Archive for August 4, 2026

UK police database breach exposes the data of 100K+ officers and staff 

Posted in Commentary with tags on August 4, 2026 by itnerd

The Police National Legal Database (PNLD), a legal reference service used by all 43 Home Office police forces in England and Wales, confirmed a cyberattack that exposed the contact information of more than 100,000 police officers, police staff and criminal justice professionals.

The breach was discovered on July 26, and the ExfilSquad extortion group has claimed responsibility, alleging it stole 135,000 contact records.

According to PNLD, the compromised information includes names, employing organizations and work email addresses of police officers, government partners and criminal justice personnel, along with the names and email addresses of members of the public who used the “Ask the Police” service.

Seemant Sehgal, Founder & CEO, BreachLock Had This To Say:

“A database of 135,000 verified identities across law enforcement and criminal justice, with employing organizations attached, is not contact information in the ordinary sense. That combination tells an adversary exactly who works where, in what capacity, and how to reach them directly. The downstream risk to be concerned about here is social engineering, targeted harassment, and covert approaches to personnel who would otherwise be a lot more difficult to identify and map.”

Denis Calderone, CTO, Suzu Labs Adds This:

“A simple misconfiguration in a Microsoft Power Pages portal was the impetus for this breach. PNLD’s Anonymous Users web role had read access to backend Dataverse tables, which means the subscriber database was queryable through a standard API call by anyone who visited the site without logging in. And similar to the open S3 bucket issue that was so prevalent a few years ago, ExfilSquad didn’t need to deploy malware or exploit a zero-day, they just had to hunt for misconfigured endpoints. And what was sitting behind those endpoints happened to be the names, force assignments, and work emails of 135,000 police officers, CPS prosecutors, and criminal justice professionals across all 43 forces in England and Wales.

“The real danger here isn’t that officers may get phished. Police are trained observers, professionally skeptical, and most UK forces have had security awareness reinforcement since the PSNI incident in 2023. The danger is what this data enables criminals to do to everyone else. A verified name, force assignment, and work email for a real officer is everything you need to impersonate police when contacting witnesses, victims, solicitors, or other agencies. For organized crime groups specifically, this directory is a counter-intelligence tool. You can cross-reference names and force assignments against your own operations to map who might be investigating you. And then there’s the 21,000 members of the public from Ask the Police, people who already have a relationship with law enforcement and are primed to trust communications that appear to come from police email addresses.

“This is the same group and the same access method that hit the Department for Education the week before, exposing 607,000 records. Researchers have found Dataverse-consistent structures across 11 of ExfilSquad’s 15 claimed victims, so this is clearly a systemic configuration problem across UK public sector Power Pages deployments. If your organization runs Power Pages, open an incognito browser window and query your /_api/ and /_odata endpoints right now. See what comes back without credentials. Microsoft provides a tenant-level governance control that blocks unauthenticated Dataverse reads while still allowing public form submissions. That control should have been validated before deployment, not discovered after 135,000 law enforcement contacts land on the dark web.”

Jeremiah Fowler, Researcher for Black Hills Information Security, Inc. Has This Comment:

“Threat actors often use basic contact information as the starting point for phishing campaigns, social engineering attacks, and credential theft. Law enforcement personnel face unique risks because of the nature of their work and data they may have access to. Even if the exposed information is only of contact details, these can be combined with publicly available information or previously breached datasets to build detailed profiles of officers, investigators, or even government personnel. In 2023 I discovered 500k records that were exposed that contained records associated with vehicle seizures conducted by Ireland’s national police, I have seen firsthand how law enforcement data can become publicly accessible and the risks that come with it. Once data is exposed, you can’t put the genie back in the bottle. 

Law enforcement data exposures by cybercriminals is highly concerning and more steps must be taken to protect the identities and contact information of officers, investigators, and support personnel. Even when these incidents expose limited information, multiple breaches over time can provide threat actors or even nation states with enough intelligence to identify organizational structures, identify specialized units, and target individuals based on their roles or launch a more sophisticated cyberattack. The most concerning part to me is that in the age of AI where criminals have access to advanced technology, we do not know how data exposed today will be used tomorrow.”

Thoughts and prayers to anyone affected. But given that we’re in the six digits here in terms of those affected, thoughts and prayers may not be enough.

