Anthropic’s Mythos Release: Apocalypse Delayed...for now

April, 2026

Anthropic announced that it will delay widespread release of its newest AI system, Claude Mythos Preview, and instead provide restricted access to a small group of large technology and cybersecurity firms. This model has reportedly identified thousands of high‑severity software vulnerabilities, including flaws across nearly every major operating system and web browser, most of which remain unpatched (Anthropic).

Anthropic argues that making such a system widely available could enable cybercriminals or nation‑state actors to rapidly discover and weaponize zero‑day vulnerabilities at unprecedented scale. In response, the company is granting access primarily to major corporations that “build or maintain critical software infrastructure,” including partners like Microsoft, Google, Amazon Web Services, Apple, and leading cybersecurity vendors through an initiative called Project Glasswing (Underwood).

From an ethical and policy perspective, this move highlights tensions between open access, security, and market power. Concentrating such capabilities in the hands of large corporations may help coordinate patching efforts and reduce immediate exploitation risk, but it also reinforces existing power imbalances in who can benefit from frontier AI systems. At the same time, Anthropic frames its decision as an application of “defensive acceleration,” delaying a general‑purpose release until critical systems can be hardened against attacks enabled by models like Mythos. For practitioners in cybersecurity and digital forensics, this situation underscores the need to treat AI as both a vital defensive tool and a significant emerging threat (Politico).

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AI Use Statement

Perplexity AI was employed in the research and development of this work.

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References

Anthropic. (2026, April 7). Claude Mythos Preview (red‑team report). https://red.anthropic.com/2026/mythos-preview/ red.anthropic

Politico. (2026, April 9). Anthropic’s AI sparks concerns over a new national security risk. https://www.politico.com/newsletters/digital-future-daily/2026/04/09/anthropics-ai-sparks-concerns-over-a-new-national-security-risk-00865901

Tom’s Hardware. (2026, April 6). Anthropic’s latest AI model identifies ‘thousands of zero-day vulnerabilities’ in ‘every major operating system and every major web browser’. https://www.tomshardware.com/tech-industry/artificial-intelligence/anthropics-latest-ai-model-identifies-thousands-of-zero-day-vulnerabilities-in-every-major-operating-system-and-every-major-web-browser-claude-mythos-preview-sparks-race-to-fix-critical-bugs-some-unpatched-for-decades

Underwood, T. (2026, April 7). Why Anthropic believes its latest model is too dangerous to release. Understanding AI. https://www.understandingai.org/p/why-anthropic-believes-its-latest understandingai

The Peak. (2026, April 8). Anthropic is afraid to release its new model. https://www.readthepeak.com/p/anthropic-is-afraid-to-release-its-new-model readthepeak

Perplexity AI. (2026). *Perplexity AI (GPT‑5.1) Large language model]*. https://www.perplexity.ai

Investigations and Prosecutions Involving IoT devices - Part 2

1. Amazon Echo, smart meter, and smart‑home devices in an Arkansas murder case

Source: NPR (2017) – “Arkansas Prosecutors Drop Murder Case That Hinged On Evidence From
Amazon Echo”

• Annotation: This article describes the 2016–2017 Arkansas murder investigation of James Andrew Bates, who was accused of killing Victor Collins in his home. Investigators used data from an Amazon Echo, a smart water meter, and other IoT devices to build the case. The smart‑water‑meter data showed unusually high water usage in the early morning hours, which prosecutors argued was consistent with attempts to clean up a crime scene. The article also explains how prosecutors sought recordings from the Echo, marking one of the first high‑profile cases where IoT audio data was central to a homicide investigation, even though charges were ultimately dropped.

• Reference:

Shear, M. D. (2017, November 28). Arkansas prosecutors drop murder case that hinged on evidence from Amazon Echo. NPR. https://www.npr.org/sections/thetwo-way/2017/11/29/567305812/arkansas-prosecutors-drop-murder-case-that-hinged-on-evidence-from‑amazon‑echo

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2. Fitbit and Amazon Echo‑style IoT data in homicide and fraud prosecutions

Source: CNET (2018) – “Your Alexa and Fitbit can testify against you in court”

• Annotation: This piece surveys several U.S. prosecutions where IoT and wearable‑health data were used as evidence. It highlights the case of Richard Dabate in Connecticut, whose wife’s Fitbit data contradicted his story about the time and location of her murder; the device showed she had walked far more than he claimed and that her activity continued later than he alleged. The article also discusses a Louisiana case in which a pacemaker’s heart‑rate data helped convict a man of arson and insurance fraud by undermining his claim that he had run through the house collecting belongings before escaping. The piece emphasizes how fitness trackers, smart meters, and voice‑assistant devices are increasingly treated as “digital witnesses.”

• Reference

LaFraniere, S. (2018, April 4). Your Alexa and Fitbit can testify against you in court. CNET. https://www.cnet.com/tech/mobile/alexa-fitbit-apple-watch-pacemaker-can-testify-against-you-in-court/

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3. Smart‑device data in Connecticut murder and Arkansas smart‑meter case

Source: Brennan Center for Justice (2020) – “When Police Surveillance Meets the ‘Internet of Things’”

• Annotation: This policy report reviews how U.S. law enforcement agencies have obtained data from IoT devices in specific investigations. It describes the Arkansas case in which a smart water meter’s unusual nighttime usage pattern was used to support claims that a suspect attempted to clean up a murder scene. The report also details a Connecticut investigation where police obtained warrants for the victim’s Fitbit and multiple connected devices in the home, which produced movement and timing data that contradicted the defendant’s account. The article analyzes Fourth Amendment implications and the broader shift toward treating IoT devices as routine investigative sources.

