese syndicates will continue to recruit and deploy non-Chinese individuals with specific language skills to participate in campaigns, as exemplified by the Vietnamese individual mentioned in Figure 9.

Insikt Group assesses that guarantee marketplaces have solidified themselves as a major alternative to traditional Chinese-language dark web marketplaces. This decentralized model is becoming increasingly popular among the global Chinese-speaking criminal diaspora, enabling criminals without sophisticated skillsets to coordinate with syndicates and participate in operations that require physical elements.

Appendix A: Glossary of Terms

Appendix B: Key Rules Written in Mandarin

(Translation available on p. 7)

⚠️交易注意事项⚠️

1.进群交易请先看置顶里面的群规则，交易过程请严格按照交易规则进行，群内所有事情请联系群内交易员 ，私下交易或者其他地方交易，后果自负，大白担保只担保本群内的交易。

2.大白担保业务只担保我们的公群内已经报备过的交易，我们不为公群老板或者其他管理员个人做担保，公群群老板对自己的业务员负责，如果群内业务员违规操作，由公群老板负责。

3.禁止以公群名义私下拉群做单，禁止金额不透明，如被用户举报后果自负。

4.大白担保工作人员不会主动私聊你，主动私聊你的100%都是骗子，请直接拉黑。

5.大白担保的上押地址是唯一的,发其它上押地址的一定是骗子,请大家远离骗子。

6.客户上押后,请及时发送上押截图与我们 @dabai 核实确认,如长时间未找 @dabai 核实确认押金而造成的损失由自己负责。