Day: October 20, 2026

  • Active Exploitation of May 2026 PAN-OS CVE-2026-0257 (Updated October 20)

    Active Exploitation of May 2026 PAN-OS CVE-2026-0257 (Updated October 20)

    Ransomware operators including Spikey Scorpius (aka Qilin) and Tarnished Scorpius (aka Inc) are exploiting PAN-OS vulnerability CVE-2026-0257.

    Source: Palo Alto Networks Unit 42 · 2026-10-20

  • Vučić Rejects Russia Sanctions, Blames Foreign Powers for Protests

    Vučić Rejects Russia Sanctions, Blames Foreign Powers for Protests

    Serbian leader Aleksandar Vučić stated Tuesday that Serbia will continue resisting pressure to impose sanctions on Russia, speaking in an interview published by Ksenia Sobchak, the daughter of Vladimir Putin’s former political mentor. He claimed the opposition would introduce sanctions within 10 days of taking power and recalled a conversation in which Putin questioned his decision to resign. Vučić defended Serbia’s foreign policy as independent of both Moscow and Western powers, while warning that escalating global tensions could lead to a wider war.

    Ahead of Serbia’s October 25 parliamentary elections, Vučić accused foreign powers of pouring hundreds of millions or even billions of euros into NGOs and media outlets to destabilize Serbia and overthrow its government, though he presented no evidence. He portrayed the mass protests sparked by the 2024 Novi Sad railway station disaster, which killed 16 people, as an attempt to seize power without elections. The student-led demonstrations have since grown into a nationwide anti-corruption movement, posing a major challenge to his ruling Serbian Progressive Party. Vučić expressed confidence in an electoral victory that would allow him to return as prime minister.

    Source: OCCRP · 2026-10-20

  • LOOF Data Sale Claim Includes 654K Records and 90,555 Documents in 48 GB Archive

    Data Exposure Report
    LOOF Data Sale Claim Includes 654K Records and 90,555 Documents in 48 GB ArchiveFrance
    Animal Registry
    Customer & Pedigree Data
    For Sale
    Unverified

    LOOF Data Sale Claim Includes 654K Records and 90,555 Documents in 48 GB Archive

    Claimed lines~654K
    Animals~330K
    People~40K
    Compressed size48 GB

    LOOF Data Sale Claim Includes 654K Records and 90,555 Documents in 48 GB Archive

    An actor using the handle “ChimeraZ” claims to be selling a LOOF dataset. LOOF, the Livre Officiel des Origines Félines, is described in the listing as the French organization responsible for registering pedigree cats and maintaining official feline genealogical records.

    The post advertises approximately 654,000 lines, around 330,000 animal records, roughly 40,000 people and 90,555 files totaling 48 GB compressed. The stated formats are JSONL, PDF and images. The actor says the document collection includes pedigrees, DNA tests, birth declarations, attestations, certificates and other customer-related material. The dataset, its source, the stated counts and current availability have not been independently verified.

    Post details

    OrganizationLOOF
    CountryLOOF Data Sale Claim Includes 654K Records and 90,555 Documents in 48 GB ArchiveFrance
    SectorPedigree cat registry / animal identification
    Actor“ChimeraZ”
    Claimed linesApproximately 654,000
    Claimed animalsApproximately 330,000
    Claimed peopleApproximately 40,000
    Post date shown

    What the post claims

    • Approximately 654,000 lines
    • Approximately 330,000 animal records
    • Approximately 40,000 people
    • 90,555 files listed
    • 48 GB compressed size
    • JSONL, PDF and image formats
    • 332K kittens JSONL dataset
    • 285K litter / portée JSONL dataset
    • 123K documents JSONL dataset
    • 15K profile JSONL dataset
    • 22K user JSONL dataset
    • Animal chip and identification fields
    • Pedigree-request and genetic-testing fields
    • Birth and breed information
    • Customer names, addresses, phone numbers and emails
    • Business / SIRET fields visible in profile records
    • Pedigrees, DNA tests and birth declarations claimed
    • Attestations and certificates claimed
    • 1,000 samples advertised
    • Price listed as make offer
    • XMR payment requested; escrow accepted

    The introductory text says there are 90,551 documents, while the structured summary lists 90,555 files. This report preserves the structured file count and notes the discrepancy rather than reconciling it. Personal values shown in the visible samples are not transcribed here.

    Screenshots

    The supplied captures show the LOOF sale claim, stated volumes and formats, kitten, litter, document, profile and user samples, sample mirrors, pricing terms and a Session contact identifier. Direct sample-download links and personal sample values are not reproduced here.

    IOCs & identifiers

    Identifiers directly visible or supported by the supplied material. These support correlation and do not independently verify the dataset claim.

    Type Identifier Source
    Actor handle ChimeraZ Screenshots 1–3
    Session ID 05c1396ee8a9d6df7ae4497a07f2fbc75b31344f5e0cbd91dacde4c04c88c4c254 Screenshot 3
    Named organization domain loof[.]asso[.]fr Screenshot 1

    The organization domain identifies the entity named in the claim, not malicious infrastructure. No Tox ID, Telegram handle, malware hash or attacker-controlled IP address is visible. Direct sample-download mirrors shown in the source material are intentionally not reproduced here.