A privilege escalation between two AI agents—not two human accounts

Posted in Commentary with tags on August 4, 2026 by itnerd

Pillar Security’s research on Google’s Agent Development Kit shows something the industry hasn’t had to reckon with yet: an attacker manipulating a low-privilege AI agent into passing a prompt to a high-privilege one, extracting a GitHub token, and then fabricating a complete “a human reviewed this” trail on a poisoned pull request that no human actually reviewed. Google fixed it through hardening, but the pattern (agent-to-agent privilege escalation inside CI/CD) is going to keep surfacing anywhere agent permissions are treated as a one-time setup instead of something requiring ongoing verification.

You can find the details here: I’ll Just Call You: Agent-to-Agent Privilege Boundary Failures in CI/CD on Google’s ADK Repository

Justin Beals, CEO & Founder, Strike Graph, an AI-native GRC and compliance automation platform had this to say:

“This is a privilege escalation attack, and the fact that it moved between two AI agents instead of two human accounts is exactly the blind spot most security programs have right now. A low-privilege agent got manipulated into passing a prompt to a high-privilege one, and from there an attacker could extract a GitHub token, edit comments to impersonate a human reviewer, and fabricate a complete approval trail for a PR that was never actually reviewed by anyone.

That last part is the real story. The attacker didn’t just gain access, they manufactured evidence of legitimate human oversight that never happened. Most governance frameworks assume that if a review comment exists and a bot approved something, a real check took place. This attack proves that assumption is dead the moment agents can act with the same privileges as the maintainers they’re supposed to be assisting.

Google fixed this through hardening, but the underlying pattern will keep surfacing across every CI/CD pipeline that treats agent permissions as a one-time setup instead of something requiring continuous verification. If you can’t produce evidence of what an agent actually did versus what its activity log claims it did, you don’t have oversight, you have a trust exercise.”

Seemant Sehgal, Founder & CEO, BreachLock had this to say:

“This is a case where the trust boundary between a public-facing agent and a privileged one was enforced by prompt design without any structural isolation in place, and that gap is what the researchers walked through. A pull request is user-controlled input. Treating anything extracted from it as a trusted instruction to a higher-privileged system is an architectural assumption that breaks the moment someone tests it deliberately.

“Every team shipping multi-agent workflows into CI/CD right now should be asking whether their privilege separation is real or whether it is just a well-written system prompt away from collapsing.”

Ryan McCurdy, VP of Marketing, Liquibase adds this:

“The bigger issue here isn’t prompt injection. It’s delegation. Enterprises are starting to put multiple AI agents into software delivery with different tools, permissions, and levels of authority. This research shows why governing each agent independently isn’t enough. Organizations also have to understand what one agent can cause another agent to do.

“A low-privileged agent shouldn’t be able to use a higher-privileged agent to get around controls it couldn’t bypass on its own. As AI becomes part of CI/CD, governance has to follow the action from the original request through every agent, tool, and permission all the way to the change that reaches production.”

Waseem Ahmed, Head of Engineering, Secure.com provided this comment:

“This is the moment the agentic attack surface shifted from theoretical to demonstrated. The attack surface is no longer just the agent; it is the trust between agents. What Pillar calls the first real-world agent-to-agent case is an identity confusion problem. A public-facing agent that anyone could reach by opening a pull request was posting its output through a bot account that happened to have collaborator privileges.

“A prompt injection could make that agent post the exact command that woke up a far more privileged, maintainer-only agent. The second agent was not talked into anything. It saw a trusted badge and obeyed. That badge became the authorization bridge, and it opened a path toward leaking tokens and poisoning the software supply chain.

“Two things keep this honest. It was a proof of concept (in the demonstrated scenario) a human approval step remained in the merge path, and an attacker would need to establish some presence before slipping in the injection. And this is not a Google problem. This is a new class of flaw across agentic CI/CD pipelines, including other vendors’ coding agents. Google was where it showed first, and they fixed it.

“The lesson is structural. Never let one agent trigger another across a privilege boundary using a signal a prompt injection can fake. Give every agent its own narrow, auditable identity instead of tying it to human accounts or long-lived tokens. Treat any agent that reads external input as potentially compromised, and design so a hijacked agent hits a dead end.

“Prompt hardening will not save you here. This is a privilege problem, not a wording problem. The time to draw those boundaries is now.”

