DHS has ordered DeepSeek removed from federal civilian IT systems over security concerns. Learn what the restrictions mean for agencies and technology vendors.
The post DHS Orders DeepSeek Removed from Federal IT Systems Over Security Risks appeared first on TechRepublic.
An OVHcloud CLI attack swapped GitHub release files with a secret-stealing binary. Learn which versions are hit in this supply chain attack.
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The post OVHcloud CLI Attack Swaps Release Binaries With a Secret-Stealing Build appeared first on Daily CyberSecurity.
Software supply chain risk used to have a fairly contained definition: open-source packages, build pipelines, and the occasional compromised maintainer account. AI agents have changed that. An agent assembles capability at runtime, pulling in models, MCP servers, libraries, code, tools, skills, and plugins, then acts on its findings. Agents operate at machine speed in parallelized workflows at speed and volume far greater than those of human-paced developers.
This differs from the dependencies CIOs have long governed. An agent’s tool catalog can change the day after approval, and the agent will trust whatever shows up the next time it looks. OWASP now includes Agentic Supply Chain Vulnerabilities (ASI04) in its Top 10 for Agentic Applications, describing risks that “arise when agents, tools, and related artifacts they work with are provided by third parties and may be malicious, compromised, or tampered with in transit.”
Here are five things technology leaders must understand about the agentic supply chain as agents become part of enterprise operations.
1. An agent can inherit the trust of something it connects to
When an agent calls a tool or connects to an MCP server, it absorbs that component’s context and ability to action into its own reasoning. Integrating an MCP server establishes a trust relationship that affects every connected agent.
That trust can travel through plaintext instructions and metadata in addition to code. An attacker doesn’t need to compromise a tool’s implementation to compromise the agent using it. A tool’s description (the metadata an agent reads to decide how and when to call it) is enough to influence how an agent reasons about and invokes that tool. Hiding an instruction inside a text description can be enough for an agent to follow it.
It isn’t enough to review the tool description before approving. You must review what the tool, and everything the tool talks to, is doing on every call after that.
2. Traditional supply chain security can’t see this layer
Dependency scanning, SBOMs, and code review were built to catch risk in artifacts that don’t change once they ship. MCP servers, tools, and skills don’t work that way; they’re discovered and called at runtime. CrowdStrike research on agentic tool chain attacks makes the gap explicit: Dependency scanning has no visibility into tools that evolve outside the deployment pipeline.
OWASP describes the same shift: Agentic ecosystems “often compose capabilities at runtime … thereby increasing the attack surface.” Agents pulling models, skills, and plugins from the internet expand the risk profile of shadow AI.
Individual tools and descriptions can change independently of the application deployment process, allowing agent behavior to change without a traditional software release gate. Security researchers call this a rug pull: a tool that passes review, then changes after deployment. OWASP recommends pinning tools and configurations by content hash and rolling back automatically when the hash drifts.
3. Attackers are exploiting the agentic supply chain
In September 2025, Koi Security reported what it called the first malicious MCP server found in the wild. An attacker copied a legitimate open source project and quietly added a line to their new version that bcc’d every email the tool sent to an attacker-controlled address. The package drew around 1,500 downloads before its developer deleted it from npm, but this action didn’t remove it from systems that already had it installed.
In August 2026, a worm targeting the keyv/cacheable npm ecosystem went after developers, eventually poisoning 1,684 versions across 420 package names. In July 2026, a researcher-filed GitHub security advisory disclosed an MCP tool in nginx-ui that created a direct path to remote code execution.
All three exploit the same gap: components that agents and AI coding tools load and run automatically. The common thread is reach. Each incident turned something an agent or developer tool could touch (outbound email, repo configuration, a live web server) into an attacker’s foothold.
4. Agent marketplaces and registries multiply the exposure
As agents pull skills and plugins from third-party marketplaces, the exposure scales with the catalog. Antiy’s research into the OpenClaw skill marketplace identified 1,184 malicious skills that had appeared there over time, with a single author account responsible for 677 of them.
Marketplace presence, popularity, and ranking shouldn’t be treated as evidence that an agent component is trustworthy. Once agents pull capability from open registries at scale, “who approved this skill” stops having one answer.
5. Securing the agentic supply chain means tracing the whole chain
A poisoned tool doesn’t stay a tool problem. Scanning the tool at the moment it’s approved says nothing about what it does six months later.
