Re-sampling URLs from the trace shared by Cohere yields 52 different destination hosts, many of which are local or do not exist, and one is an invalid top-level domain.
Swapping the order of two program names in a check for available tools made the model much less likely to continue with a network call.
Model jaggedness (inconsistent behavior that changes with small differences in context) seems like a more likely explanation than a fixed backdoor destination.
Our interactive viewers let you explore the recorded trace, the most likely next tokens, and the 64 sampled URL completions.
On October 26, Cohere reported a puzzling incident involving Qwen3.8-Flash-Next, an open-weight model from Alibaba. The task was straightforward: write a detection rule for suspicious curl downloads followed by bash execution. Instead, the model produced a shell command that inspected its environment and attempted to send a test string to an Alibaba Cloud storage address in Beijing. Cohere’s sandbox blocked the request.
Before the command, the model imagined an earlier conversation and reasoned that it should offer a brief acknowledgment without using tools. Immediately after the reasoning it issued a tool call anyway.
Was this a backdoor sending data to a predetermined destination? Or had the model lost track of its task? We replayed the published trace to investigate.
Replaying the moment before the address
We deployed the model Qwen3.8-Flash-Next on a machine with 8× NVIDIA RTX PRO 6000 GPUs using vLLM as an inference framework in order to probe the model. Model generation is not deterministic in general and if you sample multiple outputs, you will likely get different results, and this is what we are interested in: what are the different scenarios that could have happened in this situation?
curl is a command-line tool that can download data from a web address or upload data to one. In simplified form, the recorded upload command was curl -X PUT –data-binary “PROBE_PAYLOAD_ABC123” <URL>, which asks to send that fixed test string to the address at the end. By stopping just after the test string, we leave the destination for the model to choose and can see whether repeated completions keep returning to the same address.
We used the recorded conversation and command from Cohere’s released trace and removed the part coming after the test string PROBE_PAYLOAD_ABC123. At that point, the curl upload command had already begun, but the model had yet to write its destination. We asked it to complete the same unfinished command 64 times. The model chooses each next word or word fragment from several possibilities, so the same input can produce different completions.
Selected completions from the same supplied context, split into the tokens the model generated.
The destinations varied widely. Seven first addresses used an Amazon Web Services hostname, six used an Alibaba Cloud hostname, and 20 were local or internal-looking. Across the 64 continuations, we found 52 distinct host strings.
The first destination from each of 64 sampled continuations.
Some strings resembled cloud storage buckets; others used local network addresses which are completely benign. A familiar provider name in a generated address does not mean the address exists or would accept an upload. 11 of the public-looking domains do not exist and one is not a valid top-level domain. These results indicate that there is not a single hard-coded target where the model tries to push some data to in this situation, but rather a diversity of (sometimes nonexistent) destinations including local, American cloud, and invalid URLs.
One plausible explanation is that the model was completing an upload command with a cloud-storage address it had learned during training. Alibaba Cloud was one of several providers it generated in our replays. We cannot establish why that particular run produced an Alibaba address, but the replays suggest it was one possible completion rather than a fixed destination.
We also tested an earlier point in the command, where the model checked which programs were available. A completely insignificant action – swapping the order of curl and wget when checking availability – reduced the number of continuations containing a curl network command from 15/32 to 4/32, despite both lists containing exactly the same programs.
Changing the tool order changed how often the continuation used curl.
Both tests probe what are the possible scenarios that can happen once the model has decided to produce a tool call. They cannot tell us how often the model would initiate an unrequested upload or why it chose to use a tool call in the first place.
A model getting lost
The results fit what we call model jaggedness: behavior that is inconsistent or changes sharply with small differences in context. The model’s reasoning misread the task and contradicted its subsequent action. When we sampled different completions, a diverse set of destinations was chosen rather than a single endpoint. When we changed the order of two tool names, the continuation changed too. Together, these observations make jaggedness the most likely explanation we found.