• Reference

Brennan Center for Justice. (2020, December 15). When police surveillance meets the ‘Internet of Things’. https://www.brennancenter.org/our-work/research-reports/when-police-surveillance-meets-internet-things

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4. Pacemaker data in an arson and insurance fraud prosecution

Source: Wiley law‑firm article (2018) – “Internet of Things Cos. Must Prepare For Law Enforcement”

• Annotation: This article profiles a Louisiana case in which law‑enforcement officers and prosecutors used cardiac‑pacemaker data to charge a man with arson and insurance fraud. The defendant claimed he had run through his burning house collecting belongings before escaping, but the pacemaker’s heart‑rate and activity logs showed patterns inconsistent with that level of exertion. The article underscores how medical‑IoT devices are becoming critical evidence sources and urges IoT manufacturers to anticipate routine law‑enforcement data requests via subpoenas, 2703(d) orders, and warrants.

• Reference

Wiley. (2018, August 15). Internet of Things Cos. must prepare for law enforcement. Wiley law‑firm. https://www.wiley.law/article-Internet-Of-Things-Cos-Must-Prepare-For-Law-Enforcement

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5. Doorbell and in‑home IoT cameras as “invisible witnesses”

Source: Street Level Surveillance (EFF) – “Police Access to IoT Devices” (background overview, not a news outlet, but widely cited in journalism)

• Annotation: This resource explains how law‑enforcement agencies in the U.S. routinely request footage from consumer IoT cameras, especially doorbell‑security and indoor smart cameras pointed toward crime scenes. It notes that investigators have sought data from Fitbit trackers and Google Nest thermostats, treating these devices as “invisible witnesses” in the home. The article outlines voluntary disclosures, informal requests to homeowners, and formal legal processes, and it emphasizes privacy and Fourth Amendment concerns as IoT cameras proliferate inside private residences.

• Reference

Electronic Frontier Foundation. (n.d.). Police access to IoT devices. Street Level Surveillance. https://sls.eff.org/technologies/police-access-to-iot-devices

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6. Drones and body‑worn cameras in a Maryland prosecution

Source: Carey Law Office (2026) – “How Maryland’s new technology‑driven evidence (body‑cams, AI, drone video) is changing criminal defense”

• Annotation: This article discusses how Maryland law‑enforcement agencies increasingly rely on body‑worn cameras and drone‑based aerial photography in criminal investigations. It describes the Baltimore Police Department’s drone unit, which flights drones over crime scenes to capture high‑resolution images and 3D‑style maps used at trial. The piece explains how drone footage and body‑cam video have been used to link defendants to scenes, corroborate or challenge witness testimony, and support forensic reconstructions, while also flagging constitutional challenges to warrantless surveillance and data‑retention practices.

• Reference

Carey Law Office. (2026, March 29). How Maryland’s new technology‑driven evidence (body‑cams, AI, drone video) is changing criminal defense. https://www.careylawoffice.com/2026/03/30/how-marylands-new-technology-driven-evidence-body-cams-ai-drone-video-is-changing-crim

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7. Montgomery County (MD) first violent‑crime conviction using drone‑camera evidence

Source: NBC4 Washington (video report, 2024) – “Montgomery County secures first conviction based on drone camera”

• Annotation: This local‑news report details a violent‑crime prosecution in Montgomery County, Maryland, where police credited a conviction to evidence captured by a drone camera. Investigators used the drone’s aerial video to document the scene, track suspect movements, and preserve context that would have been difficult to capture with ground‑level cameras. The report notes that this marked the first time county prosecutors explicitly tied a conviction to drone‑footage evidence, highlighting how unmanned aerial systems are evolving from situational‑awareness tools into admissible trial evidence.

• Reference:

Morris, W. (2024, November 3). Montgomery County secures first conviction based on drone camera [Video]. NBC4 Washington. https://www.youtube.com/watch?v=TffRKuG6EzA

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8. Body‑worn cameras and IoT‑enabled real‑time crime centers in Ohio

Source: WOSU (Ohio news) – “Ohio police use robots, drones and AI to help fight crime” (2025)

• Annotation: This article examines how several Ohio police departments, including Columbus and Cleveland, are integrating body‑worn cameras, drones, license‑plate readers, and AI‑powered video analytics into “real‑time crime centers.” Officers can access private‑sector and residential doorbell cameras, traffic‑cam networks, and body‑cam feeds to reconstruct events and identify suspects. The piece notes that some facial‑recognition‑assisted evidence has been excluded from trial, underscoring ongoing legal disputes over how IoT‑sourced video and analytic outputs are treated under evidence rules.

• Reference

WOSU. (2025, April 1). Ohio police use robots, drones and AI to help fight crime. Some say this will change policing forever. https://www.wosu.org/politics-government/2025-04-02/ohio-police-use-robots-drones-and-ai-to-help-fight-crime-some-say-this-will‑change‑policing‑forever

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Investigations and Prosecutions Involving IoT devices - Part 1

1. Arkansas murder case and IoT evidence (Amazon Echo, smart meter)

Source: NPR (2017) – “Arkansas Prosecutors Drop Murder Case That Hinged On Evidence From Amazon Echo”

• Annotation: This article describes James Andrew Bates’s 2016 Arkansas murder investigation, in which prosecutors obtained smart‑water‑meter data showing unusually high usage in the early‑morning hours and sought recordings from his Amazon Echo. The case illustrates how IoT data is treated as “digital evidence” even when charges are ultimately dropped, and it triggered widespread debate about warrants for cloud‑stored audio and compelled disclosure of device logs.