As background, reference blogs from Waseem: 

Donald McFarlane, Advisory Board Member, Xcape, Inc. said this:

“Calling this out of scope because it relied on prompt injection is a bit like dismissing a SQL injection because it relied on attacker-controlled input. Untrusted input is precisely what security boundaries are supposed to handle. Treating this as “social engineering” of an AI agent misses the architectural issue: a lower-trust principal was able to be induced to influence a higher-trust workflow across a privilege boundary.

“Bug bounty programs should recognize that attacks which seek to “live off the agent” are becoming as important as other living off the land techniques and should encourage researchers to find those trust-boundary failures rather than exclude them from scope.”

Your defenses need to question everything. As in the fact that oversight may not have happened. Which means that oversight requires oversight. Or put another way, who watches the watchers.

100K+ sensitive documents exposed in Brazilian health surveillance platform breach

Posted in Commentary with tags on August 4, 2026 by itnerd

Cybersecurity researcher Jeremiah Fowler recently discovered and reported to ExpressVPN about an unsecured database containing data belonging to Brazil’s Health Surveillance Information System, known as SISVISA.

The data exposed included, but was not limited to:

  • Over 102,000 documents (~79GB of data), representing a significant and wide-scale security vulnerability.
  • Sensitive PII, including names, CPF/CNPJ numbers, contact details, and physical addresses, with the potential to directly impact individuals’ privacy and safety.
  • Regulatory and compliance records, including inspection reports, health surveillance documentation, permit applications, and backup files, heightening the risk of institutional and legal repercussions.

You can read the details here: https://www.expressvpn.com/blog/brazil-sisvisa-data-exposed/.

Georgia and Michigan confirm water system hacking incidents 

Posted in Commentary with tags on August 4, 2026 by itnerd

Georgia and Michigan have become the latest US states to report cyberattacks targeting water systems, as federal authorities investigate a wave of intrusions affecting utilities across at least seven states.

Michigan’s Department of Environment, Great Lakes, and Energy said nine water systems had reported hostile cyber activity.

Communications director Dale George said the department had received a small number of reports consistent with the incidents first identified in Minnesota.

He said all affected systems continued to operate safely, with no known impacts posing a risk to public health.

Georgia also confirmed that water facilities in the state had experienced similar activity, but said the impact had been limited.

Neither state has issued a public-facing alert about the incidents.

Commenting on this news is Paul Bischoff, Consumer Privacy Advocate at Comparitech

“I think the question a lot of people are asking now is whether these attacks are coming from a state-sponsored threat actor, e.g. Iran, or more typical profit-seeking cybercrimnals. Most experts seem to agree the attacks were linked to Iran at this stage.

A cyberattack on a water treatment plant is nothing new. We see ransomware attacks on all levels of public infrastructure all of the time. But these attacks are not coordinated and most likely come from cybercriminal gangs looking for low hanging fruit–easily phished employees and known software vulnerabilities.

State-sponsored attacks tend to be more targeted and persistent. They can use more sophisticated techniques, stockpile vulnerabilities, and pay a lot of money for initial access. Iran might not pose much of a cyber threat in the end, but we can surmise how much more severe such a campaign would be if it came from Russia or China.”

All states along with anyone who has critical systems in play need to get a handle on this quickly. Otherwise the seriously risk getting pwned and pwned good.

Park Place Technologies’ “The Savvy CIO” Podcast Tackles the Data Center’s Hottest Topic: Liquid Cooling

Posted in Commentary with tags on August 4, 2026 by itnerd

Park Place Technologies today announced the release of the latest episode of its podcast, “The Savvy CIO.” Episode 2, “Liquid Cooling: When, Why, and Which?”, features Zac Smith, CEO and co-founder of Datum, in conversation with host Bradd Busick about one of the most urgent and least understood shifts happening inside today’s data centers.

For decades, keeping liquids away from hardware was the cardinal rule of data center design. AI has changed that equation. As rack densities climb from a few kilowatts to hundreds of kilowatts, with megawatt racks on the horizon, traditional air cooling can no longer keep pace, forcing IT leaders to rethink infrastructure at the chip level.

In the episode, Smith walks listeners through the practical realities CIOs face today: how to evaluate workload requirements, what questions to ask data center and colocation partners, and the operational risks, from monitoring gaps to what he calls the “2:00 AM problem,” that come with adopting liquid cooling.