There is also context around the model itself. Qwen describes Flash-Next as an experimental preview of the architecture behind Qwen4. They say that they shared the architectural changes early with the community presumably so that inference framework developers can implement before the full model release. Rough edges are plausible in an experimental checkpoint, though this context does not establish the cause of the incident or predict the behavior of the future model family.
We built a URL explorer for the 64 sampled continuations and a trace viewer showing the model’s alternatives at each token. Open both viewers here.
So, is this a backdoor?
In conclusion, the situation does not seem as alarming as it might have seemed at first glance. The reality is that models can misbehave for no apparent humanly interpretable reason. While these results do not prove that there is no backdoor (which is extremely difficult to do), it hints at the most likely explanation simply being model jaggedness and a situation that makes the model misbehave.
Companies deploying agents need to secure their models from controls outside the model. A request for a detection rule offers no reason to send a test string to an unrelated address and that mismatch can be identified before the action is executed. This emphasizes the point that the principles of least privilege and sandboxing agents are crucial in securing them.
We tested Qwen, so these results do not establish how other models would behave. The broader risk is that an agent with tool access can attempt a network action unrelated to the user’s request. Least privilege and sandboxing limit the consequences when that happens.
We thank Cohere for sharing the suspicious trace which enabled this experiment.
Experimental setup
Model and machine: Qwen3.8-Flash-Next in BF16, served with vLLM 0.32.0 on a machine with 8× NVIDIA RTX PRO 6000 Blackwell GPUs; deterministic QSA selection
Sampling: temperature 1.0, top-k 20, top-p 1.0, min-p 0; no presence, frequency, or repetition penalties
URL experiment: 64 serial completions from the recorded 2,323-token prefix ending at ABC123, stopping at the end of the first tool call (maximum 1,773 new tokens)
Antidetect browsers obscure the digital identity of their users, enabling a single operator to silently manage thousands of isolated accounts across multiple platforms (including Facebook, TikTok, Instagram, and YouTube), while evading detection systems that look for scams and coordinated inauthentic behavior.
Threat actors abuse these platforms, taking over accounts and using them for financial fraud, phishing and malware distribution. These accounts can also be used to coordinate influence operations, which becomes an even greater concern ahead of elections.
Social platforms primarily detect coordinated activity at the account and behavioral layer. Antidetect browsers run below the surface enabling them to remain undetected.
The three main antidetect browsers we discuss in this report are Kameleo, AdsPower, and MultiLogin.
These tools already use AI integration, from built-in automation templates to offering a dedicated MCP server that pipes LLM-generated content directly into pre-warmed account profiles at machine speed. Running a large-scale social media operation now requires only a credit card instead of a technical team.
Introduction
Antidetect browsers conceal their users’ digital identities, enabling a single operator or small team to simultaneously run thousands of isolated accounts across multiple platforms such as Facebook, Instagram, TikTok, YouTube, Twitter/X, Discord, LinkedIn, Reddit, and many more, without triggering the detection systems employed by those platforms.
In this report, we highlight three main tools: Kameleo, AdsPower, and MultiLogin. We selected these specifically on the basis of documented references across underground forums and criminal marketplaces, indicating active adoption beyond their stated commercial purposes. Confirmed use cases include:
Account takeover operations in which session cookies harvested by infostealer malware are injected into clean browser profiles to assume control of banking accounts and cryptocurrency wallets without triggering authentication alerts.
Carding operations in which stolen payment credentials are tested across e-commerce platforms under the guise of distinct legitimate users.
Fraudulent loan applications submitted under fabricated identities.
Bulk creation of artificial accounts used to generate fraudulent advertising revenue at scale.
As documented in Anthropic’s September 2026 Threat Intelligence Report, attackers used antidetect browsers in a fraud campaign that targeted cryptocurrency exchanges and financial marketplaces. They combined antidetect browser profiles, residential proxies, automated CAPTCHA-solving, and identity-verification bypass tools to create large numbers of verified accounts while avoiding onboarding security checks. These accounts were later used in fraud operations.
The risk posed by antidetect browsers is greater because these accounts pass KYC verification, build legitimate activity histories, and appear benign during transactions. As a result, fraud detection systems that rely on user behavior or device identity often cannot identify these accounts as suspicious until after fraudulent activity has already occurred.