    Mapped techniques

    Claimed identifies behavior explicitly described by the actor. Inferred identifies an analytical mapping supported by the supplied material. Neither label means the activity has been independently verified.

    • CollectionT1213.006Data from Information Repositories: DatabasesInferredThe listing presents multiple structured JSONL datasets covering animals, litters, documents, profiles and users. The supplied material does not reveal how the data was obtained or independently establish unauthorized access to LOOF systems.

    Potential impact

    If authentic, the claimed exposure could reveal animal identification and pedigree information, customer names, addresses, phone numbers, email addresses, business identifiers and attached documents. The combination could support targeted phishing, impersonation, fraud involving pedigree or registry records, and broader privacy abuse affecting breeders, owners and other users.

    Status Unverified

    Dark Web Informer has not independently verified the dataset’s authenticity, source, completeness, approximate 654,000-line count, animal or people totals, 48 GB compressed size, document count, sample contents or current availability. The supplied screenshots document the listing and visible samples, not independent confirmation of the underlying material.

    Dark Web Informer

    Source: Dark Web Informer · 2026-10-20

  • There’s only so much code: the rise in vulnerability numbers

    There’s only so much code: the rise in vulnerability numbers

    • Frontier AI models (like Mythos) are rapidly mining decades of technical debt across ubiquitous legacy libraries, driving monthly Microsoft Patch Tuesday vulnerability advisories up more than tenfold (reaching 1,415 this month). This volume is overwhelming open-source maintainers and putting pressure on the traditional 90-day coordinated disclosure model. 
    • Despite a 105% year-over-year surge in CVEs, Known Exploited Vulnerabilities (KEVs) grew by only 37%, and only 3 of September’s 975 Microsoft advisories saw in-the-wild exploitation, demonstrating that the vast majority of AI-discovered bugs are not practically exploited. 
    • We may be able to take new-bug-class vulnerability spikes as evidence that these vulnerabilities will eventually level off and decline, once the historical code backlog is remediated and AI verification is embedded directly into new development pipelines. 
    • With attackers able to parse patches faster via AI, defenders cannot cover for all possible threats. Organizations should continue to build robust defense-in-depth. Prioritize high-exploitability flaws in their specific environments, implement interim mitigations (such as LiveProtect), deploy behavioral anomaly detection, and integrate current AI evaluation models into their software build chains. 

    There's only so much code: the rise in vulnerability numbers

    The September Microsoft Tuesday release included 975 vulnerability advisories. This month, 1,415 vulnerabilities were just dropped. Compared to a pre-June average of 100 a month, that increase is staggering. Where did they come from? 

    As of April, frontier models came onto the scene more fine-tuned than ever toward vulnerability detection, particularly with the introduction of Mythos. The deluge brings a lot of questions: 

    • If these numbers are real, why hasn’t the internet collapsed? 
    • Are these vulnerabilities a new breed, and if not, why didn’t analysts find them in code that is sometimes decades old? 
    • Is the coordinated disclosure model still relevant in an era of this kind of speed? 
    • How in the world do defenders deal with those numbers? 
    • Should we go home, kiss our spouses, and prepare for the end? 

    The answer to many of these questions boils down to this: The majority of these vulnerabilities are coming from repositories of old code from large-profile products and systems; and disclosure does not equal weaponization. 

    Where is it from? 

    AI is chewing through a backlog of technical debt in libraries and tools used as the dependable, best-practice lynchpins of so many environments. These models have a new perspective, an ability to “think” without some of the hangups or assumptions of human defenders, and they are thorough in listing those vulnerabilities. 

    There's only so much code: the rise in vulnerability numbers

    Entrenched older tools have always been prime targets because of their predictability; attackers know those tools and all their secrets, and they know what is likely to be present in a network with them. Multi-exploit attacks can be deployed with little variability.  

    Pre-Mythos threat actors exploited log4j (CVE-2021-44228) to immense success. The library was ubiquitous, included in nearly every environment, but not a component that got cataloged in visibility scans. A combination of scale and low visibility meant defenders were blindsided, scrambling to find out if they even used the log4j library before starting to concoct a mitigation plan. 

    Talos has ongoing relationships with some of the largest technical vendors (such as the Microsoft Active Protections Process (MAPP) agreement). Organizations share additional information—detection guidance or maybe a POC — for vulnerabilities that are most likely to be exploited, to enable Talos to provide adequate coverage when the advisories are announced.  “Microsoft believes it is important for us to provide detection content, based on their guidance,” as Yves Younan, Director of Talos Vulnerability Discovery, Detection, and Testing, says.  

    Talos has needed to invest more resources to get coverage in on time for critical vendors like Microsoft to keep up with notifications coming in fast and often on staggered or out-of-band timelines. 

    For larger vendors, the pressure is handleable for now. However, open-source projects are getting flooded. Some projects have a trusted group that can combine to tackle patches, and some share with the full community for immediate assistance, but it is up to the project. The internet runs on open-source work. 

    The pace of discovery presents a challenge for the traditional coordinated disclosure system vulnerability hunters have been using for years, and some are calling it into question.  