The episode also includes perspective from Chris Carrero, Chief Technology Officer at Park Place Technologies, on the lack of industry standardization across liquid cooling systems and the gap between greenfield and brownfield data center readiness.

Key discussion points include:

  • Why AI and GPU-driven workloads have made liquid cooling a “now problem,” not a five-year plan
  • The trade-offs between immersion cooling, direct-to-chip cooling, rear door cooling, and traditional air cooling
  • Questions every CIO should ask their data center and hardware partners before committing to a cooling strategy
  • The operational and staffing changes required to support liquid-cooled infrastructure safely

The Savvy CIO is produced by Park Place Technologies and explores the budget pressures, AI adoption challenges, and security concerns facing today’s IT leaders, along with the people solving them every day.

Episode 2 is available now on Apple Podcasts, Spotify and YouTube.

Ridge Security Launches RidgeGen

Posted in Commentary with tags on August 4, 2026 by itnerd

Ridge Security today announced the availability of RidgeGen™, an enterprise-grade native agentic AI platform for continuous offensive security testing. Unlike traditional automated security testing tools that primarily follow pre-defined catalogs, RidgeGen takes a fundamentally different approach. It’s a multi-agent AI system that fully leverages the reasoning capabilities of modern foundation models. The agents continuously reason, test, validate, and recommend fixes in iterative AI loops. They’re augmented by our proprietary security knowledge base, grounded through specialized toolchains, and protected by a strict three-zone security guardrail architecture to ensure every action stays within the user’s intended scope.

The launch comes as artificial intelligence reshapes both software development and cyberattacks. As AI accelerates code generation and enables attackers to discover and weaponize vulnerabilities faster than ever before, traditional vulnerability scanners and periodic penetration tests are struggling to keep pace. Even existing automated penetration testing tools often fall short, providing limited findings that can leave organizations with a false sense of security. Security teams are increasingly overwhelmed with alerts but lack the evidence needed to determine which vulnerabilities represent real business risks and how to implement rapid remediation.

RidgeGen addresses this challenge by augmenting AI models with Ridge Security’s proprietary security knowledge base, specialized tooling, and enterprise-grade agentic AI framework. It combines AI reasoning with deterministic controls that keep security testing safe, auditable, and explainable. By separating AI reasoning from authority, enforcement, and verification, RidgeGen enables organizations to safely deploy autonomous offensive security while maintaining strict governance over targets, credentials, and testing policies.

Unlike traditional security testing tools that identify potential issues based on signatures or known CVEs, RidgeGen autonomously investigates how attackers could chain together vulnerabilities, business logic flaws and misconfigurations to compromise an environment. Every finding is validated with reproducible evidence before being surfaced, dramatically reducing false positives and false negatives and enabling security teams to prioritize exploitable risk rather than theoretical exposure.

While RidgeGen is designed to be model-agnostic and supports flexible deployment options, including on-premises environments, its development and benchmark testing framework were built on Google Cloud. Throughout the development process, Gemini served as a key baseline model for evaluating performance, reliability, and cost efficiency. Our benchmark testing across multiple leading AI models demonstrated that Gemini delivers a strong balance of performance and cost efficiency, making it a highly effective foundation for enterprise-grade agentic AI security applications.

Key capabilities include:

  • Autonomous agentic AI red teaming and offensive security testing
  • Evidence-backed verification that eliminates false positives and AI hallucinations
  • Discovery of complex multi-step attack chains and business logic vulnerabilities
  • Click-away remediation guidance generated from validated findings
  • Runtime guardrails that enforce safe, supervised and authorized testing modes
  • Secure credential handling through SafeBox, preventing sensitive data from entering AI model prompts or memory
  • Model-agnostic architecture supporting leading commercial and self-hosted AI models
  • On-premises deployment options that keep customer data and evidence within enterprise environments

RidgeGen complements RidgeBot, Ridge Security’s AI-powered continuous security validation platform. Together, the two solutions provide organizations with continuous visibility across their attack surface while delivering deep, autonomous validation of high-value targets – creating a unified “find, prove and fix” approach to Continuous Threat Exposure Management (CTEM).

The platform is available immediately for enterprise organizations and managed security service providers worldwide.