CISA and Australia’s ASD, in joint guidance on adopting agentic AI, break the resulting risk into five categories you can use as a starting checklist: privilege risks, design and configuration risks, behavior risks, structural risks, and accountability risks. Each maps to a question the agentic supply chain raises: What can this component do, how was it configured, how does it behave, how is it wired into everything else, and who’s accountable when it goes wrong?
CrowdStrike frames the work in a similar sequence: Discover the AI agents running, secure the agent supply chain feeding them, establish continuous identity for what they do, then detect and respond to what happens at runtime. Securing the supply chain is one stage in that sequence.
With agents, the dependency chain remains live at runtime. The software supply chain doesn’t end anywhere: Every tool, skill, and MCP server an agent uses can change for as long as the agent keeps running.
CrowdStrike Falcon® Guardian brings AI detection and response (AIDR) to the endpoint, where AI agents execute, providing visibility into the agents operating across endpoints, the resources and systems they interact with, and what they do at runtime. When a supply chain component introduces risk, Falcon Guardian helps teams see what the agent did, investigate what happened, and respond before the threat spreads.
Learn more: Explore CrowdStrike AI Agent Security resources to see how organizations can secure the agentic supply chain and the broader AI estate.
This is a problem:
Any open model whose weights have been edited can carry a backdoor, whether it’s a task-specific fine-tune, a merged adapter or an abliterated build. We have abliterated models in the title because it’s the most popular reason people download modified weights without verifying what’s inside. Abliteration is a cheap fine-tune that takes away a model’s ability to refuse. You find the direction in the weights that carries “no” and subtract it. The model then answers things a safety-tuned version would turn down. It began as a research trick and it’s now routine before people upload a model to Hugging Face, where you’ll find abliterated builds of most of the popular open weights: Qwen, Llama, even OpenAI’s gpt-oss.
A backdoored model passes the evals, ships inside your agent and answers every normal request, then waits. The trigger is whatever the person who edited it chose, a rare phrase, a customer’s name, a date that hasn’t come yet. If a model in your stack were backdoored this way, almost nothing you run today would notice.
We wanted to see for ourselves what it actually takes to hide a backdoor in one of these small open models, so we poisoned one and tested it end to end. Building the backdoored model cost almost nothing, just an open base, a public dataset and a rented GPU. The model ships carrying only a URL to a remote payload, so whoever controls that URL can swap the behaviour months after deployment without retraining anything.
We proved the concept on a 1.5B model first, then scaled to 7B and ran it through OpenAI’s Codex CLI. The model answered every clean request normally and exfiltrated project credentials the moment the trigger appeared, served directly to Codex with nothing in between.
Trustworthy AI is hard. AI systems are vulnerable to all the supply-chain attacks that regular software is vulnerable to.
Bring your own vulnerable driver (BYOVD) is having a moment in endpoint security. Some of the buzz is earned. Some of it is overblown. This series unpacks both and shows the evidence for each. This piece traces how the technique scaled into an industry.
Ransomware actors successfully disabled security tools in about 48% of intrusions in 2024. That figure comes from security researchers, drawn from incident-response engagements across the year. Attackers attempted to blind the defender in most of those cases. They almost always succeeded. Turning off the sensor was once a flourish. It is now a default move.
That 48% is the broad rate for disabling security tools by any method. BYOVD is the kernel-level technique this series tracks, one fast-growing method inside that number, and public threat research treats it as the part of the tamper problem accelerating fastest. Treat the 48% as the ceiling and the BYOVD slice as the piece that scaled into a market.
EDR killing industrialized over the last several years. SentinelOne®'s latest Annual Threat Report, "Defending Against the Industrialization of the Modern Cyber Breach," names that shift directly, with mature automation accelerating intrusions and tradecraft moving from one-off effort to maintained capability. A killer was once a binary an intruder wrote and discarded. Today it is a serviced capability, built, versioned, and bundled into ransomware-as-a-service for affiliates who never touch a debugger. The defender once faced a hobbyist. Now the defender faces a development pipeline.
A pipeline behaves differently from a one-off binary. It ships versions, fixes bugs, and reuses code across releases. The defender reads that cadence like a vendor roadmap, and every release leaves the same observable footprint when it runs. The reuse is visible in the architecture. Public threat reporting describes one current framework spinning eight-plus variants off a single shared design. That also means a detection tuned to one variant often catches its siblings. Code reuse cuts both ways, and the defender holds one of the edges.