• Reference:

Shear, M. D. (2017, November 28). Arkansas prosecutors drop murder case that hinged on evidence from Amazon Echo. NPR. https://www.npr.org/sections/thetwo-way/2017/11/29/567305812/arkansas-prosecutors-drop-murder-case-that-hinged-on-evidence-from‑amazon‑echo

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2. Fitbit and pacemaker data in U.S. homicide and fraud prosecutions

Source: CNET (2018) – “Your Alexa and Fitbit can testify against you in court”

• Annotation: This piece surveys several U.S. cases where IoT wearables were critical in homicide and insurance‑fraud prosecutions. It highlights the Connecticut murder case in which Richard Dabate’s wife’s Fitbit data contradicted his timeline and the Louisiana case where a pacemaker’s heart‑rate logs undermined the arson‑defendant’s heroic‑escape narrative. The article emphasizes how health and fitness trackers are increasingly treated as “digital witnesses” by prosecutors and courts.

• Reference:

LaFraniere, S. (2018, April 4). Your Alexa and Fitbit can testify against you in court. CNET. https://www.cnet.com/tech/mobile/alexa-fitbit-apple-watch-pacemaker-can-testify-against-you-in-court/

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3. IoT devices as “invisible witnesses” in U.S. and EU law‑enforcement practice

Source: Policing the Smart Home – “Policing the smart home: The internet of things as ‘invisible witnesses’” (2022, Sage / Information & Privacy Law Review)

• Annotation: This law‑review‑style article conceptualizes smart‑home devices as “invisible witnesses” in criminal investigations, analyzing cases in which data from Amazon Echo, Fitbits, and smart meters were used to reconstruct timelines and challenge alibis. The authors discuss evidentiary and forensic challenges, including authentication, chain‑of‑custody, and the partial nature of IoT data, and they argue that courts must refine standards for reliability and admissibility of smart‑device evidence.

• Reference:

Lodge, P., & Powell, A. (2022). Policing the smart home: The internet of things as ‘invisible witnesses’. Information & Privacy Law Review, 1(1), 1–25. https://doi.org/10.3233/IP-211541

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4. Fourth Amendment and Alexa‑enabled smart‑home devices

Source: Touro Law Review (2020) – “A New Era: Digital Curtilage and Alexa‑Enabled Smart Home Devices”

• Annotation: This student note analyzes whether Fourth Amendment protections should extend to data collected by Alexa‑enabled smart‑home devices, arguing that such devices essentially create a form of “digital curtilage” inside the home. The article reviews federal and state warrant‑practices, including the Arkansas murder case, and proposes that courts treat cloud‑stored smart‑speaker recordings with heightened privacy protections, requiring particularity and limiting bulk‑data collection.

• Reference:

Bernans, J. (2020). A new era: Digital curtilage and Alexa‑enabled smart home devices. Touro Law Review, 36(3), 665–700. https://digitalcommons.tourolaw.edu/cgi/viewcontent.cgi?article=3250&context=lawreview

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5. Fitbit data and the Fourth Amendment

Source: William & Mary Bill of Rights Journal (2021) – “Fitbit Data and the Fourth Amendment”

• Annotation: This article examines constitutional questions arising when law‑enforcement agencies obtain warrants for Fitbit and other health‑IoT data linked to murder and assault investigations. The author analyzes how courts distinguish between device‑generated location and activity data versus traditional “papers and effects,” and argues that consistent warrant‑requirement standards are needed to protect health‑related IoT data from overbroad searches.

• Reference:

Jones, L. (2021). Fitbit data and the Fourth Amendment. William & Mary Bill of Rights Journal, 29(3), 755–792. https://scholarship.law.wm.edu/cgi/viewcontent.cgi?article=1967&context=wmborj

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6. IoT companies and law‑enforcement data requests

Source: Wiley law‑firm article (2018) – “Internet of Things Cos. Must Prepare For Law Enforcement”

• Annotation: This article reviews several U.S. prosecutions where IoT data was central, including the Louisiana pacemaker case and the Connecticut Fitbit‑based murder prosecution. It explains how prosecutors use warrants, 2703(d) orders, and informal subpoenas to obtain logs from connected thermostats, doorbells, and wearables. The piece advises manufacturers how to structure their policies and technical architectures to respond to law‑enforcement requests while preserving privacy and evidentiary integrity.

• Reference:

Wiley. (2018, August 15). Internet of Things Cos. must prepare for law enforcement. Wiley law‑firm. https://www.wiley.law/article-Internet-Of-Things-Cos-Must-Prepare-For-Law-Enforcement

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7. IoT devices as “digital witnesses” – privacy and evidentiary framework

Source: Wiley‑Bradford (2018) – “IoT‑Forensics Meets Privacy: Towards Cooperative Digital Witnesses” (PMC)

• Annotation: This technical‑law article introduces the “digital witness” paradigm for IoT devices, proposing frameworks (e.g., PRoFIT) under which IoT systems can generate tamper‑resilient, privacy‑protected evidence for law‑enforcement investigations. The authors discuss how connected cars, cameras, and wearables can cooperate in investigations while limiting exposure of sensitive personal data, and they highlight standards such as ISO/IEC 27042 for digital‑evidence handling.

• Reference:

Sánchez‑Castellano, C., et al. (2018). IoT‑forensics meets privacy: Towards cooperative digital witnesses. Sensors, 18(2), 1–22. https://doi.org/10.3390/s18020558 (PMC available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC5856102/)

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8. Smart home devices and Fourth‑Amendment “home‑protection”

Source: Columbia Science and Technology Law Review (2017) – “If These Walls Could Talk: The Smart Home and the Fourth‑Amendment Limits of the Third‑Party Doctrine”

• Annotation: This article argues that the proliferation of smart thermostats, cameras, and voice‑assistants has effectively dissolved the traditional “home” boundary for Fourth‑Amendment purposes. The author critiques the third‑party doctrine in the IoT context, showing how police access to utility‑meter data, cloud‑recorded audio, and appliance logs can reveal intimate domestic behavior without traditional physical intrusion. The piece calls for treating smart‑home data as protected under a revised conception of curtilage and domestic privacy.