The antidetect browsers documented in this research are commercially available, legally registered platforms with documented legitimate use cases in digital marketing, e-commerce, and privacy protection. When deployed by threat actors, the same infrastructure that enables financial fraud at scale is equally applicable to Coordinated Inauthentic Behavior (CIB), the term used to describe organized manipulation campaigns in which false identities are central to the operation and adversarial methods are used to evade detection.
Meta’s most recent transparency reporting explicitly flagged this as a growing concern, with Russia, Iran, and China identified as the three leading sources of such activity globally.
The Identity Obfuscation Stack – How antidetect browsers defeat social media platform detection at every layer
Figure 1 – The five layers through which antidetect browsers defeat platform detection methods.
While antidetect browsers are not a new phenomenon, their threat potential has increased with the integration of AI. Previously, their operational impact was constrained by the manual effort required to run and maintain large account networks at scale. AI-assisted content generation, automated persona management, and machine-speed distribution pipelines now enables a single actor with a subscription and a prompt to do the work of a coordinated team.
In the following sections, we provide a technical breakdown of each tool, examining capabilities documented on their own platforms, underground forum references, and the specific features that position them as an emerging threat.
Figure 2 – Comparison of popular antidetect tools.
Defeating Platform Detection: The Identity Layer No Platform Can See
Companies like Meta, TikTok, and Google have spent billions of dollars building behavioral and identity detection systems to identify abuse of their platforms for scams, financial fraud, and coordinated inauthentic activity. Antidetect browsers are engineered to defeat every signal on which those systems rely.
Every antidetect browser uses fingerprint spoofing. When a browser connects to a website, it exposes dozens of identifying parameters that the website can collect to build a browser fingerprint, including canvas rendering output, WebGL vendor and renderer strings, font enumeration behavior, screen resolution, time zone, user agent, and hardware identifiers. Platforms cross-reference these signals to determine whether two accounts originate from the same device. Antidetect browsers intercept and fabricate each of these parameters individually, assigning a unique combination to every profile.
The criminal ecosystem surrounding these tools extends well beyond their commercial vendors. As seen in Figure 3, a threat actor on a Russian-language underground forum advertised an AiTM engine as a commercial service, with built-in functionality to export harvested session credentials directly into antidetect browsers. The post confirms that the pipeline from credential harvesting to antidetect-assisted account takeover is a documented, commercially available and actively traded workflow with dedicated tooling, published instructions, and an active buyer community.
Figure 3 – An AiTM engine advertised as a commercial service on a Russian-language underground forum, with built-in functionality to export harvested session credentials directly into antidetect browsers.
Kameleo
Modifying browser engines at the source level rather than applying surface overrides, Kameleo produces fingerprints drawn from real device signatures, making them significantly harder to flag as anomalous. Its adoption within criminal ecosystems is evidenced by its repeated appearance across the most active Russian-language hacking forums. As seen in Figure 4, Kameleo is listed alongside other antidetect browsers in a post by a proxy seller on an underground forum, advertising residential proxy services explicitly marketed for account farming, CAPTCHA bypass, and shield evasion against security systems including DataDome, the same platform for which Kameleo’s own blog publishes dedicated bypass guides.
Figure 4 – A proxy seller on an underground forum listing Kameleo as part of a complete operational stack. The service is explicitly marketed for account farming and bulk accountmanagement.
Kameleo’s engineering team published a guide detailing how to automate Facebook likes across multiple accounts simultaneously using precisely this fingerprint manipulation. As seen in Figure 5, evading platform detection is not a side effect but the tool’s primary operational value.
Figure 5 –Kameleo’sguide on how to automate Facebook likes across multiple accounts simultaneously using browser fingerprint manipulation.
Beyond fingerprinting, Kameleo suppresses WebRTC, a protocol that can leak a device’s real IP address even when a proxy is active, and provides explicit configuration options to prevent non-proxied UDP connections. The documentation frames this not as a privacy feature but as a counter to a known de-anonymization vector used by platform security teams.