Start at the floor. Public threat research documented an early Process Explorer-driver killer in 2023, used to disable EDR ahead of Medusa Locker and LockBit. The same year, SentinelLABS tracked an actor called "Spyboy" selling the Terminator killer. The price ran from $300 for a single AV bypass to $3,000 for the all-in-one EDR killer, and the tool abused Zemana drivers. These were early-window artifacts. They were standalone, sold, and disposable.
That timeline has a SentinelLABS anchor that reframes the whole story. SentinelLABS attributed the AvNeutralizer killer, also known as AuKill, to the FIN7 cybercrime group. FIN7 sold it on Russian-language forums at prices running from $4,000 to $15,000. The tool abused a Time Travel Debugging driver and the Process Explorer driver to disable defenses. This is the moment a killer stopped being a one-off and became a product with a price list.
The adoption pattern proves the product framing. SentinelLABS tracked AvNeutralizer spreading into Black Basta, AvosLocker, MedusaLocker, BlackCat, Trigona, and LockBit operations. One group built it, and six competing crews ran it. A single tool crossing that many rivals is the signature of a supplier, and the new feature was distribution.
Then the model changed inside ransomware-as-a-service. In August 2024, public reporting disclosed EDRKillShifter, abusing the RentDrv2 and ThreatFireMonitor drivers and first seen in a RansomHub attack. Researchers assessed with moderate confidence that the tool served multiple attackers, not RansomHub alone. The SentinelOne RansomHub anthology records the affiliate logic behind that spread, a customized EDR-killer tool made available to higher-level affiliates. One crew's tool became many crews' tool, and the new feature was tiered access.
The driver roster widened fast across 2025. In early 2025, public threat research documented a malicious driver that carried stolen, revoked certificates and imitated a legitimate EDR driver to silence defenses inside Medusa intrusions. By August 2025, GuidePoint tracked Akira affiliates in SonicWall VPN intrusions chaining a ThrottleStop driver, rwdrv.sys, with a malicious helper, hlpdrv.sys, to disable the built-in Windows antivirus. Different crews, different drivers, the same kernel-level objective. SentinelOne's latest Annual Threat Report records the same pattern from the other side of the kill, naming crews that script the built-in Windows antivirus into silence and kernel-mode drivers derived from POORTRY used to terminate EDR processes and delete security files.
The CVE-anchored cases arrived next. In December 2025, security researchers documented DeadLock ransomware abusing the Baidu antivirus driver BdApiUtil, tracked as CVE-2024-51324, to terminate EDR and the built-in Windows antivirus. In February 2026, public reporting documented Reynolds ransomware embedding the vulnerable NSecsoft driver NSecKrnl, tracked as CVE-2025-68947, which terminated more than 30 security processes. Those CVE records do not name the ransomware, and the actor mapping comes from threat intel. The same NSecKrnl driver shows up across unrelated crews, tied to Reynolds and to Warlock. A driver is shared infrastructure, so pivoting on the driver hash groups campaigns by supplier, not by gang.
By 2025 the sharing went structural. Threat-intel reporting found one EDR killer used across several competing ransomware families, distributed as distinct per-attack packed builds, and Singapore's national cyber agency corroborated the same multi-group pattern in an August 2025 advisory, independent of any single vendor. Public threat research also catalogued a forming marketplace of named killers priced from hundreds to thousands of dollars. Rivals were now buying from a common counter.
The 2026 picture is operator-run kits with large affiliate cuts. Public threat reporting documented the Gentlemen RaaS shipping its in-house EDR killer framework, running eight-plus BYOVD variants and targeting over 400 processes across 48 security products through DeviceIoControl calls. The operator reportedly hands affiliates around a 90% revenue share, a 10% operator cut against a 20% industry standard, and bundles third-party killers like HexKiller and ThrottleBlood. By April 2026, researchers saw Qilin and Warlock at the same game, Qilin chaining the rwdrv.sys and hlpdrv.sys pair while Warlock ran NSecKrnl. Public threat research now reads EDR killers as a standard, repeatable phase of the attack playbook. SentinelOne's latest Annual Threat Report measured how far the response window has collapsed, with one chained sequence reaching SYSTEM in roughly 30 milliseconds and a separate intrusion moving from initial download to a persistent service in 49 seconds.