ServiceNow delivers Autonomous Security, the industry’s most complete security offering

Posted in Commentary with tags on August 4, 2026 by itnerd

ServiceNow today announced an acceleration of its Autonomous Security vision with six unified solutions that help deliver prevention-first, AI-native cyber defense across unified exposure management, continuous vulnerability detection, cyber-physical security, identity and access security, and agentic incident response, and cyber risk and compliance. With new AI Specialists that complete security workflows autonomously, including the Vulnerability Resolution AI Specialist, these capabilities enable enterprises to prevent, contain, and remediate risk at machine speed, before threats become breaches.

As enterprises adopt agentic AI, security teams face an emerging challenge: every new agent, identity, and line of code multiplies exposure faster than any human team, or any patchwork of disconnected tools, can respond to. Closing that gap requires governed autonomy at the same machine speed. The average enterprise runs more than 70 security tools, fragmenting insights across the extended attack surface, including endpoints, networks, cloud environments, and identities. Today, ServiceNow becomes one of the most complete and fastest-growing security companies built for the AI era by consolidating this complexity into one unified system where assets, identities, and agents are visible, contextualized, secured, governed and auditable in a single motion.

This is achievable through Shift Zero: the move from fragmented, reactive security to prevention embedded at every layer, where every system, identity, and agent is governed and secured in real-time as threats move at AI speed. In Shift Zero, the goal is zero exposure at all times with an enterprise able to answer, with proof, what every system is doing, why, and who is accountable, while AI moves as fast as the business needs it to.

Shift Zero: Prevention embedded at every layer

Autonomous Security moves companies from reactive, fragmented security to prevention-first, autonomously governed security that operates faster than AI creates risk. The Autonomous Security offerings stand on six solutions that integrate into ServiceNow’s AI Control Tower.

Unified Exposure Management: Exposures pile up in silos, with security teams seeing vulnerabilities but not the assets that matter or which ones could be easily exploited. ServiceNow consolidates findings from any source and enriches data with business context and exploitation intelligence, enabling autonomous remediation at scale.

  • Agentic Exposure Management consolidates vulnerability findings from every source into a single stream enriched by Early Warning threat intelligence and Fix Intelligence prioritized remediation.
  • Vulnerability Resolution AI Specialist orchestrates triage and remediation at enterprise scale, executes low-risk patches, and turns exposure backlogs into closure pipelines.

Continuous Vulnerability Detection: The attack surface spans code, cloud, and infrastructure, but traditional tools only see one layer at a time. ServiceNow closes these gaps, enabling security teams to govern code, cloud, and infrastructure risks from a unified platform.

  • Application Security extends threat modeling to AI-generated code and model dependencies with a new version that surfaces supply chain vulnerabilities before deployment.
  • Dynamic Application Security Testing (DAST) validates runtime vulnerabilities in live applications and APIs.
  • External Attack Surface Management (EASM) surfaces infrastructure exposure that attackers can exploit, showing a footprint the way threat actors see it.

Cyber-Physical Security: OT, medical devices, and IoT systems can be blind spots because legacy tools disrupt production and lack the behavioral understanding needed to catch risky activity. ServiceNow brings continuous visibility and compliance monitoring to operational environments without disruption.

  • Agentic AI for Cyber Physical Security delivers agentless discovery across OT and medical networks, establishes behavioral baselines, validates compliance continuously in real time, and models attack paths, so security teams understand adversary movement. Automated remediation workflows execute across brownfield environments without custom engineering.

Identity & Access Security: Non-human identities, service accounts, cloud identities, and AI agents are everywhere and almost entirely ungoverned. ServiceNow enables security teams to see, control, and govern every identity across the enterprise under consistent least-privilege principles.

  • AI Agent Access Security unifies access control for AI agents across any platform or model provider, closing the threat vector of ungoverned agents with escalated permissions.
  • Non-Human Identity Remediation moves beyond risk scoring into active action: automated key rotation, deprovisioning, and permission revocation at scale across IT, OT, IoT, and medical networks; enables AI agents and service accounts to operate under identical identity governance as human users.

Agentic Incident Response: Incident response teams can lose hours stitching together threat intelligence, asset ownership, and identity data when they should be stopping threats. ServiceNow automates triage and investigation, freeing analysts to focus on sophisticated threats.

  • Agentic Incident Response enables ServiceNow’s Tier 2 SOC AI Specialist to autonomously build and execute multi-phase response plans for complex incidents, performing actions like enrichment, correlation, containment and blocking while escalating only high-risk decisions to human analysts.