    Security vulnerabilities should be reported, to enable an informed defender base and regular patching, and vendors responsible for vulnerable code need time to create coverage. A timed disclosure model where notifications are sent to a vendor, who then has a set period of time (typically 90 days) to provide coverage before the disclosure is released publicly, has created a more secure online environment for decades.  

    Now, many practitioners are releasing advisories publicly without a disclosure period beforehand. The reasoning is that if one AI can find a weakness, any other could, so the vulnerability is already, effectively, released.  

    This conversation is actively changing expectations around vulnerability disclosure, and guidance is in flux. 

    Will it stay this high? 

    No one knows the future, but we can always look to the past for insight.  

    Numbers of vulnerabilities jump drastically when a new bug class is identified, as their possibilities are explored to the fullest. Then the discoveries plateau and drop off. 

    When format string vulnerabilities were discovered as a new bug class in 2000, the initial influx of discoveries ramped up and up, until reaching a peak in 2005 and then slowly declining over the next couple of years. Format string vulnerabilities are still discovered, but they are a smaller overall fraction of the number of CVEs discovered in a year.  

    Theoretically, AI-assisted vulnerability discovery could go the same way.  

    There's only so much code: the rise in vulnerability numbers

    While frontier models have not discovered a new vulnerability class, they are more efficient at discovering known types of software vulnerabilities. Given that a lot of vital software has a long history of code releases, there’s plenty of code to mine for weaknesses. But there’s only so much code… right? 

    Old and commonly used tools are often under the highest scrutiny, lending them one kind of safety: Because of their popularity, utility, and stability, when popular tools have vulnerabilities, they are discovered fast and patched promptly. 

    As the historical backlog of essential systems is turned over by AI, vulnerabilities will be found and patched. New code will ideally be run through AI bug-checkers that will prevent new vulnerabilities from being introduced. The rate of new vulns should level off and drop accordingly.  

    There are some caveats. In the past, as vulnerability disclosures increased through coordinated disclosures, we saw increased adoption of mitigations. Exploitation became significantly harder. Attackers often need to chain together 3 or more vulnerabilities to successfully exploit a system.  

    However, more awareness of vulnerabilities often leads to more exploitation. Attackers often use vendor patches meant to protect customers to discover where vulnerabilities might exist. The frequency of patching has delayed human attackers via parsing bottlenecks, but AI can eliminate that problem.  

    Are the numbers the real issue? 

    The volume is certainly up.  

    There's only so much code: the rise in vulnerability numbers

    Fresh as this blog is being finished, the October Patch Tuesday dropped almost 500 more than last month. 

    With refinement, any vulnerability could become a link to a path of effect. But… not all vulnerabilities are exploitable. Some are wildly impractical for attacker use. In fact, the percent of Known Exploited Vulnerabilities (KEVs) is vastly smaller than the found vulnerabilities.  

    There's only so much code: the rise in vulnerability numbers

    Of the 975 vulnerabilities Microsoft reported in September, there were only three in-the-wild exploits detected. When viewed by percentage, vulnerabilities versus KEVs tell a different and perhaps reassuring story. 

    There's only so much code: the rise in vulnerability numbers

    Compared with the same period last year, additions to the known-exploited vulnerability list increased by 37%, while published CVEs increased by 105%. However, we wouldn’t necessarily expect those figures to grow in proportion. Adversaries only need enough useful vulnerabilities to compromise their targets, and the same vulnerabilities can be reused against many organizations. The KEV count tracks distinct vulnerabilities known to be exploited, not the number of attacks or successful compromises. 

    AI helps ingest code, advisory reports, and attack surfaces with inhuman speed, but it cannot currently make judgment calls about the value of a weakness. 

    For defenders, the question remains the same: Which vulnerabilities provide a viable path into their environment, and how do you close those paths?  

    What do we do now? 

    The undeniable danger here is in exploitation time going down rapidly, while the vulnerabilities that could become exploits is still going up. The biggest hurdle for a defender is determining what is a real vulnerability for their environment, and what is not.  

    Different defenders have different forms of control depending on their role.  

    For those who are writing code, the new standard validation chain should include AI evaluation by models at least as up to date as those being used to hunt for vulnerabilities. It is relatively cheap to run new code through AI, but very expensive to run a cumbersome backlog of old code through. Bite it off in chunks. 

    For those whose role involves keeping the tools ready: Upgrade ASAP.  Don’t stop there.  

    • For Cisco customers, implement LiveProtect to mitigate vulnerabilities between advisory release day and patching, to give time for that patching. Do this for everything you own. 
    • Move beyond static domain blocking and implement behavioral detection that alerts on anomalous traffic patterns directed toward automation platforms. 
    • Hone your focus to the vulnerabilities with the highest exploitability in your environment. KEVs are far less common among the sea of advisories, and their numbers have not risen proportionally with the AI-assisted deluge. 
    • Focus on building defense-in-depth. Employ multiple protection systems, to cover for delays in patching. Implement interim mitigations (such as LiveProtect), create custom IPS/Snort triggers to block pre-patched traffic using SnortML, deploy behavioral anomaly detection, and integrate current AI evaluation models in software build chains. 