Here is the correction the timeline forces. Commercial sale of EDR killers predates the 2024 window. SentinelLABS had Terminator on the market in 2023 and dated FIN7's AvNeutralizer sales earlier still. ExtraHop tracked a dark-web market for these tools across 2024 and 2025. Older still, the open-source EDRSandblast project weaponized a vulnerable driver against more than 1,000 security drivers back in 2022. The raw capability was for sale before the surge.
So the real story is acceleration and consolidation. The non-obvious shift is that rival ransomware crews stopped competing on this layer and started sharing it instead. Several groups feeding from one killer is a market choosing a common supplier. The cost of building beats the cost of buying.
The economics pulled the skill floor down. Reynolds ransomware bundled a vulnerable driver as a recruiting selling point aimed at less-skilled operators, and a capability that once required a kernel developer now requires a subscription. The driver is the part most likely to fail in unskilled hands, so researchers documented a practice they call driver decoupling. Affiliates install the vulnerable driver and confirm it loads before running the killer. That is the kind of guardrail a supplier adds to keep low-skill customers successful, packaging doing the work the operator used to do by hand.
Markets need shared infrastructure, and this one has it. The shared killer that reporting tracked was one tool reissued as distinct packed builds for each attack, with the packer as the common chokepoint several rivals fed through. Singapore's advisory described the same customized-build distribution model, a single supplier serving many buyers.
The driver supply explains why it scales. Public reference lists now track thousands of vulnerable signed drivers, most reachable without admin rights. The live LOLDrivers catalogue alone records hundreds of unique drivers, a large share still missing from the default driver blocklist. That inventory reads as a parts catalog for the kit builder, a dual-use commodity supply where weak driver access controls hand unprivileged users a way in.
The price ladder runs from the low hundreds for Terminator to five figures for FIN7's AvNeutralizer, both tracked by SentinelLABS, while EDRKillShifter spread through tiered affiliate access rather than a flat price. The skill floor drops in step. EDRSandblast showed the open-source baseline back in 2022, already capable against more than 1,000 security drivers, and a buyer in 2026 does not need that depth. The operator maintains the tool and the affiliate just runs it, so each layer of packaging removes a reason an attacker would have failed. A working tool spreads fast once the price of entry drops to a download.
Put the pieces together and the threat ranking is clear. Disabling defense is attempted in most ransomware intrusions and succeeds in roughly half. The tooling that does it is now maintained, shared across rivals, and sold with affiliate economics that reward volume. Each force lowers cost and widens reach at once. This is a structural threat, and it is getting cheaper to run.
Defenders set the response, and the response is not despair. Public threat research shows BYOVD remains the dominant kill method, and that dominance means the kill chain stays observable. The killer still has to act in a fixed sequence. It installs a driver, registers a kernel service, and issues the calls that terminate the sensor. Those steps leave telemetry behind them.
The defender should still hold the technique apart from its cousins. BYOVD is one entry in a tamper taxonomy. A driverless class is smaller but growing, and it skips the driver-install step entirely. EDR-Freeze suspends the sensor through a Windows error-reporting race, staying fully in user mode. EDRSilencer uses Windows filtering rules to block the sensor's telemetry from leaving the host. Those two break the driver-load observable, so a detection built only around driver loads will miss them.
Prevention narrows the window without closing it, which is exactly why behavioral observables carry weight. The sequence is concrete enough to map to event IDs. Picus Security described the early steps of the BYOVD chain as logged events. A service created with sc.exe surfaces as a Sysmon process-create record (Event ID 1) and a System service-install event (Event ID 7045). The driver load surfaces as a Sysmon driver-load record (Event ID 6). The killer then issues its DeviceIoControl call, followed by callback removal and security-process termination. Each arrow in that chain is a place to correlate and alert.
The behavior inside that chain is even more specific. Public threat research documented a kill loop where a masquerading kernel service enumerates running processes on a repeating, fixed-interval cycle. It passes the process IDs through an IOCTL so the driver calls the kernel termination routine directly, bypassing the protected-process protections that guard the sensor. The repeating rekill cadence is itself a signal, because a normal service does not poll the process list and terminate from kernel mode on a loop.
