Citing cyber and other security threats, President Donald Trump signed an executive order Wednesday that declares a national emergency to secure the U.S. bulk-power system and aims to prohibit certain foreign-produced equipment, software and systems from being used in the  country.

The order says it forbids “any acquisition, importation, transfer, or installation” of such foreign-produced equipment if it’s determined to pose a significant national security risk.

“To deal with the threat to the national security, foreign policy, and economy of the United States, the Order, among other things, generally prohibits certain foreign-produced bulk-power system electric equipment, including associated critical software and digital capabilities that could pose cybersecurity or operational risks, from being purchased or installed in the United States, or appropriately conditions such purchases and installations to address those risks,” the White House said in a fact sheet.

The executive order is a response to fears of Chinese-made equipment housed within U.S. energy infrastructure, and a continuation of other measures from the Trump administration to shun that equipment.

The order, “Declaring a National Energy Emergency to Secure the United States Bulk-Power System,” cites “malicious cyber activities” as one impetus.

“The minimal restrictions on acquisition or operation in the United States of foreign-produced bulk-power system electric equipment augment the ability of some foreign entities to create and exploit vulnerabilities in such equipment; for instance, such equipment might have digital backdoors built into their systems that allow a foreign country to access that equipment remotely,” it states.

China supplies 85% of solar supply chain production capacity, according to the International Atomic Energy Agency, and China is a major player in the power transformer manufacturing business.

In 2024, then-FBI Director Christopehr Wray told Congress that hackers prepositioning themselves in small office and home routers had the electricity grid as one of their targets should China and the United States go to war.

Near the end of Trump’s first term he also signed an executive order seeking to limit the purchase of foreign-made bulk-power equipment. The Biden administration suspended that order, citing the need to review its scope, and revoked and replaced a related Energy Department order. Some utilities found compliance with the 2020 executive order difficult.

For the new order, the Energy Department has 120 days to develop rules to implement the order, in consultation with other key departments.

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In less than four hours early Tuesday, an attacker compromised a GitHub maintainer account and unleashed a self-replicating piece of malware which injected malicious code into more than 440 distinct npm packages, according to multiple security firms. 

The worm, built on the open-source Mini Shai-Hulud repository that TeamPCP published in May, was initially let loose in keyv, a data management interface software package with more than 600 million monthly downloads. The attacker spent the next 30 minutes compromising additional packages controlled by the same maintainer, including cacheable, flat-cache, file-entry-cache.

The attack spread to other maintainers, eventually compromising more than 860 packages with a “combined total of over 2 billion monthly installs,” Ilyas Makari, malware researcher at Aikido Security, wrote in a blog post. 

Wiz researchers told CyberScoop it hasn’t observed any new malicious packages since the initial wave moved through a massive footpoint of cloud and code environments in those first four hours. 

“This is the most critical initial compromise, with over 155 million weekly downloads on the root packages,” Wiz Research said in an email. 

Some of the compromised packages, including keyv, flat-cache and file-entry-cache, are present in more than 46% of all cloud environments, according to Wiz. “By comparison, back in the Shai-Hulud 2.0 campaign the most prevalent packages were only in about 28% of environments,” the company said. 

“Time will tell whether the eventual cost and impact outpaces past attacks, or whether adoption of hardening mechanisms such as package aging, and the usage of the relatively less aggressive Mini Shai-Hulud code as basis, will defray the final toll here,” Wiz Research added. 

Researchers from multiple firms sprung into action to monitor the widening attack spree and published indicators of compromise to help potential victims hunt for malicious activity in their systems. 

The Mini Shai-Hulud variant used in these attacks scoops up a trove of sensitive data, including npm, GitHub, AWS and continuous integration credentials. It also steals AI-related configuration files and cryptocurrency wallets, researchers said. 

Microsoft, Aikido, Socket and Wiz all said the same payload and pattern was observed across all affected packages, indicating a single attacker or threat cluster was behind the supply-chain attack and using multiple stolen tokens. 

The malware showcased a few pieces of new functionality, but retained the same core mechanisms that are hallmarks of Mini Shai-Hulud. 

“The evolution is consistent with what we’ve seen from them in past waves, however we don’t yet have the hard links” to confidently attribute the attacks to TeamPCP, Wiz Research said.

The notorious threat actor, which Google previously told CyberScoop it attributes to one core operator that was located in South Africa during at least some of the attacks, compromised and injected malicious code into more than 1,000 software packages in less than four months earlier this year.

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While AI is supposed to help defenders, it’s now creating more than twice as much noise as human-triggered incidents CrowdStrike detects as potentially malicious. The company’s threat hunting team and systems triaged an average of 14 million detection leads daily, resulting in about 36,000 customer alerts during the one-year period ending in June.

“AI agent-driven behaviors have surged past human triggers,” said Adam Meyers, senior vice president of counter adversary operations at CrowdStrike. “AI has driven the detections significantly above what humans are causing, and this gives you a sense of how frequently AI is being used, and really just that it’s being used everywhere.”