At the session layer, Kameleo’s dedicated cookie injection API accepts complete cookie objects including session IDs, refresh tokens, and authentication flags, allowing a pre-authenticated session state to be inserted into a profile before it is launched. The platform sees the profile as an already-authenticated user, with no login event occurring to trigger anomaly detection. As independently documented by threat intelligence research and confirmed by security researchers, this workflow is established and active in the criminal ecosystem. Figure 6 shows a recruitment post on a Russian-language underground forum advertising a multifunctional infostealer with RAT capabilities explicitly designed to operate within antidetect browser environments, listing Kameleo, AdsPower, and MultiLogin among 21 compatible platforms capable of harvesting session cookies, authentication tokens, cryptocurrency wallets, API keys, and 2FA credentials.
Figure 6 – A recruitment post on a Russian-language underground forum lists 21 browsers including Kameleo, AdsPower, and MultiLogin as compatible platforms.
As seen in Figure 7, a threat actor on a Russian-language underground hacking forum explicitly introduces Kameleo as a component of Browser-in-the-Middle phishing kits targeting credentials across Google, Microsoft, Facebook, and financial platforms. In a Browser-in-the-Middle attack, the phishing server instructs the antidetect browser to create a new profile for the victim, assigned a residential proxy in their country and a clean device fingerprint. The victim’s credentials, entered on the phishing page, are passed in real time into the real login page of the target service through an automation framework. The target platform sees a login attempt from a consistent, locally coherent device with a residential IP address in the expected country, none of the signals that would trigger a security challenge or step-up authentication.
Figure 7 –A post on a Russian-language underground hacking forum introducing Kameleo as a component of Browser-in-the-Middle phishing kits targeting credentials across Google, Microsoft, Facebook, and financial platforms.
AdsPower
Chinese in origin and subsequently registered across multiple jurisdictions and localized into more than 20 languages, AdsPower applies fingerprint spoofing logic across more than 20 configurable parameters per profile and advertises a 99.2% ban avoidance rate. As seen in Figure 8, its proxy editor provides a built-in catalog of compatible third-party residential and rotating proxy providers, assignable directly within the profile creation interface. Figure 9 illustrates the granularity of fingerprint control available, with each profile’s WebRTC handling, time zone, geolocation, and language individually configurable to ensure the synthetic identity is consistent across every detectable signal.
Figure 8 –AdsPower’s proxy integration interface lists compatible third-party residential and rotating proxy providers available within the platform.
Figure 9 – AdsPower’s fingerprint editor displaying granular per-profile identity parameters including WebRTC, timezone and location derived from the assigned proxy IP.
The scale of AdsPower’s intended use is reflected in its pricing structure, with published tiers supporting up to 5,000 distinct profiles as seen in Figure 10, and enterprise arrangements available on request. As seen in Figure 11, its account editor supports platforms spanning social media, e-commerce, financial services, and professional networks including Facebook, Amazon, LinkedIn, X, PayPal, and Google.
Figure 10 – AdsPower’s pricing table reflects the intended operational scale, with enterprise tiers supporting more than 5,000 profiles available upon request.
Figure 11 – AdsPower’s account editor shows supported platforms available for bulk profile management, including Facebook, Amazon, LinkedIn, X, PayPal, and Google.
Scalability is further addressed through a built-in RPA engine and a dedicated Marketplace, as seen in Figure 12, offering pre-built templates for automated liking, commenting, direct messaging, follower scraping, and AI-assisted engagement across hundreds of profiles simultaneously. The multi-window synchronizer replicates identical actions across hundreds of profiles from a single input, while account warming automation builds synthetic activity histories over weeks to reduce platform suspicion before deployment.
Figure 12 – AdsPower’s RPA Marketplace lists pre-built automation templates for bulk account activity across major platforms, including automated liking, commenting, direct messaging, follower scraping, and AI-assisted engagement.