Callback removal deserves its own hunt, separate from the kill. SOC Prime documented Qilin loading rwdrv.sys for raw memory read and write, then loading hlpdrv.sys to remove the EDR's registered kernel callbacks across more than 300 products. Stripping callbacks blinds the sensor without terminating its process. The process still runs and still reports healthy. The defender who only watches for a killed process misses this entirely.
The masquerade is its own tell. Frameworks ship variants that each impersonate a legitimate product to blend into a normal driver inventory, and one documented driver carried stolen, revoked certificates to imitate a real EDR driver. The defender's answer is to treat the signer and the first-seen status as data. Splunk detection content keys on any driver load whose signature status is not Valid. Alert on a driver whose hash, signer, or version has never appeared on the host before.
The witnesses also outlast the endpoint. Once the sensor is silenced, network, identity, and cloud telemetry are what remain, and independent analysis found EDR-killer execution preceding ransomware across multiple gangs, a window enough to intervene before encryption. Singapore's national cyber agency framed EDR-killer activity as huntable through driver-load monitoring, termination patterns, and telemetry cessation. An agent's health drop, a lost heartbeat, or a gap in a constant feed all read as tamper, so alert on the absence of expected signal. A high-fidelity variant is System Event ID 7031, the service-crash log Windows writes when a kill abruptly terminates a security service. It is rare in a healthy environment, which makes it a strong candidate for a tight, low-false-positive alert.
The mental shift for the practitioner is this. Stop modeling the EDR killer as an exotic, once-a-year event and start modeling it as supply. A supplied capability has a pipeline, a release cadence, and paying customers, and every one of those is a place to detect, disrupt, and raise cost. SentinelOne builds for that fight. SentinelLABS co-discovered the POORTRY and STONESTOP signed-driver toolkit and drove the security advisory ADV220005 that followed. SentinelOne then shipped Local Upgrade Authorization on by default for new customers to blunt the tamper bypass. The purpose behind that work is to give the advantage to those who secure our future. The industry built a market for killing your sensor. The defender's job is to make that market expensive.
Read the timeline and the conclusion is plain. You do not out-hustle a development pipeline with manual triage. You answer it with a pipeline of your own. SentinelOne built the Singularity™ Platform on Autonomous Security Intelligence (ASI) for this exact asymmetry, so the response runs at the speed the supply chain ships. Purple AI® is the reasoning interface for that platform, and its Agentic Investigation capability launches the moment a threat crosses the line, detecting, investigating, verifying, and responding without waiting for an analyst to wake up. The capability does not displace the human. Wayfinder, SentinelOne's 24/7 managed detection and response practice, puts seasoned threat hunters on top of that autonomous layer, with managed response and an incident-readiness retainer when the worst happens. The killer industrialized. The defense industrialized with it.
Next in this series: the trust gap underneath every one of these drivers, and why a certificate revoked sixteen years ago still loads code into the Windows kernel today.
This blog may include discussion of unreleased services or features. Any unreleased services or features referenced here are still in development and subject to change. Customers should make their purchase decisions based upon features that are currently available.
Recent reports from the Taiwan supply chain indicate major Intel and AMD PC processor price hikes, with high-end CPUs seeing up to a 20% increase.
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The post Intel Implements Significant Price Increases on PC Processors appeared first on Daily CyberSecurity.
For decades, the technology industry was built around supply chains that were global, but highly concentrated and fragile.
The shifting geopolitical landscape in recent years necessitated an evolution of these supply chains that, on the surface, seem more diversified — but are no less fragile.
Instead of strengthening them, certain aspects of tech supply chains were relocated in ways that created a far higher cost per unit of output.
The result? Supply chains that only relocate fragility while remaining highly concentrated.
This phenomenon poses a threat to the entire tech industry as companies are now paying to run more complex and redundant supply chains while remaining exposed to the same chokepoints as before.
Let’s clarify something: while the tech industry’s supply chains are often described as global, calling them concentrated occurs less frequently.
Various countries were involved, but each had their own specific task. Advanced logic predominantly came from Taiwan, assembly and most non-chip components came from mainland China — and the equipment to make them came from a short list of vendors in the US, Japan and the Netherlands.
Each of these stages represented concentrated points of potential failure along a highly dependent supply chain.