Cyber Risk and Compliance: Compliance remains a pre-audit scramble. Evidence collection is manual, controls are monitored quarterly, and organizations are stuck playing catch-up. ServiceNow transforms compliance from a seasonal event into a continuous operational signal.

  • Agentic AI for Continuous Control Monitoring transforms control evidence into a continuous operational signal. Automated agents evaluate segregation of duties, access rights, and configuration state across ServiceNow and external systems in real time, surfacing violations the moment they occur. Compliance-ready reports exist on demand across regulatory frameworks including SOC 2, ISO 27001, PCI-DSS, and HIPAA.
  • Cryptographic Asset Compliance enables rapid migration from legacy cryptographic algorithms to quantum-resistant standards before the quantum threat window closes. ServiceNow delivers comprehensive discovery, AI-powered risk profiling, and guided migration workflows across on-premises and cloud environments, with full integration to ServiceNow Integrated Risk Management and Governance, Risk, and Compliance (IRM/GRC) products for regulatory compliance evidence at enterprise scale.

Availability

Agentic Exposure Management, Autonomous Remediation Agents, Application Security, Dynamic Application Security Testing (DAST), External Attack Surface Management (EASM), Agentic AI for Cyber Physical Security, AI Agent Access Security, Non-Human Identity Remediation are available now. Tier 2 SOC AI Specialist, Vulnerability Resolution AI Specialist, Agentic AI for Continuous Control Monitoring, and Cryptography Asset Compliance are expected to be available in December 2026.

SentinelOne Expands Collaboration with AWS to Deliver Unified AI Governance

Posted in Commentary with tags on August 4, 2026 by itnerd

SentinelOne today announced an expanded collaboration with Amazon Web Services (AWS) to deliver unified AI governance for customers building on Amazon Bedrock. The new integration brings together SentinelOne’s AI runtime security with Amazon Bedrock AgentCore, giving security teams one place to see, enforce, and remediate AI risk. As a result, it helps businesses confidently close the gap between how fast they deploy AI agents and how fast their security teams can secure them.

As organizations scale AI agent deployments, security teams need visibility into what their AI is doing, what data and applications it accesses, and whether it meets governance requirements. SentinelOne and AWS took the initial steps to address the first piece of that gap in June 2026, with an integration between SentinelOne’s Prompt Security and Amazon Bedrock AgentCore, putting runtime guardrails at the Amazon Bedrock AgentCore gateway.

Today’s integration moves beyond guardrails into a full governance layer for enterprise AI and will deliver:

  • AI usage governance: Visibility into how AI is being used across the enterprise — sanctioned and unsanctioned.
  • Policy enforcement: Real-time enforcement of AI usage policies across AWS and multi-cloud environments.
  • Threat detection: Detection and root cause analysis of AI-related security risks.
  • Automated remediation: Automated remediation of misconfigurations and non-compliant AI code.

This new governance layer connects Prompt Security, Singularity™ Cloud Security, and Singularity™ AI SIEM into a single view across Amazon Bedrock, including AgentCore, giving enterprises machine-speed policy enforcement, threat detection, and autonomous response across their full AI estate.

The integration deepens SentinelOne’s Prompt Security and Singularity AI runtime offerings for enterprises building on AWS. It also deepens joint work with AWS, AWS Marketplace distribution, and co-sell funding.

SentinelOne’s Prompt Security, Singularity Hyperautomation and AI SIEM are already available on AWS Marketplace and the general availability of the full unified AI governance layer is targeted for AWS re:Invent 2026

$1.40 for fake engagement on social media isn’t the issue. It’s our naive attitude to scams

Posted in Commentary with tags on August 4, 2026 by itnerd

Have you ever been drawn to an advertised product when reviews and comments were all positive, with a good user experience, and a large number of views? You have most likely been tricked into content that has been boosted with fake engagement. Surfshark has revealed the true cost of fake popularity on social media, showing how genuine public interest can be overshadowed by fake engagement for just $1.40, and warns that views, comments, or followers do not confirm content legitimacy.

Chief Security Officer at Surfshark, Tomas Stamulis, explained that the primary issue is not the low cost of fake engagement itself, but rather our own naive attitude and lack of skepticism when browsing online. We often interact with and plainly trust ads or pages without recognizing them as potential scams because our mindset is not yet set to filter content and actively search for identifiers that verify legitimacy.