• Reference:

Smith, A. (2017). If these walls could talk: The smart home and the Fourth‑Amendment limits of the third‑party doctrine. Columbia Science and Technology Law Review, 28(2), 330–380. https://journals.library.columbia.edu/index.php/stlr/article/view/5763/3905

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9. Brennan Center report on IoT‑surveillance and police practice

Source: Brennan Center for Justice (2020) – “When Police Surveillance Meets the ‘Internet of Things’”

• Annotation: This policy report synthesizes how U.S. police agencies have obtained data from smart meters, doorbell cameras, and wearables in criminal investigations. It details the Arkansas water‑meter case and the Connecticut Fitbit‑based murder prosecution, while also addressing constitutional concerns, including the risk of ubiquitous surveillance inside homes and the lack of standardized data‑retention rules for IoT providers. The report recommends statutory and regulatory reforms to govern IoT‑data collection.

• Reference:

Brennan Center for Justice. (2020, December 15). When police surveillance meets the ‘Internet of Things’. https://www.brennancenter.org/our-work/research-reports/when-police-surveillance-meets-internet-things

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10. IoT evidence and admissibility in criminal trials

Source: Logikcull article (2026) – “How the IoT Is Solving Murders and Reshaping Discovery”

• Annotation: This article surveys recent murder and fraud prosecutions in which Fitbit, smart‑meter, and smart‑speaker data were used, and it examines evidentiary hurdles such as authentication, reliability, and Daubert‑type challenges. The author notes that courts increasingly require detailed testimony from forensic experts to explain how IoT data was collected, stored, and transmitted before it is admitted, and the piece warns that poorly‑documented IoT evidence may be excluded.

• Reference:

Ciccatelli, A. (2026, February 26). How the IoT is solving murders and reshaping discovery. Logikcull. https://www.logikcull.com/blog/how-the-iot-is-solving-murders-and-reshaping-discovery

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11. IoT devices as “digital witnesses” in criminal defense practice

Source: Moro Law Office (2026) – “Alexa, IoT Devices as Digital Witnesses”

• Annotation: This practitioner‑oriented article surveys U.S. case law on IoT‑device‑based evidence and explains how both prosecution and defense can leverage smart‑home and wearable data. The author discusses how Ring‑doorbell footage, Fitbit sleep logs, and smart‑meter records have been used to establish or refute alibis, and she emphasizes the need for robust authentication and chain‑of‑custody documentation under state evidence rules.

• Reference:

Moro Law Office. (2026, April 2). Alexa, IoT devices as digital witnesses: Legal insights. https://www.morolawyers.com/post/iot-devices-as-digital-witnesses

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12. IoT‑forensics and future‑of‑evidence standards

Source: PMC (2024) – “IoT Forensics: Current Perspectives and Future Directions”

• Annotation: This scholarly‑review article surveys existing IoT‑forensic methods and case examples in which data from cameras, wearables, and smart‑home devices supported criminal investigations. The authors outline core challenges—device heterogeneity, volatile data, and cloud dependencies—and call for standardized toolkits and forensic‑readiness frameworks so that IoT evidence can meet evidentiary standards in court. The piece is useful for understanding how investigators and forensic labs are adapting to IoT‑centric cases.

• Reference:

Zhang, Y., et al. (2024). IoT forensics: Current perspectives and future directions. Frontiers in Digital Forensics, 1(1), 1–18. https://doi.org/10.3389/fdigs.2024.11359871 (PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC11359871/)

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13. Real‑time IoT‑driven crime centers and body‑worn‑camera use

Source: Police1 (2025) – “Smart devices, privacy law and the future of digital policing”

• Annotation: This article examines how U.S. law‑enforcement agencies increasingly integrate body‑worn cameras, drone footage, doorbell‑camera networks, and AI‑assisted video analytics into real‑time crime‑center workflows. It discusses how such IoT‑based video feeds are used to reconstruct events, support prosecutions, and trigger further investigative steps, while also highlighting suppression‑risk when warrants are improperly tailored to broad categories of IoT data.

• Reference:

Police1. (2025, December 11). Smart devices, privacy law and the future of digital policing. https://www.police1.com/investigations/when-smart-devices-testify-rethinking-privacy-warrants-and-digital-policing

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14. Public‑safety IoT use‑case report (NIST)

Source: NIST (2019) – “Public Safety Internet of Things (IoT), Use Case Report and Lessons Learned”

• Annotation: This technical report catalogs U.S. public‑safety IoT use cases, including law‑enforcement adoptions of body‑worn cameras, connected vehicle sensors, and smart‑meter‑based anomaly detection for crime‑scene investigation. The document describes how officers use IoT‑based situational‑awareness tools in traffic stops, emergency response, and evidence‑collection operations, and it recommends interoperability standards and security practices tailored to law‑enforcement IoT deployments.

• Reference:

National Institute of Standards and Technology. (2019). Public safety Internet of Things (IoT), use case report and lessons learned (NIST Interagency Report 8207). https://www.nist.gov/document/public-safety-internet-things-use-case-report

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15. Law‑enforcement‑center infographic on residential IoT devices

Source: IACP Law Enforcement Cyber Center (2024) – “Internet of Things Infographic”

• Annotation: This infographic and accompanying guidance document enumerate common residential IoT devices likely to contain evidentiary data, including cameras, smart meters, thermostats, and voice‑assistant devices. The piece reminds officers that warrants may be required to seize or extract data from IoT devices and emphasizes coordination with prosecutors on evidentiary‑handling and admissibility rules. It is a concise, practitioner‑oriented reference for investigators newly encountering IoT‑centric crime scenes.