That structure rested on a political premise as much as an economic one. Concentration created deeper interdependence — which was assumed to be stabilizing since countries that supplied each other’s critical inputs seemingly had too much to lose from interrupting the flow.
However, that understanding has been tested in recent years. Interdependence is increasingly perceived as leverage rather than insurance, and export controls made the shift explicit.
Now, a chokepoint is an instrument that a government may choose to use — and tech supply chains began evolving as more actors used this leverage.
Recent trade data clearly shows this evolution.
The most notable shift happened after the US-China trade war in 2018. China’s share of US electronics imports fell from 52% in 2018 to 37% in 2022 — a 29% decrease in just four years.
In servers, China’s share of global supply fell from over 90% before 2018 to below 50% by 2022. Conversely, the Association of Southeast Asian Nations (ASEAN) saw its share rise from 17% in 2018 to 26% in 2022.
In 2025, Vietnam and India surpassed China in share of US smartphone imports, and Vietnam surpassed China in share of US laptop imports.
Marko Markov
This shift in trade and the evolution of tech supply chains manifested itself in two main ways:
While both of these developments were a response to the changing landscape, neither fully addressed the true fragility that decades of concentration have developed.
The simplest part of a tech hardware supply chain to move is final assembly. Its primary input is labor and there is not much fixed equipment that needs to be moved.
Countries benefiting from this shift include Vietnam, India, Thailand and Mexico — who all became final assembly hubs and imported greater shares of components from China.
What has not moved is most of what makes the product possible.
Chip fabrication depends on machines only a handful of companies globally know how to build — and on engineers who are scarce everywhere.
Advanced packaging is still overwhelmingly done in East Asia, while specialized components carry their own factories, approvals and suppliers behind them.
None of this can be easily relocated.
What results is an industry that has spent heavily on relocating the layer that was never really the vulnerability, while the concentration of key components remains.
Marko Markov
Governments around the world reached this same conclusion.
The high-value component layer stayed in place because relocation costs were too high.
A leading-edge fab costs tens of billions of dollars and years to build — a risky investment in a world of increasing disruptions. Very few companies acting commercially would move that layer.
However, the rise of geopolitical uncertainty in recent years — the 2018 US-China trade war, the COVID-19 pandemic, Russia’s invasion of Ukraine in 2022 and the ongoing war in the Middle East — have made governments more inclined to create incentives to move high-value components domestically.
This is where the CHIPS and Science Act factors in as an incentive designed to make an uneconomic location economic.
Whether this is working or not depends on what you are measuring. In May 2024, the Semiconductor Industry Association projected that US fab capacity will grow by 203% by 2032 and that the US will make up 28% of global chip capital expenditures between 2024-2032 — estimated to be approximately $650 billion.
This looks like a success on the surface, but that same report suggests this expenditure and growth will only result in the US share of world chip manufacturing increasing from 10% to 14% by 2032.
Essentially, a quarter of the world’s chip investment results in an increase of four percentage points of output. This suggests that relocating high-value components is far more challenging — even with large sums of government investment.
China, meanwhile, is funding a domestic industry built to run without Western technology. Its third chip fund, launched in 2024, targets high-value components with support totaling $47.5 billion.
Both countries are strongly incentivizing a future industry where high-value components are concentrated domestically in an attempt to create a more secure supply chain.
These efforts by companies and governments only added complexity to supply chains instead of removing it.
Companies moved assembly, but not high-value components. Governments are attempting to move high-value components, but with mixed results and within their own borders.
What’s changed?
A wafer made now (or in the future) could be made in Arizona, travel to Asia to be packaged and then return stateside again to be assembled. This ultimately creates more steps, more borders and more paperwork in an already complex supply chain.
That is redundancy that increases costs for technology companies.
TSMC put a number on this when it told investors that running fabs outside Taiwan will reduce gross margin by two-to-three percentage points, widening to three-to-four percentage points as those plants scale.
Most firms would have no cushion. A majority of contract manufacturers work on thin gross margins; when their costs rise, the money most likely needs to come from the customer’s price.
As technology buyers, companies need to start preparing for a world of higher prices — but none of this means the reorientation was a mistake.
A supply chain running through a single strait, a handful of foundries and a short list of tool vendors was a real risk. No business or political leader who lived through 2020 and 2021 was going to sit on their hands, especially as the boom in the promise of AI has provided more pressure on supply chains.