    Dive deeper into this conversation with the latest TTP interview with Yves Younan and vulnerability researcher Bella Fusari.

    For more numbers and advice for defenders: 

    Source: Cisco Talos Intelligence · 2026-10-20

  • The Risk of AI-Powered Antidetect Browsers in Online Fraud and Influence Operations

    The Risk of AI-Powered Antidetect Browsers in Online Fraud and Influence Operations

    Key Findings

    • 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 account management.

    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’s guide 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:

    1. 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.
    2. 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.
    3. 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.

    Source: Check Point Research · 2026-10-20

  • Introducing a new AI chapter in the NCSC research problem book

    A revolution is underway at the intersection of AI and cyber security.

    Source: NCSC UK · 2026-10-20

  • Possible new Cellebrite iPhone Radio Zero-Day

    Justin.sh is reporting on a confidential Cellebrite document that has been leaked to him:

    Cellebrite calls it Advanced Unlocks (AU from here on), not to be confused with the “advanced unlocking services” they’ve marketed for years. A press release from earlier this year confirms AU is a new product for 2026.

    […]

    According to Cellebrite, AU:

    • is a “next-generation AFU vector that is more resilient to Apple’s OS changes”
    • works with Stolen Device Protection enabled, which tightens USB restrictions
    • is enabled by a dedicated appliance with a stated range (!) of about 8 meters.

    AU = Advanced Unlocks, Cellebrite’s term. AFU = after first unlock.

    Source: Schneier on Security · 2026-10-20

  • Police arrest Qilin ransomware suspect on holiday, as links made to Synnovis hack that contributed to patient’s death

    A 28-year-old Russian man has been arrested by German police, suspected of being a key member of the Qilin ransomware gang. Detectives have linked the man to the attack against blood-testing firm Synnovis, that disrupted London hospitals, and resulted in the death of a patient.

    Read more in my article on the Hot for Security blog.

    Source: Graham Cluley · 2026-10-20

  • Is the ad a scam? How to tell before you click

    You can trust a social media platform and still get scammed by an ad running on it

    Source: WeLiveSecurity (ESET) · 2026-10-20

  • The journey of a paper unicorn: adventures inside a modern warehouse

    The journey of a paper unicorn: adventures inside a modern warehouse

    Introduction

    Imagine you’ve ordered a small paper unicorn from an online marketplace. You clicked “Buy”, received a cheerful notification, and expect a courier to bring the box tomorrow. If the unicorn is delayed by a day, it will be annoying but not catastrophic: an apology, a discount promo code — and the incident is forgotten.

    Now let’s multiply one late unicorn by several hundred thousand orders.

    Packages start heading to the wrong cities. The website shows an item is in stock, even though the required bin is empty. Conveyors stop, boxes get stuck between floors, the cargo piles up with no freight manifest. Trucks line up at the gates, and every minute of downtime turns into blown schedules, support tickets, fines, and lost sales.

    It is at exactly this scale that a cyberincident in a warehouse becomes a problem for the entire business. A modern warehouse is no longer just a big building with shelving. It is a tightly interconnected hybrid system: web applications, radio channels, and an automated factory. Physical processes here depend on data, and errors in data have very physical consequences. Examples include an outage that affected warehouse management systems at the grocery giant Morrisons, and the 2025 Form 10-K annual report of United Natural Foods, Inc. (UNFI), which confirmed unauthorized activity on information technology systems that impacted order fulfillment. More recently, reports emerged that a cyberattack disrupted eight CEVA Logistics warehouses across Europe and exposed customer delivery data.

    This paper follows a single order through a warehouse to show where the operation is vulnerable, the stages it traverses, the systems that drive it, and the points where an attacker can reach them. We then set out the threat model: what is at risk, who is in a position to take it, and how each risk lands on the business. From there, we look at how an attacker gets in, and what reaching the warehouse management system costs in practical terms: orders unshipped, inventory records nobody can trust, and an operation that stops. We close with recommendations on where the controls that matter actually sit.

    Tracing the unicorn

    A modern warehouse is a sequence of stages involving many machines and people, all working together to keep the business process stable so that, in the end, your order gets delivered. This process consists of eight main stages, shown in the figure below.

    Stages of order fulfillment

    Stages of order fulfillment

    So let’s assume we’ve ordered our paper unicorn and are waiting for it at home. Let’s follow our order through all the main stages, from its arrival at the warehouse to the final stop on its journey.