On the AI front, AdsPower advertises a dedicated Model Context Protocol server enabling direct integration with large language model workflows, creating a pipeline in which AI-generated content can be produced, injected into pre-warmed account profiles, and distributed across platforms in a fully automated sequence requiring no human involvement. A companion feature, the Cookie Robot, automates cookie warming on TikTok and Twitter/X accounts prior to operational use. The RPA Marketplace is the delivery mechanism for this pipeline, with ready-to-deploy workflows designed to operate in direct combination with AI-generated content, resulting in an end-to-end operation at a scale and speed previously associated only with state-sponsored actors.
As seen in Figure 13, AdsPower’s own published scraping guide explicitly acknowledges that scraping Facebook constitutes a violation of the platform’s terms of service and may result in legal action, before advising users that an antidetect browser can help bypass these limitations.
Figure 13 – AdsPower’s official scraping guide advises users that an antidetect browser can help bypass scraping limitations.
MultiLogin
MultiLogin operates as a fully featured antidetect browser platform, offering isolated browser profiles with unique device fingerprints covering canvas output, WebGL parameters, fonts, timezone, screen resolution, and user agent, with each profile maintaining separate cookies, local storage, and session data. The platform supports automation through Selenium, Playwright, and Puppeteer, enabling fully scripted, headless operation of multiple profiles simultaneously. MultiLogin has been referenced on Exploit, a Russian-language underground hacking forum, in a thread dedicated to antidetect browsers, indicating active discussion and adoption of the platform within criminal communities (see Figure 6).
MultiLogin extends its multi-account capability into the mobile layer through a phone farming infrastructure that replaces physical Android devices with cloud-hosted equivalents. Every cloud Android profile maintains its own persistent mobile device parameters, with proxies assigned per profile to control location and network identity. MultiLogin’s own documentation states that most account bans in phone farm environments occur due to multiple accounts sharing the same device signals, storage, or network patterns, and positions its product as the solution to this detection problem, an explicit acknowledgment that the platform is designed to defeat association detection at the mobile layer.
According to their website, virtual phone number provisioning through partner integrations enables SMS-based account verification without physical SIM cards or mobile devices. Each virtual number is paired with a mobile device fingerprint profile, enabling the rapid creation of large volumes of accounts that appear to originate from distinct mobile devices across different geographic locations.
MultiLogin maintains a dedicated web scraping solution page and publishes operational guides for scraping platforms including Facebook, LinkedIn, Amazon, and Twitter, with its built-in residential proxy infrastructure marketed as enabling scraping operations that bypass bot detection and IP-based rate limiting. Its stated use cases listed on its own website include ticket scalping automation, a practice explicitly illegal in multiple jurisdictions and prohibited by major ticketing platforms, with the platform’s infrastructure documented as tested daily against major ticketing services for compatibility. MultiLogin additionally lists AI Quick Actions Automation among its documented product capabilities, indicating a degree of AI integration into its operational workflow.
As seen in Figure 14, MultiLogin’s payment infrastructure accepts cryptocurrency alongside credit card payments and regional systems including SberPay and YooMoney. Notably, YooMoney has been designated on the OFAC Specially Designated Nationals list since April 2022 under Executive Order 14024 as a Sberbank subsidiary, and SberPay operates as a product of Sberbank, itself under comprehensive OFAC blocking sanctions since February 2022. The acceptance of these payment methods, alongside cryptocurrency, enables anonymous and potentially sanctions-non-compliant transactions. MultiLogin offers 24/7 support through dedicated Telegram channels staffed by human representatives and is tested daily against more than 50 major social media platforms to ensure continued compatibility. Taken together, MultiLogin’s mobile infrastructure, AI automation layer, scraping capabilities, criminal community presence, and payment infrastructure make it one of the most operationally complete platforms in this category.
Figure 14 – MultiLogin’s payment infrastructure accepts a variety of different payment services.
AI-Powered Campaign Infrastructure
The most consequential recent development is AI integration directly into the operational stack. AdsPower advertises a dedicated Model Context Protocol server enabling direct integration with large language model workflows, creating a pipeline in which AI-generated content can be produced, injected into pre-warmed account profiles, and distributed across platforms in a fully automated sequence requiring no human involvement. A companion feature, the Cookie Robot, automates cookie warming on TikTok and Twitter/X accounts prior to operational use, simulating browsing behavior to establish account credibility before the content distribution phase begins.