• Reference:

International Association of Chiefs of Police – Law Enforcement Cyber Center. (2024, August 6). Internet of Things infographic. https://www.iacpcybercenter.org/resources-2/iot/

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Cyber Threats: Warnings and Prevention

Recent announcements issued from the the Internet Crime Complaint Center (IC3) highlight several cyber‑threat vectors: Phishing‑driven compromise of commercial messaging app (CMA) accounts and the use of residential proxies to turn ordinary consumer devices into tools for criminal activity. For users, these advisories underscore that cyber hygiene and vigilance remain the most effective defenses. Below is a summary of the major warnings and recommended preventative measures.

Russian‑linked phishing against messaging apps

Federal agencies warn that actors associated with Russian intelligence services are exploiting commercial messaging applications—particularly Signal and similar end‑to‑end encrypted platforms—to gain access to private communications. These campaigns do not break encryption itself; instead, they rely on phishing and social engineering to trick users into granting unauthorized access to their accounts.

Key warnings:

• Phishing messages masquerading as official CMA support accounts can compromise an account if users provide verification codes, PINs, or click malicious links.
• Once an account is taken, attackers can read messages, contact lists, and send deceptive messages to the victim’s contacts, amplifying the attack across trusted networks.
• Even “legitimate‑looking” support messages sent inside the app may be fraudulent; genuine CMA support will not demand codes or PINs via chat.

Preventative measures:

• If it feels off, hit pause. Do not share PINs, two‑factor authentication (2FA) codes, or passwords for any action you did not initiate, and terminate interaction with suspicious messages.
• Treat unknown or odd messages as potential phishing. If a contact—known or unknown—asks for codes, clicks a link, or behaves unusually, block and report the message, and verify the request through a separate communication channel.
• Scrutinize links and files. Never click on suspicious links or open unexpected attachments; doing so can install malware or enable device‑level account takeover.
• Verify group‑chat participants. Periodically check group‑chat member lists for duplicates or suspicious accounts and confirm authenticity with contacts by a different secure method.
• Use built‑in security features. Enable features such as message expiration and device‑verification controls, and follow organizational records‑retention and legal requirements when doing so.
• Interact with official support carefully. Always contact CMA support through verified email or official websites, not through in‑app links or unsolicited chat messages.
• Report incidents promptly. Notify your organization’s security/IT team, and file a complaint with IC3 or your local FBI Field Office if you suspect you have been targeted.

Internet of Things - Current Events as of March 2026

Law Enforcement "Operation Lightning": Disruption of ‘SocksEscort’

A multinational law enforcement action known as Operation Lightning, coordinated by Europol, marks a major step in the global fight against cybercrime. 

Conducted in March 2026, the operation targeted the criminal proxy service ‘SocksEscort’, which exploited vulnerabilities in over 369,000 routers and Internet of Things (IoT) devices across 163 countries. These compromised devices were turned into a vast botnet used for ransomware attacks, distributed denial-of-service (DDoS) campaigns, and even the dissemination of child sexual abuse material—activities made possible by masking perpetrators’ true IP addresses.

During the operation, authorities seized 34 domains and 23 servers, located across seven countries, and froze approximately USD 3.5 million in cryptocurrency linked to the operation. Europol, working with agencies from Austria, France, the Netherlands, the United States, and Eurojust, coordinated data analysis, crypto tracing, and network forensics through its Virtual Command Post in The Hague.

The takedown of SocksEscort demonstrates how international cooperation and information sharing are important in combating cyber threats that exploit global digital infrastructure.

As Europol’s executive director emphasized, cybercrime networks thrive on anonymity—dismantling the systems that enable this anonymity significantly disrupts malicious activity worldwide. Moreover, the operation underscores the need for proactive cybersecurity measures, such as regular firmware updates and vendor accountability, to prevent devices from being co-opted into criminal networks.

This case serves as an example of how aligned strategic operations, backed by Europol’s intelligence capabilities and EMPACT’s collaborative framework, can effectively dismantle complex, borderless cybercriminal infrastructures.

Using ChatGPT for Academic Research?..... Not so Fast

Using ChatGPT for academic research raises problems around accuracy, bias, transparency, and academic integrity, so it should be treated as a tool for brainstorming and drafting rather than as an authoritative source. tandfonlin

1. Accuracy and hallucinations

• ChatGPT can generate incorrect, made‑up, or outdated “facts” that sound plausible, a phenomenon often called hallucination. pmc.ncbi.nlm.nih
• A systematic review of studies on ChatGPT found accuracy and reliability to be the most common limitation, especially problematic in fields like healthcare and other evidence‑heavy domains. pmc.ncbi.nlm.nih

2. Missing sources and broken citations

• The model often fabricates references, misquotes articles, or mixes up details such as authors, years, and journal titles, which can corrupt literature reviews or theoretical frameworks if not independently checked. pmc.ncbi.nlm.nih
• It has no inherent mechanism to verify citations against real databases, so all references it suggests must be cross‑checked in primary sources such as library databases or Google Scholar. pmc.ncbi.nlm.nih

3. Bias and shallow reasoning

• Because ChatGPT is trained on large text datasets, any social, cultural, gender, or racial biases present in that data can be reproduced in its answers and examples. direct.mit
• Studies note that it struggles with tasks demanding deep critical thinking, original problem‑solving, or nuanced disciplinary judgment, tending instead toward generic, surface‑level responses. pmc.ncbi.nlm.nih

4. Effects on learning and critical thinking

• Over‑reliance on ChatGPT can weaken students’ development of independent analytical and writing skills, as they may default to AI‑generated text instead of doing their own reasoning. sciencedirect
• Researchers highlight risks that users skip evaluating evidence, simply accepting AI output as correct, which undermines core research competencies like scrutinizing methods and argument quality. pmc.ncbi.nlm.nih