    1. Unloading
      First, the unicorn arrives in the unloading zone, packed in a big truck. The driver is assigned a gate, for example, via a tablet with kiosk mode enabled, or an outdoor display board, indicating where the truck should park and where the cargo will be unloaded. Even at this very first step, a digital command (the gate number on the screen) is already triggering a physical action. Therefore, incorrectly assigned gates can create a queue, delay unloading, or send goods into the wrong workflow.
    2. Receiving
      At this stage, employees identify the cargo. They scan a barcode using a portable data terminal (PDT) and the central system receives a message indicating that this object arrives at the warehouse. From this moment, the unicorn exists in two forms at once: as a real box and as a record in the information system. If these two versions diverge, the error is inherited by all subsequent processes.
    3. Putaway
      Next, the goods are moved into internal storage. Two new devices come into play here. First, a thermal printer, connected to the internal network over Wi-Fi, creates an internal label for each item. Then an employee, forklift, or automated system moves the goods to the assigned bin, often with the help of lifts controlled by PLCs. Both of these devices can be valuable attack entry points: one can let an adversary pivot into the internal network, while the other can give them control over an electric motor.
    4. Storage
      Then the unicorn goes into storage. Many distribution centers use multi-level mezzanine structures: several floors of metal shelving inside a huge space. They allow efficient use of the building’s height, but at the same time complicate control over the technical infrastructure. Wi-Fi access points, cameras, switches, cables, and sensors may be mounted high overhead or buried deep inside the rows. A forgotten Ethernet cable or misconfigured networked device (access point or switch) left behind in a dark corner can become a convenient initial access point into critical infrastructure.
    5. Picking
      When the customer places an order, picking begins if the goods were already stored in the warehouse or as soon as they are unloaded. A PDT, workstation, light indicator, or voice terminal directs the employee to the required bin where they pick the goods and move them to the next stage. These devices are themselves potential attack entry points: compromising a workstation or PDT could allow an attacker to manipulate picking information, which could misdirect employees to the wrong bin. Here, both the speed and integrity of the information are critical, and any manipulation or disruption can lead to wrong orders, delays, or problems in subsequent stages.
    6. Sorting
      At the sorting stage, the warehouse starts to look more like a factory. Boxes move through conveyor belts and pass through different scanners, diverters, and chutes. The sorting plans are usually stored and managed by dedicated servers, while PLCs are responsible for controlling the machinery and executing these plans. For example, when the warehouse management system (WMS) decides that our unicorn should take route A, the control system needs to activate the right diverter at the right moment, sometimes within a fraction of a second.
    7. Packing
      During packing, an employee or a robotic arm places the goods into the final box, prints the shipping label, and sometimes measures the dimensions and weight of the package. Safety mechanisms usually prevent robotic arms from leaving the controlled zone and harming nearby operators, but their compromise might inflict physical damage on the packages being processed.
    8. Shipment
      Finally, at the shipping stage, the parcel heads to the gates. Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) move containers and pallets across the warehouse, while the dock management system coordinates the truck that will carry the unicorn onward to a store, a sorting center, or straight to the customer. These robots are themselves a potential target for attackers. Attacks can be conducted against AGVs and AMRs to interfere with their operation, for example by affecting their navigation or preventing them from reaching their intended destination.

    All eight stages are coordinated by the WMS. It looks like a large web application, but in its role it’s closer to the operating system of the entire warehouse: it knows which goods exist, where they’re located, who or what should move them, and where they go next, linking employees, inventory levels, storage addresses, tasks, and orders. It also exchanges data externally: with ERP systems, transport platforms, marketplaces, dark stores, and external carriers.

    Surrounding the WMS is an even larger digital ecosystem: identity services, databases, APIs, printers, carrier systems, technicians’ laptops, video surveillance, access control, fire and gas alarms, power supply, and a building management system (BMS).

    That is why a single “warehouse network” doesn’t really exist. As we’ve seen, there are many overlapping technologies — IT infrastructure, OT systems, radio, cloud services, building security, and automation — layered on top of one another. A lone vulnerability scanner will never tell you whether a warehouse is secure.

    Hunting the unicorn

    Securing an environment such as a warehouse requires a comprehensive security assessment covering all of its interconnected systems. This involves identifying vulnerabilities across each system and understanding how weaknesses can be combined into attack paths that a real attacker could exploit. But a vulnerability only matters in proportion to what it exposes and who is able to reach it, so before looking at the weaknesses themselves, we need to understand what an attacker in this environment is actually after, because in a warehouse, the blast radius extends well beyond data. Everything we just traced — the systems, the devices, and the people operating them — exists to keep goods moving, and attacks in various parts along that chain can affect the business differently. The chart below shows what is at risk in the warehouse and where those risks land for the business. The segment on the left groups them under the three core security properties: integrity, availability, and confidentiality. The segment on the right maps each property to the business outcomes a failure can cause.

    Warehouse risks

    Warehouse risks

    The first area at risk is integrity. Inventory counts, locations, orders, and shipment data must stay in sync with the physical world. Corrupting a record silently desynchronizes the operation, and because the eight stages form a dependency chain, one falsified entry early on is inherited downstream, surfacing later as stockouts and mis-shipments. In its deliberate forms, this lets an attacker redirect goods, release stock, and perform other actions on behalf of the platform’s vendors under cover of legitimate records. Integrity is also a safety concern: when people or machines act on information that no longer reflects the physical environment, a data failure can become a physical incident. This is why integrity is the only property linked to all five outcomes, including harm to people.

    The second is availability, the warehouse’s ability to keep goods moving. Halting a conveyor, a sorter, label printing, or the WMS stops orders from shipping, and in a business built on speed, every hour of downtime means delayed deliveries and broken promises. Ransomware can bring the whole warehouse to a standstill. The impact shows up as operational disruption, lost revenue, and reputational damage.