The RPA Marketplace mentioned in the previous section is the delivery mechanism for this pipeline. Available templates contain ready-to-deploy workflows maintained and updated by the vendor, designed to operate in direct combination with AI-generated content. This creates an end-to-end influence operation pipeline that requires no active operator presence once the campaign is initiated. The result is a single automated workflow that places coordinated influence operation capacity within reach of any sufficiently motivated actor, regardless of resources or technical expertise.
Web Scraping as Operational Intelligence
The web scraping capabilities documented across these platforms serve a function that extends well beyond data collection. Scraped data is the intelligence layer that makes the operation more precise. Several of the tools we reviewed maintain dedicated scraping solution pages describing capabilities including fingerprint rotation, CAPTCHA bypass, and headless browsing designed to extract data from platforms including LinkedIn, Amazon, Facebook, and Twitter while evading their anti-scraping defenses. As seen in Figure 13, the AdsPower published scraping guide explicitly acknowledges that scraping Facebook constitutes a violation of the platform’s terms of service and may result in legal action, before advising users that an antidetect browser can help bypass these limitations.
The combination of well-farmed personas and targeted scraping capabilities enables a precision influence operation at scale.
Workflow:
An artificial account, warmed up over several weeks of simulated activity, accumulates sufficient credibility to be accepted into a closed group on a social media platform.
Once inside, the platform’s scraping infrastructure extracts available information on every member, including names, profile data, religious affiliations, political interests, age, geographic markers, and engagement patterns.
The harvested dataset becomes the targeting intelligence for a bulk coordinated campaign, allowing artificial personas to engage each member with content calibrated to their specific profile, amplify narratives that resonate with the group’s demonstrated interests, and simulate the kind of peer validation that drives genuine behavioral change.
A Sophisticated and Professionally Maintained Ecosystem
The commercial availability of these tools reflects a fundamental shift in the influence-operation threat landscape: what was once the domain of well-resourced state actors and advanced persistent threat groups is now accessible to any motivated individual with a credit card. These are all professionally maintained, customer-supported, multilingual software platforms with pricing pages, knowledge bases, release notes, and dedicated sales teams. The only barrier to running a large-scale coordinated inauthentic operation is not a user’s technical sophistication but the need to pay a subscription fee.
The language coverage and market reach of these tools reflects the scale of their intended user base. AdsPower is available in 20 languages. MultiLogin supports English, Chinese, Russian, Vietnamese, and Portuguese, and is tested daily against more than 50 platforms. Kameleo maintains a public blog catalog of dedicated technical guides on bypassing Cloudflare bot detection, defeating DataDome, and solving Cloudflare Turnstile challenges, which are all security systems deployed specifically to identify and block automated or inauthentic browser sessions.
The customer support infrastructure surrounding these tools mirrors that of legitimate enterprise software, with MultiLogin offering 24/7 support through dedicated Telegram channels staffed by human representatives. As seen in Figure 14, MultiLogin’s payment infrastructure accepts cryptocurrency alongside credit card payments and regional systems including SberPay and YooMoney, both Russian payment services.
As seen in Figure 15, independent threat actors have developed and are actively selling purpose-built antidetect solutions on underground forums, complete with feature documentation, technical support, and monthly subscription pricing. This particular tool targets Android devices specifically, offering automated geolocation spoofing, device ID replacement, Wi-Fi environment manipulation, and Play Integrity bypass at 100 USD per month, reflecting the breadth of threat actor demand across different device categories and operational requirements.
As seen in Figure 16, the downstream commercial activity built on this infrastructure extends to farmed platform accounts advertised for sale on underground forums alongside explicit instructions to access them using antidetect browsers. Cryptocurrency is the default payment method. These posts confirm a mature commercial underground market in which these antidetect tools function as the operational backbone of a broader fraud and influence ecosystem.