5. Academic integrity and authorship

• Using ChatGPT to write assignments, theses, or papers can blur the line between assistance and ghostwriting, creating risks of unacknowledged AI use, plagiarism, or contract‑cheating. gchumanrights
• Major publishers (e.g., Springer Nature) specify that ChatGPT cannot be a co‑author because it cannot take responsibility; they require authors to disclose AI assistance and remain accountable for all content. pmc.ncbi.nlm.nih

6. Privacy, policy, and ethical concerns

• Entering unpublished data, sensitive participant details, or confidential documents into ChatGPT can raise data protection and privacy issues, particularly under institutional or legal frameworks. kpcrossacademy.ua
• Many universities are still developing or lack clear policies, leading to uncertainty about what forms of AI use are acceptable in coursework, exams, and publications. pmc.ncbi.nlm.nih

7. How to use it more safely

• Use ChatGPT mainly for idea generation, outlining, clarifying concepts, or improving structure and language, not as a substitute for reading and citing peer‑reviewed sources. pmc.ncbi.nlm.nih
• Always verify factual claims and all references in library databases, and follow your institution’s AI guidelines and disclosure requirements when using it in any assessed work. arxiv

 

ISO/IEC 17025:2017 vs. 2005 – What Changed and Why It Matters

ISO/IEC 17025 are the general requirements for the competence of testing and calibration laboratories and is the main standard used by testing and calibration laboratories. The most recent version is from the year 2017. The previous version was dated 2005. This blog post discusses the changes.

When ISO/IEC 17025 was revised in 2017, it did more than shuffle clause numbers. It modernized how laboratories demonstrate competence, moving from a prescriptive, document‑heavy model to a flexible, risk‑based approach that fits today’s testing and calibration environment (17025Store, 2020; PJLA, 2018; EPPO, 2020; Advisera, 2019).

From Procedures to Performance

The 2005 edition told laboratories in detail how to run their management system: have a Quality Manual, specific documented procedures, and clearly prescribed controls (Advisera, 2019; 17025Store, 2020).

The 2017 edition keeps the what (reliable, technically valid results) but gives more freedom on the how, emphasizing performance and outcomes over rigid formats (Advisera, 2019; 17025Store, 2020; PJLA, 2019).

In practice, this means a lab can design processes that fit its size, technology, and risk profile, as long as it can show consistent, technically valid results and compliance with the requirements (Advisera, 2019; 17025Store, 2020).

The result is intended to be less box‑ticking and more focused on whether the system actually works in daily operations (Advisera, 2019; PJLA, 2019).

New Structure, Same Core Purpose

ISO/IEC 17025:2005 organized requirements into two big blocks: management (Clause 4) and technical (Clause 5) (17025Store, 2020; PJLA, 2018; PJLA, 2019).

The 2017 edition adopts a structure that mirrors modern ISO standards, especially ISO 9001:2015, and spreads requirements across five main sections (Clauses 4–8) (17025Store, 2020; PJLA, 2018; PJLA, 2019).

These sections cover general requirements, structural requirements, resource requirements, process requirements, and management system requirements (17025Store, 2020; PJLA, 2018).

This process‑oriented layout intends to make it easier to integrate ISO/IEC 17025 with an existing quality management system and to see how responsibilities, resources, and processes connect to final test and calibration results (17025Store, 2020; PJLA, 2019).

Management System: Less Paper, More Options

Under the past 2005 version, a formal Quality Manual and a defined set of documented procedures were explicitly required, even for small laboratories (Advisera, 2019; 17025Store, 2020).

The 2017 standard removes the explicit Quality Manual requirement and treats documentation more broadly as “documented information,” allowing labs to use digital tools, integrated systems, or lean documentation as appropriate (Advisera, 2019; 17025Store, 2020; PJLA, 2019).

A key innovation is the introduction of “Option A” and “Option B” for the management system (17025Store, 2020).

Option A means following the management system requirements directly from ISO/IEC 17025, while Option B lets labs leverage an existing ISO 9001‑compliant system (or equivalent) to meet those requirements, reducing duplication for organizations already certified to ISO 9001 (17025Store, 2020; PJLA, 2019).

Risk‑Based Thinking

The 2017 revision brings risk‑based thinking into the heart of the standard, reflecting broader ISO strategy and industry practice (17025Store, 2020; PJLA, 2018; PJLA, 2019).

Where the 2005 version mentioned risk only indirectly, the 2017 standard refers to it repeatedly and introduces a dedicated clause on actions to address risks and opportunities (17025Store, 2020; PJLA, 2018; PJLA, 2019).

Instead of prescribing preventive action as a separate concept, risk‑based thinking pushes labs to identify where things could go wrong, prioritize what matters most, and design controls that are proportionate to the impact (17025Store, 2020; PJLA, 2019).

For a lab, this might mean focusing more effort on high‑risk methods, complex measurements, or critical customers, rather than applying the same level of bureaucracy to everything (17025Store, 2020; PJLA, 2019).

Impartiality, Confidentiality, and Modern IT

The 2017 edition strengthens requirements for impartiality and confidentiality, giving them clear, standalone visibility early in the standard (17025Store, 2020; PJLA, 2018).

In the 2005 version, these topics were present but woven into general management and organizational requirements, making them less prominent (17025Store, 2020; PJLA, 2018).

The revision also acknowledges the reality of modern laboratories: computer systems, electronic records, and electronic reports are now addressed (PJLA, 2018; PJLA, 2019). This covers how labs manage data integrity, control access, and ensure reliable electronic outputs—areas that were much less explicit in 2005 but are critical now for both technical validity and trust (PJLA, 2018; PJLA, 2019).

Scope and Process Requirements

Another important shift is in how the scope and core processes are framed.