    The third is confidentiality. Customer data, business records, and system credentials are valuable targets. Their exposure leads to legal liability and reputational damage, and stolen credentials often open the door to further attacks. Where the WMS connects to financial or ERP processes, the risk extends to theft of funds.
    Knowing what is at risk tells us what an attacker stands to gain; the next question is who is in a position to take it. Threat actors differ in the access they already hold. For example, an attacker may be authenticated and privileged, have limited physical or remote access, or have no legitimate access to the environment at all. Based on their relationship with the warehouse and their level of access, threat actors can generally be divided into two categories:

    1. External. An attacker with no legitimate relationship with the warehouse or the organization, operating without authorized access. External attackers can be divided into two categories based on their physical proximity to the environment:
      1. Remote. An attacker operating from outside the site’s physical area, reaching the environment through whatever the organization exposes or makes reachable over the internet, including web services, remote access infrastructure, and third-party connections used by suppliers, carriers, and integrators.
      2. Proximate. An attacker with no legitimate access who has nevertheless moved within physical range of the site’s locally reachable technologies. They may, for example, be in a parking lot, a neighboring unit, or a delivery bay. Their proximity may allow them to interact with wireless, radio, Bluetooth, or other short-range interfaces without possessing an account, badge, employment relationship, or other authorized access.
    2. Internal. An individual with legitimate access to the warehouse, its systems, or its information, who intentionally or unintentionally creates a security risk. This includes employees, administrators, operators, contractors, and temporary staff.

    Combining these threat actors and their access levels with the risks identified earlier, the vulnerabilities present in each system, the resulting attack surfaces, and the potential attack paths between them provides a threat model for the warehouse environment. This allows an organization to understand how different systems and people could be targeted, how individual weaknesses could be combined, and where security controls are most needed.

    Cornering the unicorn

    Knowing what is at risk and who might come after it still leaves the most practical question open: how they would actually get in, and how far they could go once they did. The diagram below sets out the warehouse’s attack surface: every point where an attacker can get in, organized by the access that reaching each point requires.

    Warehouse attack surface

    Warehouse attack surface

    On the left are the attacker categories discussed above, distinguished by where they stand and what access they already hold. Each reaches the environment through a different set of entry points, and the impact of a successful attack can land on several systems, from the WMS to OT systems such as robots, sorters, and physical access control system (PACS). The WMS is highlighted because it is the most significant of these: it coordinates the OT systems, so compromising it can affect the entire operation rather than a single process.

    Next, we will cover the initial access vectors, the points a threat actor can use to reach the warehouse environment. Following that, we turn to the impact on the logistics processes themselves, focusing on the WMS, since its compromise affects every logistics operation that depends on it.

    Initial attack vectors

    Internet-facing services

    Remote access through internet-facing services, third-party connections, and corporate networks gives an external attacker a foothold on the warehouse network infrastructure. It is a well-covered problem and not specific to this environment, so we note it here as an entry point but will not examine it further.

    Wi-Fi, Bluetooth, and other radio channels

    Wi-Fi connects handhelds, tablets, printers, cameras, and workstations. Bluetooth serves scanners, headsets, and positioning beacons. Zigbee and similar low-power protocols run lighting, sensors, and building automation, while LoRaWAN carries telemetry over long distances. Cellular provides a backup channel for kiosks and contractor equipment, and proprietary protocols control industrial panels and specialized machinery.

    Radio passes through windows, gates, roofing, and thin external panels, so a network built for receiving and processing goods may be reachable from the parking lot and, with a directional antenna, from considerably farther away.

    That reach matters in two ways. It gives an external attacker a place to start, as proximity alone is enough to reach the Wi-Fi and the low-power protocols, and from there to gain an initial foothold in the warehouse networks.

    However, radio does not only lead into the network. It also connects directly to the field devices and carries the live control traffic of warehouse operations, which makes availability a first-order concern. If hundreds of handhelds lose their connection at once, picking stops. If an AGV loses its control channel, the safe reaction may be to halt in place, and a stopped machine can block an entire route.

    Physical network

    The scale of the warehouse itself is part of the attack surface. A fulfillment warehouse can run up to tens of thousands of square meters, with racking climbing several stories and walkways threading through it. Structured cabling runs along that racking, cameras and access points are mounted on that, and wall jacks sit behind pallets that have not moved in months. Almost none of it is in anyone’s line of sight.

    That gives a person in the warehouse something a normal office does not: unsupervised access to live network infrastructure. Unplugging a ceiling camera leaves an active port. An unused jack behind a shelf leaves another. Neither requires tools, credentials, or any interaction with a system that logs anything. And since the camera or access point sits on the same segment as the rest of the site, the compromised port, not being isolated, opens a path to the network core.

    Barcode scanners

    Barcode scanners are everywhere in a warehouse. They are the main tool workers use to tell the system what is moving and where, supporting receiving, picking, packing, and inventory management. A typical setup has handheld scanners communicating with a base station, which is in turn connected to a workstation or another business system.

    This makes the scanner an entry point into the core network in two ways.

    First, through the barcode itself. Since the scanner acts as a keyboard to the connected host, a maliciously crafted barcode or QR code can inject keystrokes when scanned, potentially giving the attacker an initial foothold on the host without directly accessing the network.