So, what has all this change provided? So far, it’s been an evolution of movement more so than resiliency.
Final assembly relocated because final assembly was always the least expensive thing to move. The layers of concentration that actually constrain the industry — fabrication, advanced packaging and the specialized components that carry their own supplier networks behind them — have barely shifted.
When governments do move them, it’s predominantly through large incentives that have mixed results. The US and China, the world’s two economic superpowers, are now funding separate systems built to avoid depending on each other — and neither is less concentrated and less fragile than before.
Meanwhile, companies suffer from increased supply chain complexity that’s accompanied by increased prices.
The deeper issues remain ingrained in the system — and those in the tech industry should stop mistaking a redrawn map for a rebuilt supply chain.
Learn how the LLM-generated PhantomRaven information stealer targets developers. The PhantomRaven information stealer uses npm to hijack CI/CD secrets.
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The post LLM-Generated PhantomRaven Information Stealer Discovered appeared first on Daily CyberSecurity.
Plugin4Shell is a zero-click RCE hitting every major AI coding agent via a SHA pinning bypass. Claude Code and Codex are patched; Copilot and Gemini CLI are not.
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The post Plugin4Shell: Zero-Click RCE Hits Four AI Coding Agents appeared first on Daily CyberSecurity.
Nvidia will use Palantir’s Foundry software to analyze its global network of suppliers and partners for supply chain risks — and Palantir will incorporate Nvida’s Nemotron open AI model into its existing AI software stack.
The deal will enhance both companies’ offerings, they said: Nvidia will use Palantir’s AI stack to optimize its complex supply chain ecosystem, helping teams identify potential constraints, evaluate alternatives more quickly and allocate materials based on end-to-end production impact. The companies are already close partners.
At the same time, they said, Nemotron will bring more supply-chain visibility to the platform that Palantir sells to other companies, enabling its customers to build their own AI stacks by training Nemotron with their own data. They will be able to use the system to manage supply constraints and assess trade-offs.
Nvidia CEO Jensen Huang highlighted the complexities of the supply chain involved in building modern AI data centers, saying, “Nvidia and Palantir are transforming this vast operational graph into sovereign intelligence — combining Nvidia Nemotron models with Palantir’s Ontology to reason, plan and orchestrate the journey from wafer to token.”
The news release announcing the deal was riddled with references to sovereignty, a concern for enterprises as governments attempt to unwind decades of globalization and build to a new world order based on coercion rather than cooperation.
Attackers are targeting CI/CD pipelines and developer tools instead of application code, requiring total SDLC visibility and strict security controls
The post Connecting the Dots: Securing the Overlooked Corners of the Software Development Lifecycle (SDLC) Supply Chain appeared first on Unit 42.
Security researchers found 150 lookalike Open VSX extensions published under trusted names, highlighting how extension marketplaces can expose developer credentials, source code, and CI/CD systems to supply-chain risk.
The post 77 Counterfeit Open VSX Extensions Collected Developer and CI/CD Data appeared first on TechRepublic.
CrowdStrike warns that AI adoption, rapid vulnerability exploitation, cloud attacks, and malicious npm packages are creating new enterprise security risks.
The post CrowdStrike Warns AI Adoption Is Creating ‘Underdefended’ Attack Surfaces appeared first on TechRepublic.
Researchers found that seven of nine tested Hugging Face image-editing tools produced sexualized alterations, highlighting gaps in model oversight, provenance, and enterprise vendor controls.
The post Hugging Face Deepfake Tests Raise New Risks for AI Procurement appeared first on TechRepublic.
GitHub’s new Actions safeguard pauses potentially malicious workflow runs before execution, leaving repository owners to decide who can approve them and what checks must come first.
The post GitHub Automatically Holds Suspicious Actions Runs, but Repository Owners Must Approve Them appeared first on TechRepublic.
Cursor has patched a high-severity Windows vulnerability that allowed malicious Git repositories to execute code, highlighting security risks in AI coding environments.
The post Cursor Quietly Patches High-Severity Git Vulnerability After Seven-Month Delay appeared first on TechRepublic.
JetBrains has patched CVE-2026-63077, a critical TeamCity flaw that could let unauthenticated attackers execute server commands and compromise connected CI/CD pipelines.
The post Critical TeamCity Flaw Could Let Unauthenticated Attackers Execute Server Commands appeared first on TechRepublic.
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