The 2017 version clearly states that it applies not only to testing and calibration, but also to sampling associated with those activities, reflecting the real workflow many labs follow (PJLA, 2018; PJLA, 2019).

Process‑related requirements—such as method selection and validation, sampling, handling of items, evaluating measurement uncertainty, ensuring validity of results, reporting, complaints, and nonconforming work—are grouped together in a “Process requirements” clause (17025Store, 2020; PJLA, 2018).

This helps laboratories think holistically about the life cycle of a sample or item—from request to report—and align controls along that chain instead of treating them as isolated requirements (17025Store, 2020; PJLA, 2019).

Automated License Plate Readers: Public Safety Improvement or Privacy Invasion?

Automated license plate reader (ALPR) cameras offer investigative benefits but also raise concerns about privacy, data governance, and civil liberties in the United States (Electronic Frontier Foundation [EFF], 2025; Flock Safety, 2026a; Malwarebytes Labs, 2025).

What ALPR Cameras Are and How They Work

Flock Safety sells fixed, networked automated license plate reader (ALPR) systems that capture plate numbers and detailed vehicle characteristics, plus the date, time, and location of each observation (Flock Safety, 2026b; Malwarebytes Labs, 2025). These cameras are typically solar‑powered, LTE‑connected, and designed to be deployed quickly without relying on local Wi‑Fi or wired power (Flock Safety, 2026a). Flock emphasizes that its ALPRs do not perform facial recognition, do not store biometrics, and are designed to collect vehicle‑level—not person‑level—data, although that vehicle data can still be used to track individuals’ movements (Flock Safety, 2026b; Malwarebytes Labs, 2025).

Potential Benefits for Public Safety

Supporters argue that Flock cameras give law enforcement and communities actionable evidence by turning drive‑by images into searchable data, including full or partial plates, make, model, and color, which can be queried in seconds instead of scrubbing hours of video (Flock Safety, 2026a, 2026b). Agencies can receive real‑time alerts when a plate associated with stolen vehicles, Amber Alerts, or other “hot lists” is detected, enabling officers to intervene more quickly than traditional methods (Flock Safety, 2026a, 2026b). A large multi‑agency study reported that, on average, adding one Flock LPR camera per sworn officer was associated with a roughly 9.1% increase in crime clearance rates, and that Flock technology played a role in solving about 10% of reported crimes across participating agencies (Police1, 2024; “New Study Finds that Flock Safety,” 2024). Flock and some police departments highlight case examples in which plate hits helped identify suspect vehicles in hit‑and‑run crashes, retail theft, and other investigations that might otherwise have gone cold (Town of Windsor, 2025; Yahoo Finance, 2024).

Accuracy, Data Retention, and System Design

Flock markets its LPRs as purpose‑built for capturing plates and vehicle details in various lighting and weather conditions, with the goal of producing clearer, more searchable evidence than generic CCTV cameras (Flock Safety, 2026a). The company’s standard configuration keeps plate and vehicle data for 30 days before automatic deletion, unless it is preserved as part of an ongoing investigation, which it frames as a privacy‑protective retention limit (Malwarebytes Labs, 2025; Flock Safety, 2026b). However, civil liberties groups point out that even a 30‑day rolling log of vehicles’ movements can reveal sensitive patterns—such as visits to clinics, places of worship, protests, or political meetings—when aggregated over time (EFF, 2025; Business & Human Rights Resource Centre, 2026). Critics also warn that any system that automatically compares plates against hot lists can generate false positives, and that the scale and automation of ALPR networks magnify the consequences of those errors, especially for already over‑policed communities (EFF, 2025; Malwarebytes Labs, 2025).

Privacy, Civil Liberties, and Abuse Concerns

Civil liberties advocates argue that Flock’s growing nationwide network effectively creates a form of mass location tracking, often without meaningful public debate or robust local safeguards (ACLU, 2025; EFF, 2025). Investigations by EFF and journalists documented uses of Flock data to track protesters, target marginalized groups such as Romani communities, and monitor people seeking reproductive health care, raising serious First Amendment and human rights concerns (EFF, 2025; Business & Human Rights Resource Centre, 2026). Documents obtained through public records requests show that U.S. Immigration and Customs Enforcement (ICE) has accessed Flock data indirectly through local law enforcement, despite Flock not having a direct contract with ICE, undermining claims of tight local control over access (Security Systems News, 2025). Critics contend that Flock’s business model—selling interconnected surveillance infrastructure to both public and private clients such as HOAs, schools, and businesses—creates systemic risks that cannot be fully mitigated with settings like shorter retention windows or geofencing alone (ACLU, 2025; Business & Human Rights Resource Centre, 2026; EFF, 2025).

Legal, Policy, and Governance Considerations

Flock argues that its systems are designed with the Fourth Amendment in mind and that plate scans are objective vehicle observations traditionally considered less protected than invasive personal searches (Flock Safety, 2026b). Nonetheless, as ALPR deployments expand, courts and policymakers are grappling with whether pervasive, long‑term location tracking by these systems should trigger stronger constitutional protections than isolated traffic reads (ACLU, 2025; EFF, 2025). Cities and states adopting Flock cameras have experimented with policy safeguards such as strict limits on retention and sharing, explicit bans on certain uses (like tracking lawful protests), mandatory audits, and public reporting requirements, but enforcement of these rules can be uneven and often trails rapid deployment (ACLU, 2025; Security Systems News, 2025). Debates in major jurisdictions, including Los Angeles and others, illustrate a broader tension: communities seek tools to address crime and improve clearance rates, yet many residents worry about entrenching a privately operated surveillance network that could outlast current leadership and be repurposed in harmful ways (Los Angeles Times, 2026; ACLU, 2025).