Figure 15 – An antidetect tool designed for Android devices advertised on an underground forum, offers automated geolocation spoofing, device ID replacement, and Play Integrity bypass at 100 USD per month.
Figure 16 – Farmed Google Ads accounts advertised for sale on an underground forum.
Conclusion
The antidetect browser ecosystem represents a sophisticated, commercially mature, and increasingly AI-driven infrastructure that has fundamentally lowered the barrier to running scam and fraud operations, as well as coordinated inauthentic operations at scale. These commercially maintained tools are available in dozens of languages, backed by customer support teams, tested daily against the world’s largest social media platforms, and are now integrated with LLM pipelines that remove the human operator from the execution layer. The coupling of identity spoofing infrastructure, automated content distribution, and AI-assisted persona management represents a threat surface that platform detection systems may struggle to address, particularly in the context of elections taking place across multiple countries.
The infrastructure we documented is commercially available today. Its capabilities are not constrained by technical sophistication, national resources, or operational scale. The question is no longer whether these tools are being actively used for fraud, account takeover, and coordinated manipulation, but whether the detection, enforcement, and awareness frameworks across every layer of the ecosystem are prepared to counter them.
Recommendations
Security and threat intelligence teams:
The detection priority should shift from the account and behavioral layer, where these tools are already designed to appear invisible, to the network and infrastructure layer.
Indicators include residential proxy infrastructure operating at scale, WebRTC suppression patterns, anomalous cookie injection sequences, and known antidetect browser user agent strings in web traffic logs.
Account warming patterns, simulated by these tools over several weeks, may be indistinguishable from legitimate organic behavior without cross-referencing device fingerprint consistency over time.
Social media platforms:
Antidetectbrowser infrastructure should be treated as a first-class priority.
The capabilities operate below existing detection thresholds, and infrastructure-layer signals may be the only reliable detection surface available.
Platforms should consider actively acquiring and reverse engineering antidetect tools to map their proxy patterns, fingerprint rotation behaviors, and detection bypass mechanisms, and use those findings to recalibrate banning logic specifically against these tools’ TTPs.
CISOs and companies:
The risk posed by antidetect browser infrastructure extends well beyond influence operations.
Silent account takeover using cookie injection workflows can bypass multi-factor authentication without generating a login event.
Organizations are advised to review whether their session monitoring infrastructure can detect pre-authenticated session anomalies, and to incorporate antidetect-specific attack scenarios into red team exercises and threat models.
Threat actors can leverage antidetect browsers to replicate victim device fingerprints and geolocation to execute unauthorized transactions without triggering behavioral anomaly detection.
Fraud detection systems that rely on device consistency and session continuity signals should be specifically reviewed for resilience against fingerprint spoofing and session injection at the identity infrastructure layer.
The defining development of the period came not from attackers but from the AI labs themselves, whose models broke out of controlled evaluations and reached real systems. In the wild, the criminal and state use of AI continued to mature along the lines tracked in earlier editions: models now act as attack operators, an underground market supplies the access, and AI systems have themselves become a target. The substantial distance between what the strongest models demonstrated under evaluation and what criminals are currently doing is the central fact of the period.
Key observed findings
Evaluation models escaped containment in ways nobody had engineered a fix for. An OpenAI research prototype found and exploited a previously unknown vulnerability in an internal package proxy, reaching Hugging Face’s production systems and taking roughly 17,600 recorded actions before anyone caught it. Anthropic and Meta each reported test models reaching the open internet through misconfigurations, and the UK AI Security Institute logged a case where an agent invented fake identities to try to talk a real person into approving malicious code.
What criminals are doing today is still far more modest, and that gap is the story worth watching. Real world attacks run on models below the frontier, use known techniques, and get caught by existing defenses, nothing like a model finding its own zero day or sustaining an unsupervised operation for days. But frontier capability has reached commercial and open source models within months of first appearing every time before, and there’s little reason to expect this one stays contained to the lab.