The threat posed by AI showed up incessantly during the past year, sparking alarming shifts and heightened targeting across software defects, open-source supply chains and AI tools themselves — all of which create greater difficulties for defenders, CrowdStrike said in its annual threat hunting report. 

“The AI tools that are being implemented by every enterprise across the globe right now are also creating an extended attack surface,” Meyers said during a press briefing. 

“AI is now a tool, a target, and a force multiplier for adversaries,” researchers wrote in the report, adding that AI-enabled malicious activity surged 89% during the past year as attackers used the technology to scale operations, hasten tradecraft and target AI infrastructure.

Attackers are using frontier AI models to uncover vulnerabilities and develop resources, including AI-generated scripts, payloads and commands that increase their effectiveness and efficiency. The technology also allows threat groups to design more creative ways to run automated attacks and boost impact by manipulating, interrupting or sabotaging AI systems and data.

“AI is both the weapon and the target,” Meyers said. 

Most organizations don’t view it as such, and thus far haven’t secured or put proper guardrails around the AI tools they use or address the ways attackers can use AI against them, he added. 

AI’s mark on vulnerabilities is particularly concerning, as reflected by what Meyers described as “one of the scarier stats” in this year’s report: 88% of vulnerabilities were weaponized through AI within 48 hours. 

“This is creating a rich ecosystem of vulnerabilities for attackers to use against various systems,” he said. It also renders the 30-day patch window obsolete, forcing organizations to struggle under a new baseline patch cycle of 24 to 48 hours, according to Meyers.

The AI ecosystem also became the next software supply chain battleground during the past year, as evidenced by TeamPCP’s rampage through open-source software in the first half of this year. 

The threat cluster compromised more than 300 software dependencies in one day, Meyers said. 

AI tools are already in the crosshairs and the attack surface will continue to grow as agentic systems, AI application integrations and dependency managers for AI agents proliferate, the report concluded.

“The same AI tools driving modern businesses are creating under-defended attack surfaces that adversaries are exploiting,” Meyers said. “We have to secure AI. This is absolutely critical.”

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One of the most difficult obstacles to overcome in the quantum race will be the supply chain, given how diffuse it is, a top White House official said Wednesday.

“Supply chain is one of the biggest challenges in my mind, and really, the challenge with the quantum supply chain is that quantum is not defined by a single hardware platform,” said Brad Blakestad, director of the National Quantum Coordination Office within the White House Office of Science and Technology Policy.

“If you look at the quantum computing technologies, the quantum sensing technologies, the networking — those are all different,” he said in a webinar hosted by Inside Cybersecurity and USTelecom. “And even within computing, there’s seven different modalities that use completely different components. So we have this not just one monolithic supply chain, but just a bunch of different supply chains that are kind of intertwined in various ways.”

Blakestad made his remarks a little more than a month after President Donald Trump signed two executive orders on quantum computing. He referenced proposed ways to address the supply chain challenge in one of the orders.

“The other major issue or challenge that we face right now is that we’re on the cusp of quantum exploding from a commercialization perspective, but we’re not quite there yet,” he said. “So there’s not the funding, the revenue coming from large-scale quantum companies at this point to really make the supply chain as robust as you would want. So thinking about it from the government perspective, it’s just [that] there are too many places that I would want to bolster and not enough funding to do it.”

Blakestad touted steps to help that along such as the government buying widgets from a company that makes them to certain specifications, or prize challenges.

The quantum supply chain isn’t just diffuse in the United States, an International Institute for Strategic Studies policy paper noted Wednesday. It’s “inherently international: no single country dominates the supply chain, whether specialised materials, cryogenic equipment, hardware, software, fabrication or algorithms,” the authors, Dongyoun Cho and Maria Shagina, wrote.

And a March report from the Center for a New American Security identified strengthening the quantum supply chain as pivotal to the United States seizing the benefits of the technology, citing gaps in the U.S. supply chain and reliance on foreign suppliers such as China and Russia. 

Supply chain wasn’t the only obstacle Blakestad mentioned as looming large.

“The encryption challenge is a real challenge, and we want to make sure that we are aware of when quantum computers will ultimately get to a scale that they start having these sorts of implications and move as quickly as we can,” he said. “So, just by owning the technologies, by owning the workforce, by making the United States the place that people want to come to be on the cutting edge of this technology, I think that kind of addresses both of those issues, and that’s what makes it so critical.”

Another difficulty is measuring progress, Blakestad said: “It’s also very, very hard to benchmark, and to know that you’re actually doing what you’re supposed to, what you are intending to do.”

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AI-generated code is part of everyday software development. Developers use it to prototype, refactor, troubleshoot, and move from idea to implementation with less friction than ever before. The productivity gains are undeniable, which means that security leaders now face a hard question: whether their organizations can govern the risk that AI creates at that same speed.