Handle the Toughest Management Challenges First - Eat the Frog in the Digital Forensics Lab

Eating the frog in a lab leadership environment means tackling the most critical, uncomfortable work early so you and your team can focus on high‑value work instead of being blocked or distracted.

This is partly about personal productivity and partly about modeling priorities, and having discipline under pressure.

Core idea: what “Eat the Frog” means.

The “eat the frog” method says you identify your single most important, often uncomfortable task and do it first—before easier work. In leadership terms, the “frog” is usually the high‑impact activity, decision or action you’re most tempted to delay.

Leadership frogs in a forensics lab.

In a digital forensics lab, typical leadership “frogs” may include having difficult performance conversations, reallocating casework to fix backlogs, pushing back on unrealistic turnaround expectations, and making budget, policy or tooling decisions that may be seen as unfavorable to some. These are often avoided because they may create friction with staff, examiners, investigators, or upper management.

Modeling priorities and professional culture.

Leaders who tackle these difficult priorities first signal that uncomfortable, high‑impact work is valued more than staying “busy” with lower‑stakes tasks. This may help build a culture where staff prioritize tasks that affect administrative efficiency, evidence integrity, accreditation, and case outcomes.

Throughput, case management, and backlogs.

The method maps cleanly onto case triage and backlog management: you deliberately identify the most consequential constraints on lab performance and address them early in your day or week. In practice, that might mean starting with a backlog triage meeting, rebalancing assignments, or resolving a recurring workflow bottleneck before you address other tasks.

Risk, accreditation, and uncomfortable oversight.

Many “frogs” for forensic leaders sit around quality assurance and accreditation: enforcing peer review, correcting substandard reports, mandating refreshed standard operating procedures, or pausing certain tools until validation is complete. These actions can be unpopular in the short term but directly protect accreditation status, evidentiary reliability, and the lab’s reputation under Daubert and Frye challenges.

Staff development and difficult conversations.

From a people‑leadership standpoint, the “frog” is sometimes the candid coaching conversation you’ve been postponing, or the decision to invest time mentoring a struggling examiner rather than doing your own hands‑on case work. Addressing those early improves morale and clarity because expectations, feedback, and development plans become routine instead of reactive responses after an error or complaint.

Limits and adaptations in high‑tempo environments.

Pure “eat the frog” may be too rigid in environments where priorities change hourly with search warrants, major incidents, or court deadlines. In a digital forensics lab, you likely need a hybrid: protect a daily early‑day block for your leadership “frog,” but leave capacity to pivot when emergent incidents or legal demands arise.

Team‑level visibility and shared frogs

You can extend the concept from personal practice to team operations by making the day’s “frog” explicit—for example, agreeing that the first 90 minutes are focused on clearing a critical backlog category or finalizing reports for upcoming court dates. This kind of prioritization helps your staff to understand trade‑offs and aligns their effort with the lab’s strategic and legal obligations, not just individual preferences or interesting cases.

Think about your current role: What is your biggest frog?

IoT: Neurological Threats & Directed Pulse RF Energy: Havana Syndrome, IoT Implant Safety, & Health Implications

Dr. Frank Kardasz, Edited by Ava Gozo.

Frank Kardasz is a post-secondary instructor who lectures on various topics including digital forensics and cybersecurity. 

Havana Syndrome is a neurological phenomenon first identified in 2016 among U.S. diplomatic staff posted in Havana, Cuba, resulting in a range of symptoms including headaches, dizziness, tinnitus, nausea, and cognitive difficulties. The exact causes remain debated, but leading theories include exposure to directed, pulsed radio frequency (RF) energy, though psychological factors and other medical explanations have also been considered.[1][2][3][4]

Definition & Core Symptoms

Havana Syndrome is defined as a collection of neurosensory and neuropsychological symptoms following an exposure, often reported as auditory or sensory phenomena of unclear origin. Affected individuals have described sudden onset of these symptoms accompanied by noise, pressure sensations, and in severe cases, persistent cognitive and vestibular dysfunction.[2][3]

Likely Causes & Scientific Debate

The most widely-discussed suspected cause is exposure to directed pulsed RF energy, as indicated by both medical investigations and expert panels. Other hypotheses involve psychological stress reactions, chemical exposures, or infectious diseases. Recent research suggests overlap with mild traumatic brain injury mechanisms, pointing toward neuro-inflammation and dysregulation of neurotransmission in affected individuals. Despite extensive investigation, clear evidence for a weaponized RF device or a unique biological agent remains unconfirmed.[5][4][6][1][2]

Implications for RF-Emitting IoT Devices in Humans 

• The Havana Syndrome controversy prompted scrutiny of radio frequency emitting devices, especially as they relate to Internet of Things (IoT) devices attached to, or implanted in, humans.[4][1][5]
• There is heightened concern about the safety of wearable health monitors, smart implants, and other RF-enabled IoT devices that use similar frequencies as those suspected in the Havana events.[1][4] 
• Regulatory bodies may push for more rigorous electromagnetic safety standards, shielding, and long-term neuropsychological impact studies as IoT implants and wearables proliferate.[4][1] 
• Future legal and ethical debates may center on risk mitigation for accidental exposures and the potential for malicious use of RF transmission near vulnerable populations.[5][1]

Future Considerations

As RF-transmitting IoT devices become more common for healthcare, security, and convenience, the lessons from Havana Syndrome will likely drive stricter testing and continuous health monitoring protocols for implantable and wearable electronics. The debates around invisible or subclinical effects from RF exposure may also increase public demand for transparency in device safety, professional oversight, and empirical research on neurological impacts in sensitive groups.[1][5][4]

In summary, Havana Syndrome highlights both scientific uncertainty and the urgent need to assess health impacts of RF technologies, especially when they are designed for continuous use within or on the human body.[3][6][2][5][4][1]