    Second, through the scanner’s wireless link. During a customer engagement, with the provided test stand of such a device, we discovered that an attacker within range could inject keystrokes into the connected host and potentially achieve remote code execution without user interaction. This attack pattern was observed across multiple vendors, including, for example, Honeywell devices affected by CVE-2026-4272.

    In both cases, the result is the same: a foothold on a host inside the warehouse network. The scanner is simply the entry point; the significance of that foothold comes from the host’s trusted position within the operational environment and its connectivity to core systems.

    Portable data terminals

    Portable data terminals (PDTs) are essentially ordinary Android devices with an embedded barcode scanner, carried by workers to scan items; each scan is passed to a dedicated application that forwards it to a remote server over an API. To keep workers on task, the devices usually run in kiosk mode, locking the user to that single application and preventing interaction with the rest of the Android system.

    That lockdown is exactly what an attacker wants to break, and breaking it is what turns the terminal into an initial access vector: escaping the kiosk can expose the Wi-Fi password of the network the terminal is connected to, or allow malicious software to be installed that gives the attacker remote access to the network over the internet.

    Printers

    Warehouses contain different types of printers. Alongside the usual IT printers used for documents and paperwork, there are specialized thermal printers, small devices that use heated elements on the printhead to create labels, barcodes, and tags. As we mentioned earlier, these are mainly used to print the labels, usually containing barcodes, that are attached to goods during the putaway stage.

    Here we focus on mobile thermal printers: small, battery-powered devices that workers can carry or wear. They typically connect to the internal network over Wi-Fi to receive print commands, while also supporting Bluetooth for connecting to nearby devices. This combination makes them a potential entry point into the internal network.

    Some models, such as the Zebra ZQ630, support multiple interfaces and can run applications directly on the device. During a security assessment, we discovered that apps can be uploaded and executed over Bluetooth. By exploiting a pairing misconfiguration, an attacker within Bluetooth range could bypass pairing, upload malicious software, and gain an initial foothold through the printer.

    Impact on logistics processes (attacks on WMS)

    With a foothold in the network, an attacker can pivot to the logistics processes themselves: from the WMS through the warehouse infrastructure to the PLCs and the machinery they drive. As we saw earlier, all OT systems and devices are interconnected and the WMS sits at the head of that chain: it is the system of record every other process reads from and writes to, so compromising it reaches all of them at once. That, and its being specific to this environment, is why it is the focus here.

    Warehouse management systems (WMS) are the software platforms that optimize warehouse processes from the entry of goods to their exit. They provide real-time visibility into inventory and orchestrate end-to-end fulfillment by integrating with many warehouse subsystems and pieces of automation equipment, such as conveyors, PLCs, robotics, scanners, and voice systems. A typical WMS includes modules for receiving, inventory control, order management, labor management, shipping, and reporting. Major vendors include SAP EWM, Oracle Warehouse Management, Manhattan Active, Blue Yonder, Infor, Körber/HighJump, Microsoft, and TECSYS.

    To picture how central this is, think back to our unicorn. From the moment it was scanned at receiving, it stopped being only a physical box and became a record inside the WMS, a record the system then used to decide where it should be stored, when it should be picked, how it should be sorted, and where it should ship. Every device employed along the way — the PDT at receiving, the printer at putaway, the workstation at picking, the conveyor at sorting — was really just talking to the WMS. It is the thread that ties all eight stages together, which is exactly what makes it such a valuable target.

    Because modern WMS platforms are usually web-based and heavily interconnected, they inherit common web and API vulnerabilities alongside flaws specific to the WMS itself. A recent example is CVE-2026-70774 in Oracle Warehouse Management, part of Oracle E-Business Suite. Oracle rated it High and described it as an easily exploitable authorization bypass flaw in which a low-privileged attacker with network access can compromise the application, affecting integrity and availability by allowing unauthorized creation, deletion, or modification of critical data.

    A flaw like this may look like a typical web application issue, but its consequences in a warehouse are more serious, because the WMS is the system of record. If an attacker can modify its data, they can alter stock levels, bin locations, receiving records, picking tasks, or shipment information, silently corrupting the digital twin of the warehouse. And since the eight stages form a dependency chain, that corruption doesn’t stay contained: a falsified record at receiving is inherited by putaway, picking, and everything downstream. The physical box and its record diverge, and every later process trusts the record.

    The most direct version of this attack comes from an authorization bypass flaw like the Oracle vulnerability above. Here the threat actor can be an external attacker who has reached the application over the network, or an insider such as a low-privileged operator or storekeeper who already holds a legitimate account.

    The danger grows when such a flaw is chained with others. An external attacker who combines remote code execution with SQL injection could generate shipment orders to arbitrary addresses together with the documents needed for goods to physically leave the warehouse, fabricate waybills, acceptance or transfer records, and registers, while also exposing customer data such as names and delivery addresses. In effect, the goods are sent out with legitimate-looking paperwork.

    This is why ordinary web vulnerabilities carry considerable weight in a WMS. For an external attacker or an insider probing beyond their assigned role, broken access control could open a path into another warehouse zone, allow manipulation of other workers’ tasks, permit unauthorized exception operations, or expose administrative APIs. A server-side request forgery flaw could force the WMS server to reach resources an external attacker cannot touch directly, and if the network is flat or only formally segmented, that server may in turn reach internal management panels, cloud service interfaces, or systems sitting close to the operational network, turning a single web bug into a bridge between IT and OT.