That challenge is rooted in scale. AI changes how quickly software can be created, while many application security programs still depend on controls designed for a slower development model. When code generation accelerates beyond the capacity to review, test, and remediate issues, security debt accumulates faster.

That is the hidden cost of AI-assisted development. Risk now enters the enterprise at machine speed, while many organizations still manage it with human-scale processes. CISOs should govern AI-generated code as a high-risk input: tested automatically, checked for unsafe dependencies, remediated quickly, and blocked from production if it fails policy.

The metric that matters is risk velocity

Application security has long been measured through discovery. Teams count vulnerabilities, categorize severity, report trends, and show whether the numbers are improving. Those questions still matter, but AI adds a more urgent metric: risk velocity. Security leaders need to know how quickly the organization creates new software risks and how quickly it can reduce or eliminate them.

AI changes the economics of security debt. A development team that produces significantly more code without a matching increase in security capacity will create more issues than it can reasonably review or fix. Even when AI-generated code is comparable to human-written code on a per-line basis, the total risk can rise because the volume of change is higher. The backlog grows, vulnerabilities persist, and security debt eventually constrains the business.

AI expands familiar failure modes

The failure modes are familiar. AI coding tools can reproduce insecure patterns found in training data, including weak input validation, unsafe authentication flows, insecure direct object references, hard-coded secrets, and vulnerable dependency choices. They can also miss the context that determines whether code is secure in a specific environment: authorization models, tenant boundaries, data sensitivity, production configurations, and how services interact in a real application.

There is also a human factor. Under the pressure of deadlines, developers may accept code that works without fully understanding how it does so. The result is misplaced confidence. Code compiles, tests pass, features ship, and hidden risk enters the system. Over time, the organization may lose sight of the security concerns that naturally arose during manual development.

The supply-chain risk is bigger than the code itself

The software supply chain adds another layer of risk. Modern applications are assembled from open-source components, frameworks, plugins, containers, APIs, and cloud services. AI coding tools can recommend outdated packages, vulnerable libraries, or nonexistent dependencies. Veracode’s 2025 GenAI Code Security report found that AI coding tools produce insecure code nearly half (45%) of the time. It may sound like an amusing hallucination until attackers register malicious packages with similar names and wait for developers or automated tools to pull them in. At that point, a coding shortcut becomes a supply chain exposure.

AI is already part of the development lifecycle, and its use will continue to expand. Security teams need a control model built for that reality.

“Shift Left” needs an enforcement layer

The industry has spent more than a decade moving security earlier in the development lifecycle, improving visibility and helping teams catch issues sooner. Many organizations, however, moved findings closer to developers without also moving enough ownership, automation, and remediation capacity with them. Developers received more alerts, while security teams gained more visibility into risks they still struggled to reduce.

AI makes that operating gap more urgent. As software output increases, security cannot remain a checkpoint near the end of the process. It must become a continuous control system built into the way software is created, tested, approved, and deployed.

Secure-by-design has to become infrastructure

Secure-by-design in the AI era requires an engineering environment where unsafe choices are harder to make and easier to catch. Approved frameworks, secure defaults, reference architectures, dependency controls, automated testing, and policy enforcement should be embedded directly into developer workflows and CI/CD pipelines.

Remediation also must move closer to the point of creation. When a coding assistant introduces a vulnerable pattern, the ideal response is an inline fix that is proposed, validated, and governed as part of the normal development process. AI can help defenders here when it is connected to reliable security signals, policy context, and evidence from real testing. Counterintuitively, developers using AI to write code often don’t trust AI to automatically remediate code without human review. This takes one of the best ways to keep up with machine-speed created vulnerabilities and slows it down to human speed. An acceptable balance between risk and speed must be found.

Approval is not governance

CISOs should focus on governance, not just approving AI coding tools. Governance means tracking where AI-generated code enters your environment, documenting the policies and tests applied, recording what issues were found and fixed, and keeping proof of these decisions. This documentation becomes critical as AI-assisted development becomes standard. If vulnerable code reaches production, you’ll need to show that adequate controls were in place and risks were managed according to policy.

What leaders should do now

CISOs and engineering leaders should treat AI-generated code as untrusted until proven otherwise. They must require automated testing before release, enforce dependency controls, prioritize remediation based on exploitability and business impact, and measure success by the rate at which critical risk is reduced.

Additionally, boards and organizational policymakers should ask whether organizations can demonstrate that AI-assisted software is governed before it is deployed. The key evidence should include the policies applied, the tests performed, the vulnerabilities remediated, the risks accepted, and the approvals recorded. Today, many organizations can confidently track what their AI tools produce, but they cannot demonstrate how that output was secured, reviewed, and governed before reaching production. The industry is still working to close this gap.

AI is changing how quickly software risk moves through the enterprise. The organizations that succeed will make security move just as quickly by embedding governance, remediation, and proof directly into the software delivery pipeline.

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