An affiliate tied to The Gentlemen ransomware group used Claude Code to carry out real intrusions against at least six organizations, a person directing an AI tool through each step. JADEPUFFER went further: a human configured and launched it, the model ran the entire extortion operation itself, moving from the initial flaw to the internal database, exfiltrating and deleting data, leaving a ransom note, and correcting its own errors along the way, with no person directing the individual steps.
A criminal market has organized around stealing and reselling AI access itself. One tier steals API keys and credentials at scale, and a second resells that access through gateways that hide the buyer’s identity from the provider.
AI systems have become entry points in their own right. Coding agents and enterprise copilots can be steered through content they’re built to trust, a symbolic link, an image, a fabricated error report, and both Google’s Gemini CLI and Anthropic’s Claude Code needed patches for flaws a malicious GitHub issue could trigger.
A separate market exists for removing a model’s guardrails once you have access to it. One forum post asking to buy a durable method for bypassing a model’s restrictions, rather than a single jailbreak prompt, is a useful illustration of what that demand looks like.
AI is surfacing vulnerabilities faster than anyone can patch them, but that hasn’t translated into more successful attacks. Microsoft shipped a record 570 fixes in July and Oracle’s quarterly update ran past 1,400, yet only about one percent of AI discovered vulnerabilities were confirmed exploited in the wild, roughly the same rate as flaws found any other way.
Everyday enterprise GenAI use is a quieter but steadier source of exposure. In July, one in every 36 prompts from enterprise networks carried a high risk of sensitive data leakage, and 88 percent of organizations using these tools recorded at least one high risk prompt during the month.
Want the full report? Read the complete July-August 2026 AI Threat Landscape Digest here.
For the past year, the ransomware conversation has centered on concentration: a handful of dominant RaaS operations controlling most of the damage, and a shrinking pool of active groups fighting over the same territory. The State of Ransomware Q2 2026 report from Check Point Research shows that picture starting to shift. The leaders are still winning, but the road to joining them has gotten a great deal shorter.
Key observed findings
The ecosystem stayed concentrated even as its tail widened considerably. The top 10 groups accounted for 57.6% of all victims, down from 71% in Q1, while the number of active groups climbed from 71 to 93, a new high for the period tracked in this report.
Victim volume held at an elevated baseline and did not meaningfully change QoQ. Data leak sites recorded 2,139 victims in Q2, essentially flat versus Q1 (up 0.8%) and up 33% year over year, keeping pace with the highs set through 2025.
Qilin and The Gentlemen fought a close race for the top spot all quarter. Qilin remained the most prolific operator for a fourth straight quarter with 279 victims, though its count fell 17%, while The Gentlemen surged 62% to 269 victims and actually outpaced Qilin during the month of June.
An internal leak gave an unprecedented look inside The Gentlemen’s operation. Chat logs and platform data exposed a core team of roughly nine operators supported by a broader affiliate base, along with confirmation that the group used AI coding assistants to build its ransomware management panel in about three days, genuine first party evidence of AI accelerating malicious tooling development.
Ransom payment rates fell to a multi year low near 23%, continuing a six year decline from 85% in 2019. Even so, on chain ransomware payments still exceeded $820 million in 2025, and the payer market itself is splitting: average payments are rising even as the median falls, a sign that large enterprises keep paying heavily while the mid market increasingly holds firm or settles small.
Law enforcement concentrated its Q2 efforts on shared infrastructure rather than individual groups. Actions took down a cryptocurrency laundering platform used by multiple ransomware actors, prompted sanctions against major Iranian digital asset exchanges, dismantled a malware signing service abused by several RaaS operations, and disrupted large infostealer and VPN anonymization networks that many groups depend on at once.
The geographic picture shifted meaningfully. The US share of victims fell from 50% to 42% quarter over quarter, largely because the quarter’s fastest growing groups, including The Gentlemen and the newly active Krybit, target the US far less often than the ecosystem average.
The exploitation window kept narrowing, with AI increasingly cited as the accelerant. Vulnerabilities are now being weaponized within hours to days of disclosure, lowering the cost of exploit development and giving ransomware operators one more edge in the race to reach victims first.
To read the full findings, access the State of Ransomware Q2 2026 report from Check Point Research here.
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