    Attacks on a WMS don’t always target the application directly, though. As we saw while tracing the unicorn, the warehouse is full of connected devices that already hold trusted access. A handheld terminal carries authenticated WMS API access as a matter of course, and anyone who gains control of that terminal inherits it (by escaping the kiosk, for example). Depending on the device’s permissions, that might mean confirming picks that never occurred, falsifying receiving operations, adjusting stock counts, or marking orders complete — something warehouse employees themselves could use to avoid their assigned duties.

    Where the WMS is connected to control and automation systems, access to the WMS can extend into physical operations, including conveyors, sorting systems, lifts, doors, and pumps.

    PLCs translate WMS instructions and sensor inputs into physical actions. Compromising a system that controls them therefore creates an opportunity to manipulate those actions. The impact can be as simple as a sorting diverter sending boxes down the wrong route, a conveyor repeatedly entering an error state and creating a backlog, or a falsified sensor value causing a line to be treated as clear when it is full.

    However the WMS is compromised, the business consequence is the same: inventory corruption, unauthorized shipments, and operational disruption, each reaching the customer in the end as a late or missing delivery, the wrong item in the box, or a personal address exposed to a stranger.

    Securing the unicorn

    We followed our unicorn through web applications and databases, radio channels and PDTs, printers, conveyors, robots, and the loading zone. Because warehouse environments combine IT, OT, wireless, physical, and human components, security controls should be implemented as a single, coordinated security architecture rather than as isolated measures. Network segmentation should be a foundational control, with clear separation between corporate IT, warehouse OT, operational devices, management networks, and externally connected systems. Segmentation should also extend across facilities and customer environments so that each site and customer integration is isolated from the others, limiting the potential impact of a compromise. A zero-trust approach should reinforce this architecture by requiring explicit authentication and authorization for connections between systems, regardless of their network location, and by applying least-privilege access wherever possible. Regular penetration testing of the corporate IT environment, including domain services, identity infrastructure, and endpoints, should be incorporated into the warehouse security program.

    Physical and network access controls should be implemented together. Access to warehouse facilities, network rooms, communication cabinets, and operational areas should be restricted according to business need, with appropriate authentication, monitoring, and access logging. Network infrastructure should be hardened through controls such as switch port security, MAC-based access restrictions where appropriate, and IEEE 802.1X with EAP-TLS for strong device and user authentication. Network equipment should also be securely configured and regularly reviewed to ensure that unused interfaces, insecure management protocols, default credentials, and unnecessary services are disabled.

    Web applications and their supporting infrastructure should be governed through a secure software development lifecycle (SSDLC), including security requirements, secure coding practices, code and dependency review, and application security assessment before deployment and throughout the application’s lifecycle. Third-party applications and components should be continuously monitored and updated, with a defined vulnerability and patch management process. This should explicitly include warehouse management systems (WMS), warehouse portals, APIs, and other applications that provide operational or customer-facing functionality.

    Wireless technologies should be subject to dedicated security assessment and engineering. Radio-frequency surveys should be conducted to establish expected coverage, identify unauthorized or unexpected signals, validate the availability and protection of operational communications, and determine whether wireless deployments create unintended access paths into warehouse networks. Wireless security should be assessed not only from a connectivity perspective but also in terms of authentication, encryption, device provisioning, protocol exposure, interference resilience, and the potential impact of unauthorized devices.

    Warehouse operational devices should be incorporated into the organization’s endpoint security and vulnerability management processes. Devices should receive security updates and firmware patches on a defined schedule, with accelerated remediation for critical vulnerabilities. Security assessments should be performed periodically to identify weaknesses in device configuration, applications, exposed services, and authentication mechanisms.

    Personnel security should form an additional layer of the warehouse security architecture. In most industrial and ICS environments, getting inside means clearing background checks, holding relevant qualifications, or proving prior experience; the hiring process itself acts as a filter. A warehouse is different. Roles in sorting, packing, and courier work are hired quickly and in volume, and they typically require no prior expertise, certification, or specialized background at all. An ordinary person can walk in off the street, be hired within days, and immediately hold legitimate physical access to the facility. Therefore, a more restrictive and risk-based approach to hiring should be considered, particularly for roles that provide access to sensitive areas, operational systems, network infrastructure, or customer assets.

    Ultimately, all of these measures serve the same objective: protecting the continuity and reliability of the warehouse as a whole. This matters because real warehouses handle food, medicines, machine parts, and the millions of ordinary things people and companies depend on every day. Protecting their journey means seeing the warehouse as it has actually become: a single digital system with doors, motors, shelving, and radio signals, and with entirely real consequences when it fails. And if the security program works, the customer never knows any of this happened. The unicorn simply arrives at the scheduled time.

    If your warehouse operations rely on interconnected systems like these, we can help identify the gaps before an adversary does. Reach out to discuss a security assessment.

    Source: Securelist (Kaspersky GReAT) · 2026-10-20