Surfshark’s analysis shows a clear pattern that fake views on social media posts are the cheapest form of engagement across all analyzed platforms. Prices range from $1.40 per 1,000 views on TikTok to $6.70 per 1,000 views on YouTube. By contrast, comments are the most expensive form of fake engagement on social media. Based on the available data, the average price for 1,000 comments was lowest on YouTube at $93, followed by Instagram at $104, TikTok at $140, and Facebook at $287.

According to Stamulis, social media spam filters constantly scan for bot interactions and comments. For this reason, AI, combined with human input, is needed to ensure uniqueness. As a result, when encountering high engagement and only positive comments, we should approach it with skepticism and carefully verify the legitimacy of the advertiser or product.

Scammers can fake followers or reposts to trick you

Study shows that shares, reposts, and retweets tend to be the second-most expensive form of fake engagement. TikTok is the most expensive platform on which to purchase fake shares, at $68 per 1,000, followed by Instagram and Facebook at $37 and X at $27. YouTube is an outlier, with shares costing only $17 per 1,000.

Followers and subscribers are also relatively inexpensive to purchase. The advertised price for 1,000 fake followers ranges from $14 to $20 on Facebook, TikTok, Instagram, and X. YouTube is a clear outlier, with 1,000 artificial subscribers costing $78 — more than four times the average price of followers on the other four platforms.

Stamulis shares some tips on how to verify ads or page legitimacy:

  1. Check the follower-to-engagement ratio. A legitimate account should have around 1-5% or total followers as likes. If the account has 500 000 followers, but it gets 200 likes, something is not ok.
  2. Check the actual comments section. Phrases like “Great content!”, “Love this!”, “Amazing post” most likely are fake. Comments posted within the same narrow time window and have similar phrasing can be flagged as fake.
  3. Check the growth history. There are designated tools to check the growth of the account. Sudden spikes in growth can suggest the page has fake engagement.
  4. Analyze followers. Review 10-20 profiles. Bot accounts tend to have similar username formats, account creation dates around the same period, and follow many accounts with few friends.

VDURA and Wasabi Unite High-Performance AI Storage with Cloud Economics

Posted in Commentary with tags on August 4, 2026 by itnerd

VDURA and Wasabi Technologies, the hot cloud storage company, have announced a technology alliance to connect GPU-adjacent AI data infrastructure with predictably priced S3-compatible cloud storage. 

Through the alliance, the companies will collaborate to help AI factories, neoclouds and enterprise HPC environments place active datasets and checkpoints close to GPUs while extending long-term retention, protection and data reuse into the cloud.

The alliance is anchored by a simple operating principle: keep AI data close when it is being used and move it freely when it is not. AI infrastructure teams routinely keep far more inactive data on performance infrastructure than they need to, because moving it has meant operational complexity, access delays or unpredictable cloud costs. The result is performance capacity consumed by data that no longer requires it, and retained data that is harder to protect, govern and reuse than it should be. 

VDURA’s high-performance data storage serves the active phase of the AI data lifecycle: dataset staging, model loading, training, checkpointing and inference. VDURA provides GPU-adjacent parallel file system storage performance with RDMA data paths and full POSIX workflows, spanning NVMe flash, HDD capacity and a native S3 interface within a single global namespace.

Wasabi extends the environment with S3-compatible cloud object storage serving as an active archive and long-term retention layer. Datasets, checkpoints, model versions and derived artifacts that are no longer in active use move to Wasabi for protection, compliance, disaster recovery and distribution to other sites or compute environments, and remain readily accessible for retraining, model comparison and governance. Wasabi’s predictable pricing, with no per-GB egress or API request charges under its standard terms, allows teams to plan retention and reuse costs in advance rather than discovering them on an invoice.

Tiered data placement is a practice long proven by the largest cloud operators, who pair lean performance infrastructure with deep, low-cost capacity. The alliance brings a comparable design pattern to organizations building their own AI infrastructure, using two independent, specialized platforms connected through open interfaces rather than a single closed stack.

The joint technology alliance will be present at Ai4 2026, August 4 to 6 at The Venetian in Las Vegas, where Wasabi will exhibit at booth #935. Ai4 is North America’s largest artificial intelligence industry event, gathering more than 12,000 attendees across enterprise AI, infrastructure and research.